CN118914732B - A method for detecting faults of power equipment in a power distribution system - Google Patents
A method for detecting faults of power equipment in a power distribution system Download PDFInfo
- Publication number
- CN118914732B CN118914732B CN202411407883.8A CN202411407883A CN118914732B CN 118914732 B CN118914732 B CN 118914732B CN 202411407883 A CN202411407883 A CN 202411407883A CN 118914732 B CN118914732 B CN 118914732B
- Authority
- CN
- China
- Prior art keywords
- power
- area
- electric quantity
- degree
- time period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of electric quantity testing, in particular to a fault detection method for power equipment of a power transformation and distribution system. The method comprises the steps of obtaining electric quantity data, obtaining normal descending degree according to the change condition of the electric quantity data in a descending time period of an electric quantity descending region and the distribution condition of abnormal electric quantity data and the overall change trend of the electric quantity data of the electric quantity descending region, screening a first suspected fault region, obtaining the descending degree of a user according to the user change of the first suspected fault region, screening a second suspected fault region, obtaining electric quantity regulation degree according to the electric quantity demand difference and the electric quantity distribution difference between the second suspected fault region and each other region, screening a fault region, and detecting faults of electric equipment of the fault region. According to the invention, the fault area is accurately obtained, so that the area with the power equipment fault is accurately determined, the fault detection efficiency of the power equipment is improved, and a large amount of manpower and material resources are saved.
Description
Technical Field
The invention relates to the technical field of electric quantity testing, in particular to a fault detection method for power equipment of a power transformation and distribution system.
Background
In a power transformation and distribution system, power equipment is used for converting and distributing electric energy, and normal and stable operation of the power equipment is critical to the reliability and stability of power supply of the power transformation and distribution system. The most critical power equipment for power conversion and transmission in a power transformation and distribution system is called a power transformer. Therefore, the power transformer needs to be detected in time, faults of the power transformer are found in time and maintained, and stable and reliable operation of the power transformation and distribution system is ensured.
In the prior art, the electric quantity data of each area is directly analyzed by a server, and when the electric quantity data is reduced, the power transformer in the corresponding area is prompted to be suspected to be faulty, so that the power transformer in the corresponding area is timely subjected to fault detection and maintenance. However, in practical situations, the reasons for the decrease and change of the electric quantity data are many, for example, the situations of high energy consumption unit or active power outage maintenance of high energy consumption equipment, great use of self-contained power sources such as solar panels by users, reduction of electric quantity users in areas, electric quantity regulation and control among areas and the like all cause the decrease of the electric quantity data in the areas, so that the fault detection of the electric power equipment in different areas is inaccurate, and a great amount of manpower and material resources are wasted.
Disclosure of Invention
In order to solve the technical problem of inaccurate fault detection of power equipment in an area, the invention aims to provide a power equipment fault detection method of a power transformation and distribution system, and the adopted technical scheme is as follows:
The embodiment of the invention provides a power equipment fault detection method of a power transformation and distribution system, which comprises the following steps:
Acquiring electric quantity data of each area at each moment in the current time period;
Acquiring a descending time period of each electric quantity descending region at the current moment, and acquiring the normal descending degree of each electric quantity descending region according to the change condition of electric quantity data and the distribution condition of abnormal electric quantity data in the descending time period of each electric quantity descending region and the overall change trend of the electric quantity data of each electric quantity descending region in the current time period;
obtaining the user descending degree of each first suspected fault region according to the difference of the number of users between the descending time period and the current time period of other parts of the non-descending time period of each first suspected fault region;
acquiring the electric quantity regulation degree of each second suspected fault region according to the electric quantity demand difference and the electric quantity distribution difference between each second suspected fault region and each other region at the current moment;
and performing fault detection on the power equipment in the fault area.
Further, the method for obtaining the normal decline degree comprises the following steps:
For any electric quantity reduction region, fitting electric quantity data in the current time period of the electric quantity reduction region into a curve according to time sequence, and taking the curve as an electric quantity data curve of the electric quantity reduction region;
Decomposing the electric quantity data curve through an STL decomposition algorithm to obtain a trend curve and a season curve of the electric quantity reduction region;
acquiring the slope of a fitting straight line corresponding to the trend curve as the overall change value of the electric quantity reduction area;
taking a seasonal curve corresponding to the descending time period of the electric quantity descending region as a reference seasonal curve of the electric quantity descending region;
acquiring the seasonal decline degree of the electric quantity decline area according to the change condition of the reference seasonal curve;
Acquiring abnormal electric quantity data in a descending time period of the electric quantity descending region through a local outlier factor detection algorithm, and acquiring abnormal sporadic degree of the electric quantity descending region according to the distribution condition of the abnormal electric quantity data;
and obtaining the normal descending degree of the electric quantity descending region according to the integral change value, the seasonal descending degree and the abnormal sporadic degree of the electric quantity descending region, wherein the integral change value and the normal descending degree are in a negative correlation, and the seasonal descending degree and the abnormal sporadic degree are in a positive correlation with the normal descending degree.
Further, the method for acquiring the season decline degree comprises the following steps:
for any electric quantity reduction region, taking the accumulated result of the corresponding time periods of all reduction parts in the reference seasonal curve of the electric quantity reduction region as a first time period;
Taking the accumulated result of the corresponding time periods of all ascending parts in the reference seasonal curve of the electric quantity descending area as a second time period;
Acquiring the data descending rate of each descending part in the reference seasonal curve of the electric quantity descending area as the first rate of the corresponding descending part;
acquiring the data rising rate of each rising part in the reference seasonal curve of the electric quantity falling area as a second rate of the corresponding rising part;
And obtaining the seasonal decrease degree of the electric quantity decrease area according to the difference between the first time period and the second time period and the difference between the average value of the first rate and the average value of the second rate, wherein the difference between the first time period and the second time period and the difference between the average value of the first rate and the average value of the second rate are in positive correlation with the seasonal decrease degree.
Further, the method for obtaining the anomaly contingency degree comprises the following steps:
For any electric quantity reduction region, clustering abnormal electric quantity data in the reduction time period of the electric quantity reduction region by using a DBSCAN density clustering algorithm to obtain a cluster;
acquiring the quantity of abnormal electric quantity data in each cluster as a first quantity;
Taking the difference between the maximum first quantity and the mean value of the first quantity as a first characteristic difference;
Acquiring the quantity of all abnormal electric quantity data in the descending time period of the electric quantity descending area as a second quantity;
And acquiring the abnormal sporadic degree of the electric quantity reduction region according to the difference between the number of the clusters and the second number and the first characteristic difference, wherein the difference between the number of the clusters and the second number and the abnormal sporadic degree are positive correlation, and the first characteristic difference and the abnormal sporadic degree are negative correlation.
Further, the method for acquiring the first suspected fault area comprises the following steps:
and when the normal descending degree does not meet the preset normal descending degree range, the corresponding electric quantity descending region is a first suspected fault region.
Further, the method for acquiring the user descent degree comprises the following steps:
For any first suspected fault area, acquiring the number of users at each moment in a descending time period of the first suspected fault area as the first number of users;
Acquiring the number of users at each moment in the current time period of other parts of the non-descent time period of the first suspected fault region as a second number of users;
And obtaining the user descending degree of the first suspected fault area according to the difference between the average value of the second user number and the average value of the first user number, wherein the difference between the average value of the second user number and the average value of the first user number and the user descending degree are in positive correlation.
Further, the method for acquiring the second suspected fault area comprises the following steps:
And when the user descending degree does not meet the preset user descending degree range, the corresponding first suspected fault area is a second suspected fault area.
Further, the method for obtaining the electric quantity regulation degree comprises the following steps:
Acquiring electric quantity demand data and electric quantity distribution data of each area at the current moment;
For any second suspected fault area, acquiring the average value of the electric quantity demand data and the average value of the electric quantity distribution data of all other areas except the second suspected fault area, and sequentially taking the average value and the average value as reference electric quantity demand data and reference electric quantity distribution data;
Taking the difference between the reference electric quantity demand data and the electric quantity demand data of the second suspected fault area as a first difference;
Taking the difference between the reference electric quantity distribution data and the electric quantity distribution data of the second suspected fault area as a second difference;
And acquiring the electric quantity regulation degree of the second suspected fault area according to the first difference and the second difference, wherein the first difference and the second difference are in positive correlation with the electric quantity regulation degree.
Further, the fault region obtaining method comprises the following steps:
when the electric quantity regulation degree does not meet the preset electric quantity regulation degree range, the corresponding second suspected fault area is the fault area at the current moment.
Further, the method for acquiring the electric quantity reduction region and the reduction time period comprises the following steps:
taking a time period formed by the last preset number of continuous moments of each region in the current time period as a reference time period of each region;
For any region, when all electric quantity data in the reference time period of the region are smaller than a preset electric quantity data threshold value of the region, the region is an electric quantity reduction region at the current moment;
For any electric quantity reduction region, taking the moment corresponding to the electric quantity data which is more than or equal to the preset electric quantity data threshold value of the region and is closest to the current moment in the current time period of the electric quantity reduction region as the target moment;
the time period formed by the next time of the target time and the current time is taken as the falling time period of the electric quantity falling area.
The invention has the following beneficial effects:
In order to obtain the fault area, and then according to the change condition of electric quantity data and the distribution condition of abnormal electric quantity data in the descending time period of each electric quantity descending area and the overall change trend of the electric quantity data in the current time period of each electric quantity descending area, the normal descending degree of each electric quantity descending area is obtained, the possible degree of the electric quantity data of each electric quantity descending area which is normal at the current time is reflected, the normally descending electric quantity descending area at the current time is accurately removed, the suspected fault area is primarily identified, and then the first suspected fault area is screened out based on the normal descending degree, thereby being beneficial to accurately and efficiently identifying the fault area in the follow-up, and further according to the difference of the number of users between the descending time period of each first suspected fault area and the current time period of other parts of the non-descending time period, the normal descending degree of each first suspected fault area is accurately reflected, the possible degree of the electric quantity data of each electric quantity descending area which is normal at the current time is reflected, the suspected fault area is accurately removed, and the suspected fault area is further accurately identified based on the second suspected fault area which is more accurately obtained based on the difference between the second suspected fault area and the second fault area which is more accurately identified, the possibility that the electric quantity of each second suspected fault area is regulated and controlled is accurately reflected, the second suspected fault area with electric quantity data reduced due to electric quantity regulation factors at the current moment is accurately removed, the fault area at the current moment is accurately screened out based on the electric quantity regulation degree, further, fault detection is carried out on electric equipment in the fault area, faults of the electric equipment are accurately and efficiently detected, a large amount of manpower and material resources are saved, meanwhile, timely maintenance on the faults of the electric equipment is facilitated, and the reliability and stability of power supply of a power transformation and distribution system are ensured.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a fault detection method for power equipment of a power transformation and distribution system according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for obtaining a normal descent level according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for obtaining a season degradation level according to an embodiment of the present invention;
Fig. 4 is a block diagram of a fault detection system for power equipment of a power transformation and distribution system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a power equipment fault detection method of a power transformation and distribution system according to the invention, which is specific implementation, structure, characteristics and effects thereof, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a power equipment fault detection method of a power transformation and distribution system, which is specifically described below with reference to the accompanying drawings.
Example 1:
The embodiment has the specific scene that the power equipment which is most critical in electric energy conversion and transmission in the power transformation and distribution system belongs to a power transformer. The safe operation of a power transformation and distribution system is a first defense line for avoiding grid accidents, and a power transformer is one of the most critical power equipment in the defense line. In order to timely and accurately detect faults of the power transformers in each area, the embodiment firstly obtains the power reduction area in which the faults of the power transformers are likely to exist according to the change condition of the power data in each area in the current time period, then analyzes the change condition of the power data in the power reduction time period of each power reduction area, eliminates the power reduction area in which the current power data normally falls, screens out the first suspected fault area in which the faults of the power transformers are likely to exist, further analyzes the change of the quantity of power users in each first suspected fault area, eliminates the first suspected fault area in which the power data fall due to the reduction of the power users, further screens out the second suspected fault area in which the faults of the power transformers are likely to exist, further analyzes the power regulation conditions of each second suspected fault area and other areas in the current time period, determines the second suspected fault area in which the power data fall due to the power regulation factors in the current time period, and finally detects and maintains the power transformers in the fault area, so that the fault detection efficiency of the power transformers is improved, and the manpower and material resources are greatly saved.
The invention provides a fault detection method for power equipment of a power transformation and distribution system, referring to fig. 1, which shows a schematic flow chart of the fault detection method for the power equipment of the power transformation and distribution system, according to one embodiment of the invention, the method comprises the following steps:
and S1, acquiring electric quantity data of each area at each moment in the current time period.
Specifically, each user in each area controlled by a power transformation and distribution system corresponds to an intelligent ammeter, and the electric quantity data of each user in each area at each moment can be obtained through the intelligent ammeter. In an actual situation, each intelligent ammeter stores the electric quantity data in a specified time period and transmits the electric quantity data to a server in the power transformation and distribution system, and the embodiment timely detects faults of the power transformer in each area by acquiring and analyzing the electric quantity data in the server in real time. The present embodiment sets the specified period of time to 1 hour, and the practitioner can set the duration of the specified period of time according to the actual situation, which is not limited herein. In order to improve the efficiency of analyzing the change of the electric quantity data of each area, the embodiment adds up the electric quantity data of all users in each area at the same time as the electric quantity data of the corresponding area at the same time, so as to obtain the electric quantity data of each area at each time. It should be noted that, the areas appearing in this embodiment are all areas controlled by the same power transformation and distribution system.
In order to analyze whether the power transformer in each area has a fault in real time, the embodiment acquires the electric quantity data of each area at each moment in the current time period in the server of the power transformation and distribution system, analyzes the electric quantity data of each area in the current time period, and further accurately analyzes whether the power transformer in each area has a fault at the current time. In this embodiment, the current time period is set to 3 years, the terminal time of the current time period is necessarily the current time, the time interval between two adjacent times in the current time period is set to 24 hours, and the size of the time interval between the current time period and two adjacent times can be set by an implementer according to the actual situation, which is not limited herein.
Step S2, acquiring a descending time period of each electric quantity descending region at the current moment, acquiring the normal descending degree of each electric quantity descending region according to the change condition of electric quantity data and the distribution condition of abnormal electric quantity data in the descending time period of each electric quantity descending region and the overall change trend of the electric quantity data of each electric quantity descending region in the current time period, and screening out a first suspected fault region based on the normal descending degree.
As known, when a power transformer in a certain area fails, the power data in the area must be reduced, and the reduction of the power data must be a continuous process, so this embodiment uses a time period formed by the last preset number of consecutive moments of each area in the current time period as a reference time period of each area, where the terminal moment of the reference time period is the current moment, the preset number is set to 10, and an implementer can set the magnitude of the preset number according to the actual situation, which is not limited herein. When the electric quantity data in the reference time period of a certain area is smaller, the area is more likely to be in a descending stage in the reference time period, and the area is indirectly indicated to be the area with the possibility of the power transformer fault. Therefore, the preset power data threshold of each area is set in this embodiment, and for any area, when all the power data in the reference time period of the area is smaller than the preset power data threshold of the area, the area is the power-down area where the power transformer may fail at the current time. So far, each electric quantity reduction area at the current moment is acquired.
Preferably, in some possible implementation manners of the embodiment, the method for acquiring the preset electric quantity data threshold includes, for any area, acquiring the average value of all electric quantity data of the area in the current time period as a reference value of the area, and taking the product of the first preset constant and the reference value as the preset electric quantity data threshold of the area, where the first preset constant is greater than 0 and less than 1, the first preset constant is set to 0.8, and an implementer can set the magnitude of the first preset constant according to actual conditions, and the implementation is not limited. So far, the preset electric quantity data threshold value of each area is obtained.
In order to accurately analyze the descending condition of the electric quantity data of each electric quantity descending region at the current time, the embodiment obtains the descending time period of each electric quantity descending region at the current time according to the changing condition of the electric quantity data of each electric quantity descending region. In some possible implementation manners of the embodiment, the method for acquiring the falling time period includes taking, as a target time, a time corresponding to electric quantity data which is more than or equal to a preset electric quantity data threshold value of the area and is closest to the current time in a current time period of any electric quantity falling area, and taking a time period formed by a next time of the target time and the current time as the falling time period of the electric quantity falling area. The electric quantity data in the descending time period of each electric quantity descending area are smaller than the preset electric quantity data threshold value of the corresponding electric quantity descending area. Up to this point, the falling period of each power-down region is acquired.
In practical situations, the electric quantity data of each area is reduced due to the influence of seasonal variation or other normal factors, for example, the electric quantity data of each area is relatively hot in summer, users in each area almost use an air conditioner, the weather in spring is relatively suitable, and the air conditioner is not needed, so that the electric quantity data of each area in spring is reduced compared with the electric quantity data of each area in summer. In order to determine whether the electric quantity data in the falling time period of each electric quantity falling region is the normally changed electric quantity data, the embodiment first analyzes the change condition of the electric quantity data in the falling time period of each electric quantity falling region, and when the falling part of the electric quantity data in the falling time period of a certain electric quantity falling region is more than the rising part and the falling speed of the electric quantity data is faster, the more likely the electric quantity data in the falling time period of the electric quantity falling region belongs to the normally changed electric quantity data, the less likely the electric quantity falling region is the power transformer fault region.
Since the distribution comparison set of the abnormal electric quantity data generated by the faults of the power transformer is known, and the distribution of the abnormal electric quantity data generated by other accidental accidents such as electric quantity data entry errors is more discrete, the embodiment firstly obtains the abnormal electric quantity data in the falling time period of each electric quantity falling area through the local outlier factor detection algorithm, further analyzes the distribution situation of the abnormal electric quantity data in the falling time period of each electric quantity falling area, and when the distribution of the abnormal electric quantity data in the falling time period of a certain electric quantity falling area is more discrete, the electric quantity falling area is more unlikely to be the fault area of the power transformer. The local outlier factor detection algorithm is a known technology, and will not be described in detail.
In practical situations, when the overall change trend of the electric quantity data of a certain electric quantity reduction area is downward, the electric quantity data of the electric quantity reduction area at the current moment is more likely to be the normally changed electric quantity data, and the electric quantity reduction area is less likely to be the power transformer fault area.
Therefore, in this embodiment, the normal descending degree of each electric quantity descending area is obtained according to the change condition of the electric quantity data and the distribution condition of the abnormal electric quantity data in the descending time period of each electric quantity descending area, and the overall change trend of the electric quantity data in the current time period of each electric quantity descending area. The greater the normal drop degree, the more normal the change of the electric quantity data in the drop period of the corresponding electric quantity drop region, the less likely the corresponding electric quantity drop region is a power transformer fault region. And then the first suspected fault area which possibly has the power transformer fault is primarily screened out according to the normal descending degree, and the detection efficiency of the fault area is improved.
In order to accurately analyze the change and overall change trend of the electric quantity data of each electric quantity reduction region, the embodiment respectively fits the electric quantity data of the current time period of each electric quantity reduction region into a curve according to time sequence to be used as the electric quantity data curve of each electric quantity reduction region, and decomposes each electric quantity data curve through an STL (STANDARD TEMPLATE Library) decomposition algorithm to obtain the trend curve and the season curve of each electric quantity reduction region. And analyzing the overall change trend of the electric quantity data of each electric quantity reduction region through a trend curve, and analyzing the change condition of the electric quantity data of each electric quantity reduction region through a seasonal curve. The curve fitting method and the STL (STANDARD TEMPLATE Library) decomposition algorithm are both known techniques, and will not be described in detail.
In some possible implementations of the present embodiment, referring to fig. 2, a flowchart of a method for obtaining a normal descent level according to an embodiment of the present invention is shown, where the method includes the following steps:
step S201, obtaining the whole change value.
Since the trend curve of each electric quantity reduction region can reflect the overall change condition of the electric quantity data of each electric quantity reduction region, in this embodiment, the trend curve of each electric quantity reduction region is fitted into a straight line, the slope of each straight line is obtained as the overall change value of each electric quantity reduction region, the smaller the overall change value is, the overall change trend of the electric quantity data of the corresponding electric quantity reduction region is indicated to be a reduction trend, and the more likely that the electric quantity data in the reduction time period of the corresponding electric quantity reduction region is the normally changed electric quantity data is indirectly indicated, and the less likely that the corresponding electric quantity reduction region is the power transformer fault region is.
And S202, acquiring the season decline degree.
In order to further analyze whether the electric quantity data in the descending time period of each electric quantity descending region is the electric quantity data which normally descends, the embodiment uses a part of seasonal curves corresponding to the descending time period of each electric quantity descending region as the reference seasonal curves of each electric quantity descending region, the change condition of the electric quantity data in the descending time period of each electric quantity descending region can be accurately analyzed through the reference seasonal curves, when the reference seasonal curves of a certain electric quantity descending region have more time periods in which the data descends, and when the data descending speed comparison block is used, the more likely electric quantity data in the descending time period of the electric quantity descending region are the electric quantity data which normally descend. Further, in this embodiment, the seasonal decrease degree of each electric quantity decrease area is obtained according to the change condition of the reference seasonal curve of each electric quantity decrease area. The greater the season drop degree, the more likely the electric quantity data in the drop time period of the corresponding electric quantity drop region is the electric quantity data which is normally dropped, and the more unlikely the corresponding electric quantity drop region is the power transformer fault region is indirectly indicated.
In some possible implementations of the present embodiment, referring to fig. 3, a flowchart of a method for obtaining a seasonal decline degree according to an embodiment of the present invention is shown, where the method includes the following steps:
Step S202-1, for any power-down area, the accumulated result of the corresponding time periods of all the down parts in the reference season curve of the power-down area is taken as a first time period.
The larger the first period is, the more the normal change of the electric quantity data tends to be reduced in the falling period of the electric quantity falling area, which means that the more likely the electric quantity data in the falling period of the electric quantity falling area is the normally changed electric quantity data, the less likely the electric quantity falling area is the power transformer fault area.
Step S202-2, the accumulated result of the corresponding time periods of all ascending parts in the reference season curve of the electric quantity descending region is used as a second time period.
The smaller the second period, the more the change of the power data in the falling period of the power-down area tends to be reduced, which means that the power data in the falling period of the power-down area is more likely to be the power data which is normally reduced.
Step S202-3, acquiring the data descending rate of each descending part in the reference season curve of the electric quantity descending region as the first rate of the corresponding descending part.
The greater the first rate, the more pronounced the data drop corresponding to the drop portion. The first rate obtaining method comprises the steps of obtaining a difference value between a maximum value and a minimum value of any descending part of a reference seasonal curve to be used as a first value, and taking a ratio of the first value to a time period duration corresponding to the descending part as the first rate of the descending part.
Step S202-4, the data rising rate of each rising part in the reference season curve of the electric quantity falling area is obtained as a second rate of the corresponding rising part.
The larger the second rate, the more pronounced the data rise corresponding to the rising portion. The second rate obtaining method is the same as the first rate obtaining method, and will not be described in detail.
And S202-5, acquiring the seasonal decrease degree of the electric quantity decrease area according to the difference between the first time period and the second time period and the difference between the average value of the first rate and the average value of the second rate, wherein the difference between the first time period and the second time period and the difference between the average value of the first rate and the average value of the second rate are in positive correlation with the seasonal decrease degree.
When the difference between the first time period and the second time period is larger, the electric quantity data in the falling time period of the electric quantity falling area tends to change downwards, which means that the electric quantity data in the falling time period of the electric quantity falling area is more likely to change downwards normally. In order to further determine whether the electric quantity data in the falling time period of the electric quantity falling area is the electric quantity data which normally falls, the obvious change condition of the electric quantity data in the falling time period of the electric quantity falling area is further analyzed, when the first speed is larger than the second speed, the more obvious the falling change of the electric quantity data in the falling time period of the electric quantity falling area is indicated, and the more probable the falling time period of the electric quantity falling area is the time period which normally changes is indirectly indicated. And obtaining the seasonal decrease degree of the electric quantity decrease area according to the difference value between the first time period and the second time period and the difference value between the average value of the first rate and the average value of the second rate, wherein the difference value between the first time period and the second time period and the difference value between the average value of the first rate and the average value of the second rate are in positive correlation with the seasonal decrease degree.
In order to accurately represent the normal degree of the electric quantity data in the descending time period of the electric quantity descending region, the embodiment specifically quantifies the seasonal descending degree into a seasonal descending degree value, and the larger the seasonal descending degree value is, the more likely the electric quantity data in the descending time period of the electric quantity descending region is to be the electric quantity data of the normal descending, and the more unlikely the electric quantity descending region is to be the power transformer fault region. The calculation formula of the season decline degree value is as follows: In the formula (I), in the formula (II), A season drop degree value of the a-th electric quantity drop area; A first period of time that is the a-th power-down region; a second period of time which is the a-th power-down region; A mean value of the first rate of the a-th power reduction region; is the average of the second rate for the a-th power down region. The larger andThe larger, i.eThe larger the electricity quantity data in the falling time period of the a-th electricity quantity falling area is, the more likely the electricity quantity data is to be the normally falling electricity quantity data, and the less likely the a-th electricity quantity falling area is to be the power failure area. And obtaining the season drop degree value of each electric quantity drop area.
And step S203, obtaining the anomaly contingency degree.
It is known that when the distribution of abnormal power data in a falling period of a certain power-down region is more discrete, the abnormal power data in the falling period of the power-down region is less likely to be generated by a power transformer fault, and the power-down region is more unlikely to be a power transformer fault region. Therefore, the embodiment obtains the abnormal sporadic degree of each electric quantity reduction region according to the distribution condition of the abnormal electric quantity data in the reduction time period of each electric quantity reduction region, and the larger the abnormal sporadic degree is, the less likely the corresponding electric quantity reduction region is a power transformer fault region.
Preferably, in some possible implementation manners of the embodiment, the method for acquiring the abnormal contingency degree is that, for any electric quantity reduction region, abnormal electric quantity data in a reduction time period of the electric quantity reduction region is clustered through a DBSCAN density clustering algorithm to obtain clusters, and when the clusters are scattered and the abnormal electric quantity data in each cluster are smaller, the abnormal electric quantity data in the reduction time period of the electric quantity reduction region is more likely to be caused by accidental accidents, the abnormal electric quantity data in the reduction time period of the electric quantity reduction region is less likely to be caused by power transformer faults, and the electric quantity reduction region is less likely to be caused by power transformer faults. The DBSCAN density clustering algorithm is a well-known technique, and will not be described in detail. In order to accurately analyze the distribution situation of the abnormal electric quantity data in the descending time period of the electric quantity descending region, the quantity of the abnormal electric quantity data in each cluster is obtained to be used as a first quantity, the difference value of the mean value of the largest first quantity and the first quantity is used as a first characteristic difference, the first characteristic difference is a certain nonnegative quantity, the smaller the first characteristic difference is, the more consistent the distribution of the abnormal electric quantity data in the cluster is indicated, the quantity of the abnormal electric quantity data in the descending time period of the electric quantity descending region is further obtained to be used as a second quantity, and the more discrete the distribution of the abnormal electric quantity data in the descending time period of the electric quantity descending region is indicated on the premise that the smaller the first characteristic difference is when the quantity of the cluster is equal to the second quantity. The abnormal sporadic degree of the electric quantity reduction area is obtained according to the difference between the number of the clusters and the second number and the first characteristic difference, wherein the difference between the number of the clusters and the second number and the abnormal sporadic degree are positive correlation, and the first characteristic difference and the abnormal sporadic degree are negative correlation.
In order to accurately represent the degree that abnormal electric quantity data in the falling time period of the electric quantity falling area is generated by accidental conditions, in this embodiment, the abnormal accidental degree is specifically quantified to be an abnormal accidental degree value, and the greater the abnormal accidental degree value is, the more likely the abnormal electric quantity data in the falling time period of the electric quantity falling area is caused by accidental conditions, and the more unlikely the electric quantity falling area is a power transformer fault area is indirectly described. The calculation formula of the abnormal sporadic degree value is as follows: In the formula (I), in the formula (II), The abnormal sporadic degree value of the a-th electric quantity reduction area; the number of clusters in the falling period of the a-th electric quantity falling area; a second number of power down regions a; a first number that is the greatest during the fall time period of the a-th power down region; the average value of the first quantity in the falling time period of the a-th electric quantity falling area is exp which is an exponential function based on a natural constant; Is the first characteristic difference. The larger the size of the container,The smaller the abnormal power data distribution in the falling period of the a-th power-down area, the more discrete the abnormal power data distribution in the falling period of the a-th power-down area, the more likely the abnormal power data in the falling period of the a-th power-down area is caused by accidental accidents,The larger the a-th power reduction zone is, the less likely the power transformer fault zone is. And obtaining the abnormal sporadic degree value of each electric quantity reduction area.
Step S204, obtaining the normal descent degree.
The smaller the overall change value is, the overall change trend of the electric quantity data of the corresponding electric quantity decreasing area is indicated to belong to decrease, the more likely to be the electric quantity data of normal change in the decreasing time period of the corresponding electric quantity decreasing area is indirectly indicated, the less likely to be the electric quantity data of the power transformer fault area is indicated to be the corresponding electric quantity decreasing area, the greater the season decreasing degree is, the less likely to be the electric quantity data of normal decrease in the decreasing time period of the corresponding electric quantity decreasing area is indicated to be the electric quantity data of normal decrease, the greater the abnormality contingency degree is, the more likely to be the accidental accident in the decreasing time period of the corresponding electric quantity decreasing area is indicated to be the electric quantity data of accidental occurrence, and the less likely to be the electric quantity transformer fault area is indicated to be the corresponding electric quantity decreasing area. Therefore, the embodiment obtains the normal descending degree of each electric quantity descending region according to the integral change value, the seasonal descending degree and the abnormal sporadic degree of each electric quantity descending region, wherein the integral change value and the normal descending degree are in a negative correlation, and the seasonal descending degree and the abnormal sporadic degree are in a positive correlation with the normal descending degree. The greater the normal drop degree, the more likely the power data in the drop period corresponding to the power drop region is the power data of the normal drop, and the less likely the corresponding power drop region is the power transformer fault region.
In order to accurately represent the normal degree of the electric quantity data in the descending time period of each electric quantity descending region, the embodiment specifically quantifies the normal descending degree into a normal descending degree value, and the larger the normal descending degree value is, the more likely the electric quantity data in the descending time period of the corresponding electric quantity descending region is the electric quantity data of the normal descending. Wherein, the calculation formula of the normal decline degree value is: In the formula (I), in the formula (II), The normal drop degree value of the a-th electric quantity drop area; a season drop degree value of the a-th electric quantity drop area; the abnormal sporadic degree value of the a-th electric quantity reduction area; the overall change value of the a-th electric quantity reduction area; is a second preset constant, greater than 0, and norm is a normalization function. Wherein, the value range of the normal decline degree value is 0 to 1.
The present embodiment will beSetting 1, avoiding denominator as 0, and setting by the practitioner according to actual conditionsIs not limited herein. And obtaining the normal descending degree value of each electric quantity descending area.
Since it is known that the larger the normal drop level value is, the less likely the corresponding power drop area is a power transformer fault area, and thus, the preset normal drop level threshold value is set to 0.8 according to the embodiment, and the operator can set the magnitude of the preset normal drop level threshold value according to the actual situation, which is not limited herein. When the normal descent level value is smaller than or equal to the preset normal descent level value threshold, the electric quantity data in the descent time period of the corresponding electric quantity descent area is the electric quantity data of normal change, the corresponding electric quantity descent area is not necessarily the power transformer fault area, and when the normal descent level value is smaller than or equal to the preset normal descent level value threshold, the corresponding electric quantity descent area is possibly the power transformer fault area, so that the electric quantity descent area corresponding to the normal descent level value smaller than or equal to the preset normal descent level value threshold is used as the first suspected fault area. And continuing to analyze the first suspected fault area, so that the efficiency of acquiring the fault area is improved.
And step S3, obtaining the user descending degree of each first suspected fault area according to the difference of the number of users between the descending time period of each first suspected fault area and the current time period of other parts of the non-descending time period, and screening out a second suspected fault area based on the user descending degree.
Specifically, in an actual situation, when a user in a certain area decreases, the electric quantity data of the area also decreases, so that in this embodiment, the change situation of the number of users in each first suspected fault area is analyzed, and an area where a power transformer fault actually exists at the current moment is further obtained. The user quantity at each moment in each area can be directly obtained in the power transformation and distribution system. When the user in the descending time period of a certain first suspected fault area is less than the user in the current time period of other parts of the non-descending time period, the more likely that the electric quantity data in the descending time period of the first suspected fault area is the normally-changed electric quantity data is indicated, and then the user descending degree of each first suspected fault area is obtained according to the user quantity difference between the descending time period of each first suspected fault area and the current time period of other parts of the non-descending time period, and the greater the user descending degree is, the more unlikely that the corresponding first suspected fault area is the power transformer fault area.
Preferably, in some possible implementation manners of the embodiment, the method for acquiring the user descending degree includes, for any one first suspected fault area, acquiring the number of users at each moment in a descending time period of the first suspected fault area as a first user number, acquiring the number of users at each moment in other part of the descending time period of the first suspected fault area, which are all second user numbers, as a second user number, for representing the number of users in the descending time period of the first suspected fault area and the number of users in the other part of the descending time period of the non-descending time period, acquiring a mean value of the first user number to represent the number of users in the descending time period of the first suspected fault area, and acquiring a mean value of the second user number to represent the number of users in the other part of the descending time period of the first suspected fault area, wherein the difference value of the mean value of the second user number and the mean value of the first user number is positive correlation degree.
In order to accurately represent the descending condition of the user in the descending time period of each first suspected fault area, in this embodiment, the descending degree of the user is specifically quantized into a descending degree value of the user, and the larger the descending degree value of the user is, the less likely the corresponding first suspected fault area is a fault area of the power transformer. The calculation formula of the user descent degree value is as follows: In the formula (I), in the formula (II), A user descent level value for the b first suspected fault region; a mean value of all second user numbers in the b first suspected fault area; The average value of the number of all first users in the b first suspected fault area is defined as a normalization function. Wherein, the value range of the user decline degree value is 0 to 1. So far, the user descent degree value of each first suspected fault area is obtained.
The greater the user degradation value is known, the less likely the corresponding first suspected fault region is a power transformer fault region. Therefore, in this embodiment, the preset user drop level threshold is set to 0.8, and the operator can set the magnitude of the preset user drop level threshold according to the actual situation, which is not limited herein. When the user descending degree value is larger than a preset user descending degree value threshold, the corresponding first suspected fault area is not necessarily the power transformer fault area, when the user descending degree value is smaller than or equal to the preset user descending degree value threshold, the corresponding first suspected fault area is possibly the power transformer fault area, and therefore the first suspected fault area corresponding to the user descending degree value smaller than or equal to the preset user descending degree value threshold is used as the second suspected fault area. And then, continuously analyzing the second suspected fault area, and improving the efficiency of acquiring the real power transformer fault area.
And S4, acquiring the electric quantity regulation degree of each second suspected fault area according to the electric quantity demand difference and the electric quantity distribution difference between each second suspected fault area and each other area at the current moment, and screening out the fault area at the current moment based on the electric quantity regulation degree.
Specifically, in the actual situation, some areas temporarily need more electricity consumption, so that in order to meet the demands of the areas, the power transformation and distribution system needs to regulate and control the electricity consumption of other areas, which can lead to the situation that the electricity consumption data of other areas is reduced. In order to determine whether a drop time period occurs in each second suspected fault area due to electric quantity regulation at the current time, in this embodiment, first, electric quantity demand data and electric quantity distribution data of each area at the current time are directly obtained through a power transformation and distribution system, when both the electric quantity demand data and the electric quantity distribution data of a certain second suspected fault area are smaller, the more likely that the electric quantity data in the drop time period of the second suspected fault area is generated due to electric quantity regulation between the areas, and the less likely that the second suspected fault area is a fault area of a power transformer. Therefore, according to the power demand difference and the power distribution difference between each second suspected fault area and each other area at the current time, the power regulation degree of each second suspected fault area is obtained, and the larger the power regulation degree is, the less likely the corresponding second suspected fault area is to be the power transformer fault area. And then accurately screening out a fault area at the current moment based on the electric quantity regulation degree.
Preferably, in some possible implementation manners of the embodiment, the electric quantity regulation and control degree obtaining method includes, for any one second suspected fault area, obtaining an average value of electric quantity demand data and an average value of electric quantity distribution data of all other areas except the second suspected fault area, sequentially using the average value and the average value of the electric quantity demand data as reference electric quantity demand data and reference electric quantity distribution data, using a difference value between the reference electric quantity demand data and the electric quantity demand data of the second suspected fault area as a first difference, using a difference value between the reference electric quantity distribution data and the electric quantity distribution data of the second suspected fault area as a second difference, using a difference value between the first difference and the second difference as a positive correlation, and using a difference value between the first difference and the second difference as a positive correlation, wherein the possibility that the second suspected fault area is regulated and controlled by a variable power distribution system at the current moment is greater. In order to accurately represent the possibility that each second suspected fault region is regulated by the power transformation and distribution system, in this embodiment, the electric quantity regulation degree is specifically quantized into an electric quantity regulation degree value, and the larger the electric quantity regulation degree value is, the more likely the electric quantity data corresponding to the falling time period of the second suspected fault region is generated by electric quantity regulation between regions, and the less likely the corresponding second suspected fault region is a fault region of the power transformer. The calculation formula of the electric quantity regulation degree value is as follows: In the formula (I), in the formula (II), The electric quantity regulation degree value of the d second suspected fault area is the electric quantity regulation degree value of the d second suspected fault area; is the reference electricity demand data; The electric quantity demand data of the d second suspected fault area is obtained; distributing data for the reference electric quantity; Distributing data for the electric quantity of the d second suspected fault area; is the first difference; the first difference and norm is the normalization function. Wherein, the value range of the electric quantity regulation degree value is 0 to 1. So far, the electric quantity regulation degree value of each second suspected fault area is obtained.
The greater the known electric quantity regulation degree value is, the less likely the corresponding second suspected fault region is to be the power transformer fault region, so the preset electric quantity regulation degree value threshold value is set to be 0.8 in this embodiment, and the magnitude of the preset electric quantity regulation degree value threshold value can be set by an implementer according to the actual situation, which is not limited herein. When the electric quantity regulation degree value is smaller than or equal to the preset electric quantity regulation degree value threshold, the corresponding second suspected fault area is a power transformer fault area, namely a fault area, so that the area where the power transformer fault exists is accurately determined.
And S5, performing fault detection on the power equipment in the fault area.
The power transformer in the fault area at the current moment is directly subjected to fault detection through a monitoring system or field investigation, so that the efficiency of power transformer fault detection is improved, the fault of the power transformer can be found in time, and a large amount of manpower and material resources are saved. Meanwhile, stable and reliable operation of the power transformation and distribution system is ensured in time.
In summary, the embodiment acquires electric quantity data, acquires normal descending degree according to the change condition of the electric quantity data and the distribution condition of abnormal electric quantity data in the descending time period of the electric quantity descending region and the overall change trend of the electric quantity data of the electric quantity descending region, and then screens out a first suspected fault region, acquires user descending degree according to the user change of the first suspected fault region, and then screens out a second suspected fault region, acquires electric quantity regulation degree according to the electric quantity demand difference and the electric quantity distribution difference between the second suspected fault region and each other region, and then screens out fault regions, and performs fault detection on electric equipment of the fault region. According to the invention, the fault area is accurately obtained, so that the area with the power equipment fault is accurately determined, the fault detection efficiency of the power equipment is improved, and a large amount of manpower and material resources are saved.
Example 2:
The invention also provides a fault detection system for power equipment of a power transformation and distribution system, referring to fig. 4, which shows a structure diagram of the fault detection system for power equipment of the power transformation and distribution system, provided by an embodiment of the invention, the system comprises an acquisition module 10, a first suspected fault area acquisition module 20, a second suspected fault area acquisition module 30, a fault area acquisition module 40 and a processing module 50.
And the acquisition module 10 is used for acquiring the electric quantity data of each area at each moment in the current time period.
The first suspected fault area obtaining module 20 is configured to obtain a falling time period of each electric quantity falling area at the current time, obtain a normal falling degree of each electric quantity falling area according to a change condition of electric quantity data and a distribution condition of abnormal electric quantity data in the falling time period of each electric quantity falling area and an overall change trend of the electric quantity data of each electric quantity falling area in the current time period, and screen out the first suspected fault area based on the normal falling degree.
The second suspected fault region obtaining module 30 is configured to obtain a user descent degree of each first suspected fault region according to a difference in the number of users between the descent time period of each first suspected fault region and the current time period of other parts of the non-descent time period, and screen out the second suspected fault region based on the user descent degree.
The fault region obtaining module 40 is configured to obtain a power regulation degree of each second suspected fault region according to a power demand difference and a power distribution difference between each second suspected fault region and each other region at the current time, and screen the fault region at the current time based on the power regulation degree.
And the processing module 50 is used for performing fault detection on the power equipment in the fault area.
It should be noted that, in the system provided in the above embodiment, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to perform all or part of the functions described above. In addition, the fault detection system of the power transformation and distribution system and the fault detection method embodiment of the power transformation and distribution system provided by the above embodiments belong to the same conception, and detailed implementation processes of the fault detection system and the fault detection method embodiment of the power transformation and distribution system are detailed in the method embodiment, and are not described herein again.
Example 3:
The application also provides a power equipment fault detection device of the power transformation and distribution system, which comprises a memory and a processor, wherein executable program codes are stored in the memory, and the processor is used for calling and executing the executable program codes to execute the power equipment fault detection method of the power transformation and distribution system provided by the embodiment of the application. The device can be a chip, a component or a module, the chip can comprise a processor and a memory which are connected, wherein the memory is used for storing instructions, and when the processor calls and executes the instructions, the chip can be made to execute the fault detection method for the power equipment of the power transformation and distribution system.
In addition, referring to fig. 5, the embodiment of the present application further protects a computer device, where the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, where the processor 402 executes the computer program 403, so that the computer device can execute any one of the power transformation and distribution system power device fault detection methods described above.
Example 4:
The present embodiment also provides a computer readable storage medium, in which computer program code is stored, which when run on a computer, causes the computer to execute the above-mentioned related method steps to implement a power equipment fault detection method for a power transformation and distribution system provided in the above-mentioned embodiment.
Example 5:
the present embodiment also provides a computer program product, which when run on a computer, causes the computer to perform the above related steps, so as to implement a power equipment fault detection method of a power transformation and distribution system provided by the above embodiment.
The apparatus, the computer readable storage medium, the computer program product, or the chip provided in this embodiment are used to execute the corresponding method provided above, and therefore, the advantages achieved by the apparatus, the computer readable storage medium, the computer program product, or the chip can refer to the advantages of the corresponding method provided above, which are not described herein.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411407883.8A CN118914732B (en) | 2024-10-10 | 2024-10-10 | A method for detecting faults of power equipment in a power distribution system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411407883.8A CN118914732B (en) | 2024-10-10 | 2024-10-10 | A method for detecting faults of power equipment in a power distribution system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118914732A CN118914732A (en) | 2024-11-08 |
| CN118914732B true CN118914732B (en) | 2024-12-06 |
Family
ID=93296370
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411407883.8A Active CN118914732B (en) | 2024-10-10 | 2024-10-10 | A method for detecting faults of power equipment in a power distribution system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118914732B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110634080A (en) * | 2018-06-25 | 2019-12-31 | 中兴通讯股份有限公司 | Abnormal power consumption detection method, device, equipment and computer-readable storage medium |
| CN112307435A (en) * | 2020-10-30 | 2021-02-02 | 三峡大学 | A method for screening abnormal electricity consumption based on fuzzy clustering and trend judgment |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6075997B2 (en) * | 2012-08-27 | 2017-02-08 | 株式会社日立製作所 | Fault diagnosis method for solar power generation system |
| CN115774652B (en) * | 2023-02-13 | 2023-04-21 | 浪潮通用软件有限公司 | Cluster control equipment health monitoring method, equipment and medium based on clustering algorithm |
| CN116151129A (en) * | 2023-04-18 | 2023-05-23 | 石家庄科林电气股份有限公司 | Power dispatching system fault diagnosis method based on extreme learning machine |
| CN116861312A (en) * | 2023-07-06 | 2023-10-10 | 国网黑龙江省电力有限公司营销服务中心 | Residual error network-based fault diagnosis method for electric power metering equipment |
| CN116595426B (en) * | 2023-07-17 | 2023-09-26 | 济南大陆机电股份有限公司 | An intelligent collection and management system for industrial Internet of Things data |
| CN116879662B (en) * | 2023-09-06 | 2023-12-08 | 山东华尚电气有限公司 | Transformer fault detection method based on data analysis |
| CN117057517B (en) * | 2023-10-12 | 2024-01-12 | 国网吉林省电力有限公司长春供电公司 | Efficient power data processing method and system based on digital twins |
| CN118294756A (en) * | 2024-04-02 | 2024-07-05 | 黑河学院 | Intelligent basic data association method and system |
| CN118362820B (en) * | 2024-06-20 | 2024-08-23 | 国网甘肃省电力公司电力科学研究院 | A method and system for intelligent diagnosis of energy storage equipment fault status |
-
2024
- 2024-10-10 CN CN202411407883.8A patent/CN118914732B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110634080A (en) * | 2018-06-25 | 2019-12-31 | 中兴通讯股份有限公司 | Abnormal power consumption detection method, device, equipment and computer-readable storage medium |
| CN112307435A (en) * | 2020-10-30 | 2021-02-02 | 三峡大学 | A method for screening abnormal electricity consumption based on fuzzy clustering and trend judgment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118914732A (en) | 2024-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119134354B (en) | Intelligent dispatching system of district power supply electric network | |
| CN111812427A (en) | A method and system for evaluating the state of health of a power electronic device | |
| CN109507535B (en) | Method and device for predicting operation stage and operation life of transformer substation grounding grid | |
| CN117118063A (en) | New energy monitoring method and system based on cloud edge fusion | |
| CN116154875A (en) | Photovoltaic power station active power optimization distribution method based on TCN and error function | |
| CN118914732B (en) | A method for detecting faults of power equipment in a power distribution system | |
| CN112782495A (en) | String abnormity identification method for photovoltaic power station | |
| CN114281846B (en) | A new energy power generation prediction method based on machine learning | |
| CN113934967B (en) | Intelligent storage sample-checking feedback supervision system for coal samples | |
| CN113379005A (en) | Intelligent energy management system and method for power grid power equipment | |
| CN107909096A (en) | A kind of fault of converter early warning criterion implementation method based on two points of K mean clusters | |
| CN118214367B (en) | Intelligent monitoring method for photovoltaic diode state | |
| CN119994972A (en) | Dynamic optimization control system of energy storage cluster based on edge computing | |
| CN103617454A (en) | Wind power plant power forecast method according to numerical weather forecasts | |
| CN113850017A (en) | System-level fault analysis system and method based on power flow change map | |
| CN119651555A (en) | A control method for intelligent photovoltaic power generation system | |
| CN117878958A (en) | Night reactive compensation analysis method and system for water-light complementary photovoltaic power station | |
| CN115967126A (en) | Online analysis method for new energy cluster output outward-sending risk under natural disasters | |
| CN120450145B (en) | Energy-carbon management optimization method and system for source network charge storage and virtual power plant linkage | |
| CN114362148B (en) | Emergency control method and device for coping with transient uncertainty of new energy | |
| CN119029902B (en) | A method for identifying typical wind and solar power output scenarios considering comprehensive characteristics of sources and loads | |
| CN118861557B (en) | A 5G communication power supply health management method and device | |
| CN119645624B (en) | Performance management system and method based on artificial intelligent chip | |
| CN120104432B (en) | Intelligent operation and maintenance management method, system and storage medium based on data center | |
| CN119671548B (en) | Power grid data blood-margin management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |