CN116819259A - Intelligent partial discharge identification diagnosis method, system and storage medium - Google Patents
Intelligent partial discharge identification diagnosis method, system and storage medium Download PDFInfo
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Abstract
The application provides an intelligent partial discharge identification diagnosis method, an intelligent partial discharge identification diagnosis system and a storage medium. Comprising the following steps: collecting partial discharge signals of one or more monitored objects to generate current partial discharge monitoring signals, wherein the current partial discharge monitoring signals are functions; filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals; performing interference signal identification processing on the first partial discharge monitoring signal to generate a second partial discharge monitoring signal; and judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value. The intelligent partial discharge identification diagnosis method, the intelligent partial discharge identification diagnosis system and the storage medium can effectively monitor the partial discharge of the power equipment, and can perform corresponding denoising and interference elimination in the monitoring process, so that the monitoring result of the intelligent partial discharge identification diagnosis method, the intelligent partial discharge identification diagnosis system and the storage medium is more accurate. And it has the function of identification (fingerprint, iris, face) which enables the device to be controlled by a person of a specific authority.
Description
Technical Field
The application relates to a power monitoring technology, in particular to an intelligent partial discharge identification and diagnosis method, an intelligent partial discharge identification and diagnosis system and a storage medium.
Background
The partial discharge phenomenon mainly refers to high-voltage electric equipment, and the electric equipment insulates the discharge which occurs in a partial range under the action of a strong enough electric field. Such discharge is limited by only causing local short (bridging) of insulation between conductors without forming conductive channels. Each partial discharge has some influence on the insulation medium, the slight partial discharge has little influence on the insulation of the power equipment, and the insulation strength is slowly reduced; a strong partial discharge causes a rapid decrease in the insulation strength, which is an important factor in insulating the high-voltage power equipment. Therefore, to enhance monitoring of the operating power equipment, when the partial discharge exceeds a certain level, the equipment should be taken out of operation for maintenance or replacement.
Disclosure of Invention
The embodiment of the application provides an intelligent partial discharge identification diagnosis method, an intelligent partial discharge identification diagnosis system and a storage medium, which can effectively monitor partial discharge of power equipment, and can perform corresponding denoising and interference elimination in the monitoring process, so that the monitoring result of the intelligent partial discharge identification diagnosis method is more accurate.
In a first aspect of the embodiment of the present application, an intelligent partial discharge identification and diagnosis method is provided, including:
collecting partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function;
filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals;
performing interference signal identification processing on the first partial discharge monitoring signal to generate a second partial discharge monitoring signal;
and judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value.
Optionally, in one possible implementation manner of the first aspect, the intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition manner to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
Optionally, in a possible implementation manner of the first aspect, before the step of collecting the partial discharge signals of the one or more monitored objects to generate the current partial discharge monitoring signal, the method further includes:
acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face identification information;
comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, collecting partial discharge signals of one or more monitored objects.
Optionally, in a possible implementation manner of the first aspect, filtering and denoising the partial discharge monitoring signal to generate a first partial discharge monitoring signal includes:
presetting a high point value and a low point value, wherein the high point value is a forward value of a function, and the low point value is a reverse value of the function;
and filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
Optionally, in a possible implementation manner of the first aspect, performing an interference signal identification process on the first partial discharge monitoring signal, and generating a second partial discharge monitoring signal includes:
selecting a test base point;
testing a test base point, and judging whether a suspected interference signal exists at the test base point;
if the suspected interference signal exists at the test base point, judging that the suspected interference signal is any one of an interference signal and a partial discharge signal;
and if the suspected interference signal is judged to be the interference signal, filtering the interference signal mark and reminding.
In a second aspect of the embodiment of the present application, there is provided an intelligent partial discharge identification and diagnosis system, including:
the acquisition module is used for acquiring partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function;
the noise reduction processing module is used for filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals;
the interference processing module is used for carrying out interference signal identification processing on the first partial discharge monitoring signal and generating a second partial discharge monitoring signal;
and the judging module is used for judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value.
Optionally, in one possible implementation manner of the second aspect, the intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition manner to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
Optionally, in one possible implementation manner of the second aspect, the method further includes:
the system comprises an acquisition module, a recognition module and a control module, wherein the acquisition module is used for acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face recognition information;
and the comparison module is used for comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, the partial discharge signals of one or more monitored objects are acquired.
Optionally, in one possible implementation manner of the second aspect, the noise reduction processing module includes:
a preset unit, configured to preset a high point value and a low point value, where the high point value is a forward value of the function, and the low point is a reverse value of the function;
and the noise reduction processing unit is used for filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
In a third aspect of the embodiments of the present application, there is provided a readable storage medium having stored therein a computer program for implementing the method of the first aspect and the various possible designs of the first aspect when the computer program is executed by a processor.
The intelligent partial discharge identification diagnosis method, system and storage medium provided by the application can effectively monitor the partial discharge of the power equipment, can perform corresponding denoising and interference elimination in the monitoring process, and can identify the identity of a user in the monitoring process, so that the control and monitoring of the method and system have certain authority, and the monitoring result is more accurate, and meanwhile, the method and system provided by the application have a certain threshold for use.
Drawings
FIG. 1 is a flow chart of a first embodiment of a smart partial discharge identification diagnostic method;
FIG. 2 is a flow chart of a second embodiment of a smart partial discharge identification diagnostic method;
fig. 3 is a block diagram of a first embodiment of an intelligent partial discharge identification diagnostic system.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present application, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that in the present application, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in the present application, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the application is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
The application provides an intelligent partial discharge identification diagnosis method, as shown in a flow chart of FIG. 1, comprising the following steps:
step S10, collecting partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function. The application can monitor and collect partial discharge signals of one or more monitored objects in the power grid according to actual working conditions, such as power equipment such as a power distribution cabinet and the like.
And step S20, filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals. In the actual signal acquisition process, more noise signals can be generated, denoising is performed in a filtering mode and the like, and the partial discharge monitoring signals can be more accurate.
In step S20, further comprising:
step S201, presetting a high point value and a low point value, wherein the high point value is a forward value of the function, and the low point value is a reverse value of the function. The high point value and the low point value are respectively the limits of reasonable values of the partial discharge monitoring signals, and if the limit exceeds the limit, the partial discharge monitoring signals corresponding to the partial discharge are proved to be affirmed not to be generated. At this time, filtering and denoising are performed.
And S202, filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
And step S30, performing interference signal identification processing on the first partial discharge monitoring signal to generate a second partial discharge monitoring signal. The step S30 can perform the interference signal identification processing on the first partial discharge monitoring signal, and remove the interference signal, so as to avoid the influence of the interference signal on the accuracy of the diagnosis scheme of the present application.
In step S30, as shown in fig. 2, further including:
step S301, selecting a test base point. The test base points can be set randomly, or different power equipment can have different test base points.
Step S302, testing the test base point, and judging whether a suspected interference signal exists at the test base point. And testing the test base point to judge whether a suspected interference signal exists, wherein the suspected interference signal is a point larger than a preset value of the test base point.
Step 303, if it is determined that the suspected interference signal exists at the test base point, determining that the suspected interference signal is any one of an interference signal and a partial discharge signal. And judging the suspected interference signal as the interference signal if the suspected interference signal is completely uncorrelated with the current partial discharge monitoring signal, the first partial discharge monitoring signal and the second partial discharge monitoring signal.
And step S304, if the suspected interference signal is judged to be the interference signal, filtering the interference signal mark and reminding. At the moment, the interference signals are marked and reminded, so that the application is prevented from diagnosing the interference signals again.
And S40, judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing operation if the second partial discharge monitoring signal does not reach the threshold value. The intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition mode to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
The ultra-wideband signal high-speed acquisition and recording scheme is based on high-performance PCI EXPRESS and SRIO protocols, and a standardized, modularized, extensible and reconfigurable ultra-wideband signal high-speed continuous acquisition, recording and playback generation platform is realized. High-performance ADC, DAC and super-large capacity solid state FLASH and high-speed mass disk array are adopted for storage.
In one embodiment, before the step of collecting the partial discharge signals of the one or more monitored objects to generate the current partial discharge monitoring signal, the method further includes:
step S01, acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face identification information;
step S02, comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, collecting partial discharge signals of one or more monitored objects.
In a second aspect of the embodiment of the present application, there is provided an intelligent partial discharge identification and diagnosis system, as shown in fig. 3, including:
the acquisition module is used for acquiring partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function;
the noise reduction processing module is used for filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals;
the interference processing module is used for carrying out interference signal identification processing on the first partial discharge monitoring signal and generating a second partial discharge monitoring signal;
and the judging module is used for judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value.
Optionally, in one possible implementation manner of the second aspect, the intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition manner to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
Optionally, in one possible implementation manner of the second aspect, the method further includes:
the system comprises an acquisition module, a recognition module and a control module, wherein the acquisition module is used for acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face recognition information;
and the comparison module is used for comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, the partial discharge signals of one or more monitored objects are acquired.
Optionally, in one possible implementation manner of the second aspect, the noise reduction processing module includes:
a preset unit, configured to preset a high point value and a low point value, where the high point value is a forward value of the function, and the low point is a reverse value of the function;
and the noise reduction processing unit is used for filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
The application also provides an intelligent comprehensive operation health identification management method for the power equipment, which comprises the following steps:
environmental data of the power equipment is obtained, wherein the environmental data comprises any one or more of temperature information, humidity information and water level information. The environment data are collected through a sensor arranged at the power equipment, and the environment data comprise any one or more of a temperature sensor, a humidity sensor and a water level sensor.
And acquiring power data of the power equipment, wherein the power data comprises any one or more of grounding current information, leakage current information and partial discharge information. The power data are collected through a sensor arranged at the power equipment, and the power data comprise any one or more of a current sensor and a partial discharge sensor.
And respectively carrying out quantization processing on the environment data and the power data to respectively obtain a temperature quantization value of the temperature information, a humidity quantization value of the humidity information, a water level quantization value of the water level information, a grounding quantization value of the grounding current information, a leakage quantization value of the leakage current information and a local quantization value of the partial discharge information.
Pre-constructing a quantization model, wherein the function model comprises a temperature model, a humidity model, a water level model, a grounding current model, a leakage current model and a partial discharge model;
and respectively inputting the temperature information, the humidity information, the water level information, the grounding current information, the leakage current information and the partial discharge information into the corresponding function model to obtain a temperature quantized value, a humidity quantized value, a water level quantized value, a grounding quantized value, a leakage quantized value and a partial quantized value.
And obtaining a health management value based on the temperature quantized value, the humidity quantized value, the water level quantized value, the ground quantized value, the leakage quantized value and the local quantized value.
Pre-constructing a management model, and respectively configuring one or more weights for the temperature quantized value, the humidity quantized value, the water level quantized value, the grounding quantized value, the leakage quantized value and the local quantized value;
and summing the temperature quantized value, the humidity quantized value, the water level quantized value, the ground quantized value, the leakage quantized value and the local quantized value after the weight is configured, so as to obtain the health management value.
Through the steps, the environmental data and the electric power data of the electric power equipment can be monitored, in the monitoring process, various information is quantized, the running condition of the electric power equipment is timely determined according to the quantization results of the environmental data and the electric power data, the quantized value of the health state of the electric power equipment is obtained, and corresponding maintenance and replacement are further carried out. By quantifying various indexes of the power equipment, the use state and aging condition of the power equipment can be intuitively reflected, and monitoring staff can conveniently grasp the condition of the power equipment.
In one embodiment, the method further comprises the steps of:
and evaluating the service life of the power equipment based on the health management value to obtain a life evaluation value.
In one embodiment, the method further comprises the steps of:
presetting a decision library, wherein a plurality of decision information are stored in the decision library, and each decision information is used for solving the problem corresponding to one or more health management values;
and acquiring a health management value, matching the health management value with a plurality of pieces of decision information in a decision library based on a decision model, and determining an optimal decision information output.
Wherein, still include:
the power equipment is X i ,i＝(1, 2, …, n); power equipment X i Is marked as { a }, a i ,b i ,..,n i (wherein a) i Representing an electrical power device X i Temperature quantization value of temperature dimension of b) i Representing an electrical power device X i Humidity measurement value, n, of the humidity dimension of (2) i Representing an electrical power device X i Local quantization values of the local discharge dimensions of (a). The plurality of electric devices are included in plural, and each of the electric devices has information corresponding thereto, such as temperature information, humidity information, water level information, ground current information, leakage current information, partial discharge information, and the like.
The set of different decision schemes for a power device is denoted as { A } i ,B i ,…,N i }, wherein A i Representing decision scheme D i The solution set for the temperature dimension is { a } 1 ,a 2 ,…,a n }，B i Representing decision scheme D i The solution set for the humidity dimension is { b } 1 ,b 2 ,…,b n }、N i Representing decision scheme D i The solution set for the partial discharge dimension is { n } 1 ,n 2 ,…,n n }. The solution set includes a plurality of, for example, one solution 1 is: carrying out dampproof treatment on the power equipment; the solution 2 is as follows: maintaining or replacing a heat dissipation system of the power equipment; the solution 3 is as follows: respectively and simultaneously carrying out dampproof treatment and maintenance or replacement on the power equipment; the solution 4 is as follows: replacement of electrical equipment, and the like.
The method comprises the following steps:
the detected power device includes { X } 1 ,X 2 ,…,X i Solution { D }, solution 1 ,D 2 ,…,D i Respectively evaluating solutions to power equipment, if { a } i ,b i ,..,n i }∈{A i ,B i ,…,N i Determines the power equipment X 1 ,X 2 ,…,X i The evaluation sets at the respective evaluation models are as follows:
selecting and currently detecting power equipment X i Corresponding one or more decision schemes, respectively obtaining a plurality of sets of sum decision schemes, which are { P } 1 ,P 2 ,…,P n }, wherein P 2 ,P n May be an empty set. By the method, different decision schemes can be output to different power equipment, when equipment problems are serious, the decision schemes can be directly used for replacing equipment, and when the equipment problems are small, the equipment can be adjusted according to information and indexes which are not met. For example, if the temperature of the power equipment is only too high, it proves that the power equipment can not perform normal heat dissipation due to the aging of the heat dissipation system, so that the heat dissipation system can be maintained and replaced to achieve the purpose of normal heat dissipation. For example, when the power equipment has the problems of too high temperature, too high humidity, too high partial discharge and the like, the problems are more, repair is not easy to achieve or repair cost is too high, and the output decision scheme can be used for directly replacing the power equipment.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). In addition, the ASIC may reside in a user device. The processor and the readable storage medium may reside as discrete components in a communication device. The readable storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tape, floppy disk, optical data storage device, etc.
The present application also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, the execution instructions being executed by the at least one processor to cause the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the terminal or the server, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. An intelligent partial discharge identification and diagnosis method is characterized by comprising the following steps:
collecting partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function;
filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals;
performing interference signal identification processing on the first partial discharge monitoring signal to generate a second partial discharge monitoring signal;
and judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value.
2. The intelligent partial discharge identification diagnostic method according to claim 1, wherein,
the intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition mode to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
3. The intelligent partial discharge identification diagnostic method according to claim 1, wherein,
collecting partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein before the step of generating the current partial discharge monitoring signal as a function, the method further comprises the following steps:
acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face identification information;
comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, collecting partial discharge signals of one or more monitored objects.
4. The intelligent partial discharge identification diagnostic method according to claim 1, wherein,
the filtering and denoising processing of the partial discharge monitoring signal to generate a first partial discharge monitoring signal comprises the following steps:
presetting a high point value and a low point value, wherein the high point value is a forward value of a function, and the low point value is a reverse value of the function;
and filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
5. The intelligent partial discharge identification diagnostic method according to claim 1, wherein,
performing interference signal identification processing on the first partial discharge monitoring signal, and generating a second partial discharge monitoring signal includes:
selecting a test base point;
testing a test base point, and judging whether a suspected interference signal exists at the test base point;
if the suspected interference signal exists at the test base point, judging that the suspected interference signal is any one of an interference signal and a partial discharge signal;
and if the suspected interference signal is judged to be the interference signal, filtering the interference signal mark and reminding.
6. An intelligent partial discharge identification diagnostic system, comprising:
the acquisition module is used for acquiring partial discharge signals of one or more monitored objects to generate a current partial discharge monitoring signal, wherein the current partial discharge monitoring signal is a function;
the noise reduction processing module is used for filtering and denoising the partial discharge monitoring signals to generate first partial discharge monitoring signals;
the interference processing module is used for carrying out interference signal identification processing on the first partial discharge monitoring signal and generating a second partial discharge monitoring signal;
and the judging module is used for judging whether the second partial discharge monitoring signal reaches a threshold value, prompting if the second partial discharge monitoring signal reaches the threshold value, and not performing action if the second partial discharge monitoring signal does not reach the threshold value.
7. The intelligent partial discharge identification diagnostic system of claim 6, wherein,
the intelligent partial discharge identification and diagnosis method adopts an ultra-wideband acquisition mode to separate and classify the acquired partial discharge monitoring signals according to preset requirements.
8. The intelligent partial discharge identification diagnostic system of claim 6, wherein,
further comprises:
the system comprises an acquisition module, a recognition module and a control module, wherein the acquisition module is used for acquiring current identity information of a user, wherein the current identity information is any one or more of fingerprint information, iris information and face recognition information;
and the comparison module is used for comparing the current identity information with preset white list information, wherein the white list information comprises preset identity information, and if the preset identity information consistent with the current identity information exists, the partial discharge signals of one or more monitored objects are acquired.
9. The intelligent partial discharge identification diagnostic system of claim 6, wherein,
the noise reduction processing module includes:
a preset unit, configured to preset a high point value and a low point value, where the high point value is a forward value of the function, and the low point is a reverse value of the function;
and the noise reduction processing unit is used for filtering and denoising signals exceeding the high point value and/or being lower than the low point value in the function formed by the current partial discharge monitoring signals.
10. A readable storage medium, characterized in that the readable storage medium has stored therein a computer program for implementing the method of any of claims 1 to 5 when being executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202310899929.1A CN116819259A (en) | 2023-07-21 | 2023-07-21 | Intelligent partial discharge identification diagnosis method, system and storage medium |
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| CN202310899929.1A CN116819259A (en) | 2023-07-21 | 2023-07-21 | Intelligent partial discharge identification diagnosis method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118209830A (en) * | 2024-05-14 | 2024-06-18 | 山东博通节能科技有限公司 | A method and system for intelligent monitoring of cable partial discharge anomaly |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118209830A (en) * | 2024-05-14 | 2024-06-18 | 山东博通节能科技有限公司 | A method and system for intelligent monitoring of cable partial discharge anomaly |
| CN118209830B (en) * | 2024-05-14 | 2024-07-23 | 山东博通节能科技有限公司 | Intelligent monitoring method and system for cable partial discharge abnormality |
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