CN119202617B - A method and system for controlling electric energy meter operation - Google Patents
A method and system for controlling electric energy meter operation Download PDFInfo
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Abstract
The invention provides an electric energy meter operation control method and system, and relates to the technical field of intelligent power grids, wherein the method comprises the steps of receiving a monitoring instruction, wherein the monitoring instruction comprises monitoring all working physical data of an electric energy meter, and the working physical data comprise voltage, current, power factor, active power and reactive power; the method comprises the steps of generating a corresponding data acquisition plan according to a monitoring instruction, distributing the data acquisition plan to corresponding operators and operation terminals, acquiring working physical data of the electric energy meter monitored by the monitoring terminal in real time, wherein the working physical data comprises voltage fluctuation, current change and power factor stability, and the monitoring terminal automatically calculates the gradient of the working physical data change after completing one monitoring period. According to the intelligent management method, the intelligent management of the electric energy meter is realized by monitoring and analyzing the working physical data of the electric energy meter in real time and performing feedback control, and the stability and the efficiency of the electric power system are improved.
Description
Technical Field
The invention relates to the technical field of smart grids, in particular to an electric energy meter operation control method and system.
Background
The traditional mode adopts manual meter reading, the data acquisition and processing speed of the mode is slower, the requirement on real-time data cannot be met, and the control lag can be caused by low-efficiency data processing. Manual meter reading is easily affected by human factors such as transcription errors, omission or inaccurate data entry. Inaccurate data may lead to erroneous decisions and control commands for controlling the machine, thereby affecting the stability and efficiency of the overall system.
Therefore, if real-time monitoring data of the electric energy meter cannot be provided, this limits the ability to cope with emergency situations or to perform accurate regulation. In addition, if it is difficult to adapt to the changes of the scale and the demand of the electric power system, the expandability and the flexibility of the control machine are limited, and the dependence on manual meter reading not only increases the labor cost, but also is easy to cause human errors. This means that additional verification and error correction mechanisms are required for controlling the machine to ensure the reliability of the data, thereby increasing the complexity and operating costs of the system.
Disclosure of Invention
The invention aims to provide a method and a system for controlling operation of an electric energy meter, which realize efficient and accurate management of the electric energy meter.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a method for controlling operation of an electric energy meter, the method comprising:
receiving a monitoring instruction, wherein the monitoring instruction comprises monitoring all working physical data of the electric energy meter, and the working physical data comprise voltage, current, power factor, active power and reactive power;
the data acquisition plan is distributed to corresponding operators and operation terminals;
Acquiring working physical data of the electric energy meter monitored by the monitoring terminal in real time, wherein the working physical data comprises voltage fluctuation, current variation and power factor stability, and the monitoring terminal automatically calculates a gradient of the working physical data variation every time after one monitoring period is completed;
Carrying out data analysis according to the change gradient of the working physical data, including statistical distribution and trend analysis, and detecting whether abnormal values exist in the change gradient according to a preset threshold value to obtain an analysis result;
And carrying out feedback control on the electric energy meter according to the analysis result, and adjusting the working state in real time.
Further, according to the monitoring instruction, a corresponding data acquisition plan is generated, and the data acquisition plan is distributed to corresponding operators and operation terminals, including:
receiving and analyzing a monitoring instruction, and extracting key information, wherein the key information comprises the number of a monitored electric energy meter, the type of collected working physical data, the monitored time period and the monitored frequency;
initializing group increment learning parameters including individual number, iteration times, learning rate and inertia weight according to key information, and setting an optimization target of a data acquisition plan;
Taking an optimization target of the data acquisition plan as an evaluation function of group increment learning to evaluate the quality of each individual, carrying out iterative optimization on the initialized individual, updating the position of the individual in each iteration, and taking the final individual as the data acquisition plan when the preset iteration times are reached;
And generating a task allocation scheme according to the data acquisition plan, wherein the task allocation scheme comprises operators of each task, a used portable operation terminal and execution time.
Further, the calculation formula of the evaluation function is:
;
Wherein, Representing an evaluation function; Representing a total acquisition time weighting coefficient; Representing the total number of tasks; Representing an index variable; Represent the first The predicted acquisition time of each task; Represent the first Priority coefficients of the individual tasks; representing the maximum single acquisition time weighting coefficient; Representing the basic weighting coefficient of the data coverage rate; Representing the number of covered data types; representing the total number of data types that should be covered; Representing the adjustment coefficient; representing uncovered data type penalty weighting coefficients; An index representing the data type; Represent the first The number of the electric energy meters with collected seed data types; representing the number of collected electric energy meters; representing a worker workload balance weight coefficient; Representing the total number of operators; an index indicating the operator; Represent the first Task amount of individual operators; Representing the average task quantity of all operators; representing the maximum worker workload weighting factor.
Further, the working physical data of the electric energy meter monitored by the monitoring terminal is obtained in real time, including voltage fluctuation, current variation and power factor stability, and the monitoring terminal automatically calculates the gradient of the working physical data variation each time after completing one monitoring period, including:
Sending a monitoring instruction to a monitoring terminal, wherein the monitoring instruction comprises the starting time and the ending time of a monitoring period, and receiving the working physical data of the electric energy meter sent by the monitoring terminal in real time in the monitoring period;
Checking whether the current time reaches the end time of the monitoring period, and checking whether the working physical data set of the electric energy meter is received when the monitoring period is ended;
and processing the working physical data set of the electric energy meter, and calculating the change gradient of each data point relative to the previous data point, so as to realize continuous monitoring of the working physical data of the electric energy meter and calculation of the change gradient.
Further, performing data analysis according to a change gradient of working physical data, including statistical distribution and trend analysis, and detecting whether an abnormal value exists in the change gradient according to a preset threshold value to obtain an analysis result, including:
The method comprises the steps that a change gradient sequence of working physical data of the electric energy meter is received from a monitoring terminal, and an abnormal value detection threshold is set according to historical data;
Calculating the mean value and standard deviation statistic of the variation gradient sequence, and fitting the variation gradient sequence by using normal distribution;
Using a difference quantization index to evaluate the difference between the fitting distribution and the actual data, and performing time sequence analysis on the change gradient sequence to identify the trend and periodicity in the data, so as to obtain a time sequence analysis result;
according to the time sequence analysis result, a trend model is built, and the change gradient is predicted by using the trend model, so that a predicted value of each time point is obtained;
Measuring the difference between the predicted value of the trend model and the actual change gradient value by using a difference quantization index as a trend analysis index, comparing the actual change gradient value with a preset threshold value, and marking the point exceeding the threshold value as an abnormal value to obtain an abnormal detection result;
And integrating statistical distribution analysis, trend analysis and anomaly detection results to generate analysis results containing an outlier list, statistics and trend model parameter information.
Further, the calculation formula of the difference quantization index is:
;
Wherein, Representing a difference quantization index; Representing global adjustment coefficients; 、 Representing the weight coefficient; representing the total number of data points; Representing an index variable; Represent the first Actual change gradient values at the respective time points; Represent the first Predicted values for each time point; Representing data points; representing data points in an actual data distribution Probability of (2); representing data points in a fitting distribution Is a probability of (2).
Further, the calculation formula of the gradient of each data point relative to the previous data point is as follows:
;
Wherein, Represent the firstGradient of change of data point relative to previous data point; Represent the first Physical measurement of data points; Represent the first Physical measurement of data points;、、、、 Representing the adjustment parameters; representing the maximum value of the physical quantity in the dataset; Represents angular frequency; Represent the first Measuring time of the data points; Represent the first -A measurement time of 1 data point; Representing phase; representing a time interval of adjacent data points; representing a maximum value of the time interval in the dataset; An index representing data points; Representing a time sensitivity coefficient; Representing the magnification factor.
Further, according to the analysis result, feedback control is performed on the electric energy meter, and the working state is adjusted in real time, including:
according to the analysis result, whether the current working state of the electric energy meter is stable or not and whether the current working state of the electric energy meter is in a preset normal range or not is evaluated, and an evaluation result is obtained;
If the evaluation result shows that the working state of the electric energy meter is abnormal, performing an abnormality detection program, and calculating an adjusted control parameter and a corresponding adjustment quantity, including a voltage set value and a current distribution proportion, in the abnormality detection process to obtain a feedback control instruction;
And executing corresponding adjustment operations according to the feedback control instruction, wherein the adjustment operations comprise voltage output adjustment and current distribution optimization.
In a second aspect, an electric energy meter operation control system includes:
The data acquisition module is used for acquiring electricity utilization data of the electric energy meter in real time;
the central processing module is used for receiving and processing the data of the data acquisition module to obtain processed data;
The data management module is used for storing the processed data;
The intelligent control module is used for monitoring the use condition of the electric energy in real time and remotely performing electric appliance control, data analysis and fault early warning;
the data analysis and optimization module is used for analyzing the data of the data management module and determining the power consumption mode and the peak value period;
and the alarm and notification module is used for monitoring abnormal conditions in the use of electric energy, including sudden high-power consumption and equipment failure.
In a third aspect, a computing device includes:
One or more processors;
and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.
In a fourth aspect, a computer readable storage medium has a program stored therein, which when executed by a processor, implements the method.
The scheme of the invention at least comprises the following beneficial effects:
The method can comprehensively monitor various key working physical data of the electric energy meter, such as voltage, current, power factor, active power and reactive power, and ensure the integrity and accuracy of the data. The flexibility enables the data acquisition process to be more efficient and targeted, and can adapt to electric energy meter monitoring tasks of different scenes and requirements. The electric energy meter working physical data of the monitoring terminal can be obtained in real time, and the data change gradient after each monitoring period is calculated. The real-time performance and gradient analysis are helpful for finding out the tiny change of the working state of the electric energy meter in time, and provide powerful support for preventing potential problems.
Through carrying out statistical distribution and trend analysis on the change gradient of the working physical data and combining with a preset threshold value to detect abnormal values, potential information in the data can be deeply mined, abnormal conditions in the working state of the electric energy meter can be accurately identified, and important basis is provided for timely response and processing. According to the data analysis result, the method can perform intelligent feedback control on the electric energy meter and adjust the working state of the electric energy meter in real time. The intelligent control mode not only improves the working efficiency and stability of the electric energy meter, but also is beneficial to prolonging the service life of equipment and reducing the maintenance cost.
Drawings
Fig. 1 is a schematic flow chart of a method for controlling operation of an electric energy meter according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an operation control system of an electric energy meter according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a method for controlling operation of an electric energy meter, including the following steps:
step 11, receiving a monitoring instruction, wherein the monitoring instruction comprises monitoring all working physical data of the electric energy meter, and the working physical data comprise voltage, current, power factor, active power and reactive power;
step 12, generating a corresponding data acquisition plan according to the monitoring instruction, and distributing the data acquisition plan to corresponding operators and operation terminals;
step 13, working physical data of the electric energy meter monitored by the monitoring terminal, including voltage fluctuation, current change and power factor stability, are obtained in real time, and each time after one monitoring period is completed, the monitoring terminal automatically calculates the gradient of the working physical data change;
Step 14, data analysis is carried out according to the change gradient of the working physical data, including statistical distribution and trend analysis, and whether abnormal values exist in the change gradient is detected according to a preset threshold value, so that an analysis result is obtained;
And 15, carrying out feedback control on the electric energy meter according to the analysis result, and adjusting the working state in real time.
In the embodiment of the invention, the monitoring instruction containing the detailed working physical data is received, so that the operator and the operation terminal can be guided to carry out efficient and targeted data acquisition. This not only reduces the blindness of data acquisition, but also improves the efficiency and accuracy of data processing. The working physical data of the electric energy meter, including key information such as voltage fluctuation, current change and power factor stability, are obtained in real time, so that the real-time monitoring of the working state of the electric energy meter is ensured. Meanwhile, the data change gradient is automatically calculated after each monitoring period, so that the monitoring accuracy and response speed are further improved.
The data analysis is performed deeply by utilizing the gradient of the change of the working physical data, including statistical distribution and trend analysis, so that the system can grasp the working rule of the electric energy meter more accurately. In addition, potential faults or abnormal conditions can be timely identified through detecting abnormal values through preset threshold values, and powerful support is provided for preventive maintenance. According to the data analysis result, intelligent feedback control can be carried out on the electric energy meter, and the working state of the electric energy meter can be adjusted in real time. The intelligent adjustment mode ensures stable operation of the electric energy meter, optimizes energy use efficiency and reduces unnecessary energy consumption and cost expenditure.
In a preferred embodiment of the present invention, the step 11 of receiving a monitoring command, where the monitoring command includes monitoring each item of working physical data of the electric energy meter, where the working physical data includes voltage, current, power factor, active power and reactive power, may include:
A communication interface capable of receiving external instructions is built, and the communication interface can be a network interface based on protocols such as TCP/IP, HTTP, MQTT or a serial communication interface such as RS-232 and RS-485. And continuously monitoring the communication interface, and waiting for a monitoring instruction sent by an external system. After receiving the instruction, decoding the instruction through a preset analysis rule, and extracting working physical data items such as voltage, current, power factor, active power, reactive power and the like which need to be monitored. And determining parameters such as frequency, precision, duration and the like of data acquisition according to the analyzed monitoring demand. The data acquisition device, such as a smart meter data collector or Remote Terminal Unit (RTU), interfacing with the power meter ensures that it can operate according to the acquisition strategy. And sending a specific acquisition command to the electric energy meter or the data acquisition equipment, wherein the command contains a data item identifier to be acquired.
Waiting for the response of the electric energy meter or the data acquisition equipment and receiving the returned working physical data. And checking the received data to ensure the integrity and accuracy of the received data. If an abnormality or a loss of data is found, corresponding error processing or retransmission request is performed. If any abnormality is found in the data acquisition process, such as data exceeding a preset range or equipment failure, recording abnormal information, and timely notifying related personnel through a preset notification mechanism (such as short message, mail or APP push).
It is assumed that a certain electric power company needs to monitor an electric energy meter in the jurisdiction in real time so as to ensure the stable operation and electricity safety of a power grid.
And the central control system of the electric company sends a monitoring instruction to the electric energy meter management system through the TCP/IP network interface. After the electric energy meter management system monitors the instruction, the instruction is decoded by using a preset protocol format, and the data items to be monitored are determined to be voltage, current, power factor, active power and reactive power. According to the analysis result, the electric energy meter management system determines that data are collected every 5 minutes, and the accuracy is set to be two decimal places. And the intelligent data acquisition device connected with the electric energy meter is configured, so that the intelligent data acquisition device can acquire data according to the strategy. And when each acquisition period starts, the electric energy meter management system sends an acquisition command to the intelligent data acquisition device. After receiving the command, the intelligent data collector reads the current voltage, current, power factor, active power and reactive power data from the connected electric energy meter and sends the data back to the electric energy meter management system. After receiving the data, the electric energy meter management system stores the data in a local database. After each time data is received, the electric energy meter management system can check the data and check whether the data are in a reasonable range. If the voltage data collected at a certain time is found to be abnormally high or the current data is found to be zero, the system can record the abnormal conditions and inform operation and maintenance personnel of the power company to check and process through short messages.
In a preferred embodiment of the present invention, the step 12 of generating a corresponding data collection plan according to the monitoring instruction, and distributing the data collection plan to the corresponding operator and the corresponding operation terminal may include:
step 121, receiving and analyzing the monitoring instruction, extracting key information, including the number of the monitored electric energy meter, the type of the collected working physical data, the monitored time period and the monitored frequency;
Step 122, initializing group increment learning parameters including individual number, iteration times, learning rate and inertia weight according to the key information, and setting an optimization target of a data acquisition plan;
Step 123, taking an optimization target of the data acquisition plan as an evaluation function of group increment learning to evaluate the quality of each individual, carrying out iterative optimization on the initialized individual, updating the position of the individual in each iteration, and taking the final individual as the data acquisition plan when the preset iteration times are reached;
and 124, generating a task allocation scheme according to the data acquisition plan, wherein the task allocation scheme comprises operators, used portable operation terminals and execution time of each task.
In the embodiment of the present invention, in step 121, a monitoring command is received from the user, and then the command is parsed to extract key information. The key information includes the unique number of the electric energy meter (used for identifying specific monitoring objects), the type of physical data (such as voltage, current, power factor and the like) required to be collected, the specific time period of monitoring and the frequency of data collection. In step 122, population incremental learning is an optimization algorithm that simulates natural population behavior (e.g., ant colony, bird colony, etc.). In this step, the relevant parameters of the population increment learning are initialized according to the key information extracted in step 121. These parameters include:
Number of individuals: number of individuals in a population.
Iteration number is the maximum iteration number of the algorithm operation.
Learning rate-the rate at which individuals learn individually and globally.
Inertial weight, which affects the degree to which an individual maintains the current speed.
The optimization targets are to minimize data acquisition costs, maximize data acquisition efficiency, etc. In step 123, a population increment learning algorithm will be used to optimize the data acquisition plan. Through multiple iterations, the algorithm will gradually converge to an optimized data acquisition plan. After the optimized data acquisition plan is obtained, the system generates a specific task allocation scheme based on the plan in step 124. This includes assigning an operator, portable work terminal, and determining execution time to each data acquisition task.
Assuming a monitoring command, the electric energy meter with the monitoring number of A123 is required to collect voltage data once per hour in two time periods of 8:00-10:00 and 14:00-16:00 per day. The key information extracted in step 121 includes the electric energy meter number a123, the data type is voltage, the monitoring period is 8:00-10:00 and 14:00-16:00, and the frequency is once per hour. In step 122, the group increment learning parameter is initialized, for example, the number of individuals is set to 30, the iteration number is 100, the learning rate is 2, and the inertia weight is 0.9. The optimization objective is set to minimize the data acquisition cost. In step 123, the data acquisition plan is iteratively optimized by a population increment learning algorithm until the iteration number is reached, thereby obtaining a final data acquisition plan. In step 124, a task allocation scheme is generated according to the optimized data acquisition plan. For example, a worker 1 is assigned to perform data collection using the portable terminal a at 8:00 to 10:00, and a worker 2 is assigned to perform data collection using the portable terminal B at 14:00 to 16:00.
Step 121 can quickly and accurately extract key information by receiving and analyzing the monitoring instruction, provides a clear direction for the subsequent data acquisition work, and reduces the possibility of misunderstanding or omission of information.
Step 122 and step 123 utilize a group increment learning algorithm to optimize the data acquisition plan, which not only can intelligently adjust the acquisition strategy according to actual demands, but also can realize final task allocation under the condition of limited resources, thereby improving the utilization efficiency of the resources. The application of the group increment learning method enables the system to flexibly adjust the data acquisition plan according to different monitoring instructions, better adapts to various complex and changeable actual conditions, and improves the flexibility and adaptability of the system. The task allocation scheme generated in step 124 explicitly specifies the operator, portable job terminal and execution time for each task, which helps to reduce confusion and delays in task allocation, ensuring that data acquisition work can be completed on time and efficiently.
In another preferred embodiment of the present invention, the evaluation function is calculated as:
;
Wherein, Representing an evaluation function; Representing a total acquisition time weighting coefficient; Representing the total number of tasks; Representing an index variable; Represent the first The predicted acquisition time of each task; Represent the first Priority coefficients of the individual tasks; representing the maximum single acquisition time weighting coefficient; Representing the basic weighting coefficient of the data coverage rate; Representing the number of covered data types; representing the total number of data types that should be covered; Representing the adjustment coefficient; representing uncovered data type penalty weighting coefficients; An index representing the data type; Represent the first The number of the electric energy meters with collected seed data types; representing the number of collected electric energy meters; representing a worker workload balance weight coefficient; Representing the total number of operators; an index indicating the operator; Represent the first Task amount of individual operators; Representing the average task quantity of all operators; representing the maximum worker workload weighting factor.
In an embodiment of the present invention, in the present invention,Is the weighting coefficient of the total acquisition time and reflects the influence degree of the total acquisition time on the evaluation function.Is the maximum single acquisition time weighting coefficient, emphasizing the importance of the single longest acquisition time.Is a data coverage basic weighting coefficient, and represents the contribution of the covered data type number to the evaluation function.Is an uncovered data type penalty weighting factor, which applies a penalty to uncovered data types.The weighting coefficient is the weighting coefficient of the workload balance of the operator, and the workload balance is considered.Is the maximum worker workload weighting factor, focusing on the impact of the maximum workload.The total number of tasks is the number of tasks of the electric energy meter for which data needs to be collected. For each taskSetting the estimated acquisition timeAnd priority coefficient. Total number of data types to be coveredSuch as voltage, current, etc. Number of covered data typesZero at initialization, increasing as acquisition proceeds. For each data typeDetermining the number of electric energy meters which should be collected in the typeAnd the number of acquired. Total number of workersI.e. the number of people involved in the data acquisition effort. For each workerDistributing task amountsI.e. the number of tasks that the person is responsible for collecting. Average task size of all operatorsFor evaluating the balance of the workload. After the parameter initialization is completed, each evaluation index is gradually calculated according to the formula of the evaluation function. Traversing all tasks, calculating the sum of the products of the estimated acquisition time and the priority coefficient of each task, i.e. And finding out the task with the longest predicted acquisition time from all the tasks, and recording the time as the maximum value. Calculating the ratio of the number of covered data types to the total number of data types to be covered, and multiplying the ratio by an adjustment coefficientThe basic contribution of the data coverage is obtained.
Traversing all data types, computing the sum of penalty terms for uncovered types, i.e. The variance of the task amounts of all operators is calculated, the square of the difference between the task amount of each operator and the average task amount is calculated, then the sum is added and divided by the total number of operators, and finally the square root is taken. And finding out the task amount of the worker with the largest task amount as the maximum workload. Substituting each calculated evaluation index value into a formula of an evaluation function, and carrying out weighted summation to obtain a final evaluation function value。
By optimizing the weighting coefficient of the total acquisition time and the maximum single acquisition timeAnd) The overall and individual task acquisition time can be encouraged to decrease, thereby improving the overall efficiency of data acquisition. Task priority coefficient [ ]) The evaluation function can be flexibly adjusted according to the emergency degree or importance of different tasks, and the priority processing of the high-priority tasks is ensured. By adjusting the basic weighting coefficient of the data coverage rate) And penalty weighting coefficients for uncovered data types) The impact of covered and uncovered data types can be balanced, encouraging a more comprehensive coverage of all necessary data types, thereby improving the integrity and usability of the data. Worker workload balance weight coefficient) And the maximum worker workload weighting coefficient) The method is beneficial to realizing the balanced allocation of the task amount among operators and avoiding overload of individual operators, thereby improving the overall working efficiency and the satisfaction degree of the operators. The evaluation function quantifies a plurality of complex decision factors into a specific numerical value, so that the comparison between different schemes is more visual and objective, and the transparency and persuasion of the decision process are enhanced. The result of the evaluation function may be used as feedback to guide the continuous optimization and iteration of the data acquisition plan. By continually trial and error and adjusting the parameters, the final data acquisition strategy can be approximated.
In a preferred embodiment of the present invention, the step 13 of obtaining, in real time, the working physical data of the electric energy meter monitored by the monitoring terminal, including voltage fluctuation, current variation and power factor stability, and the monitoring terminal automatically calculates the gradient of the working physical data variation whenever completing one monitoring period may include:
Step 131, sending a monitoring instruction to a monitoring terminal, wherein the monitoring instruction comprises the starting time and the ending time of a monitoring period, and receiving the working physical data of the electric energy meter sent by the monitoring terminal in real time in the monitoring period;
step 132, checking whether the current time reaches the end time of the monitoring period, and checking whether the working physical data set of the electric energy meter is received when the monitoring period is ended;
And step 133, processing the working physical data set of the electric energy meter, and calculating the change gradient of each data point relative to the previous data point to realize continuous monitoring of the working physical data of the electric energy meter and calculation of the change gradient.
In the embodiment of the invention, a monitoring instruction is sent to determine the starting time and the ending time of a monitoring period.
And sending a monitoring instruction comprising the starting time and the ending time to the monitoring terminal. And in the time period of the starting time and the ending time, the working physical data of the electric energy meter sent by the monitoring terminal are received in real time. These data include voltage, current and power factor.
Step 132, the current time is continuously compared with the end time. At the current time = end time, it is confirmed whether a complete power meter working physical data set has been received.
Step 133, performing necessary preprocessing, such as filtering, denoising, etc., on the received working physical data set of the electric energy meter. Calculating a change gradient:
for example, voltage change gradients Wherein, the method comprises the steps of, wherein,Is the time interval of data sampling; is at the moment A measured voltage value; is the moment of time The measured voltage value.
Gradient of current changeWherein, the method comprises the steps of, wherein,Is at the momentA measured current value; Indicating time of day The measured current value.
Gradient of power factor variation,Is at the momentA measured power factor value; is at the moment The measured power factor value. Data processing and gradient calculation are continuously performed during the whole monitoring period.
By monitoring working physical data of the electric energy meter in real time, including voltage fluctuation, current change and power factor stability, the system can timely find abnormal conditions such as voltage dip, current overload or instability of the power factor, thereby triggering an early warning mechanism and ensuring safe operation of the power system. And calculating the change gradient of the working physical data, and analyzing the dynamic change of the working state of the electric energy meter. The analysis can provide deeper power system operation information, help workers understand the real-time state of the power grid, forecast potential problems, and take measures in time to prevent faults.
Through continuous monitoring of the data of the electric energy meter and calculation of the gradient of change, energy management personnel can more effectively distribute and schedule energy, optimize the energy utilization efficiency, reduce the energy waste and further save the cost.
By analyzing the data of the electric energy meter in real time, factors which may cause instability of the electric power system, such as voltage fluctuation or instability of power factors, can be found and processed in time, so that stability and reliability of the whole electric power system are enhanced. The electric energy meter working physical data and the change gradient analysis result obtained in real time can provide powerful support for decisions of an electric power company, such as equipment maintenance planning, energy purchasing strategies and the like, and operation efficiency and customer satisfaction are improved. The monitoring terminal automatically calculates the change gradient of the working physical data, reduces the workload of manually processing and analyzing the data, improves the working efficiency, and simultaneously reduces the possibility of human errors.
In a preferred embodiment of the present invention, the step 14 of performing data analysis according to the gradient of the change of the working physical data, including statistical distribution and trend analysis, and detecting whether an abnormal value exists in the gradient according to a preset threshold to obtain an analysis result may include:
Step 141, a change gradient sequence of the working physical data of the electric energy meter received from the monitoring terminal, and setting an abnormal value detection threshold according to the historical data;
step 142, calculating the mean value and standard deviation statistic of the variation gradient sequence, and fitting the variation gradient sequence by using normal distribution;
Step 143, using the difference quantization index to evaluate the difference between the fitting distribution and the actual data, and performing time sequence analysis on the variation gradient sequence to identify the trend and periodicity in the data, so as to obtain a time sequence analysis result;
step 144, according to the time sequence analysis result, a trend model is constructed, and the change gradient is predicted by using the trend model, so as to obtain a predicted value of each time point;
step 145, measuring the difference between the predicted value of the trend model and the actual change gradient value by using the difference quantization index as a trend analysis index, comparing the actual change gradient value with a preset threshold value, and marking the point exceeding the threshold value as an abnormal value to obtain an abnormal detection result;
And step 146, integrating statistical distribution analysis, trend analysis and anomaly detection results to generate analysis results containing an outlier list, statistics and trend model parameter information.
In the embodiment of the invention, a variation gradient sequence { of the working physical data of the electric energy meter is obtained from a monitoring terminal,,}. An outlier detection threshold is set for each of the varying gradients based on the historical data.
Step 142, for each gradient sequence, calculating the mean valueAnd standard deviation. For example, for voltage variation gradients, the mean and standard deviation are respectively:
Wherein, the method comprises the steps of, Representing the average value of the voltage; representing the total number of data points; Is shown at the moment Voltage change gradient of (2); representing the index variable.
Wherein, the method comprises the steps of,Representing the standard deviation of the voltage. Fitting varying gradient sequences using normal distribution, assumingObeys normal distribution(,)。
Step 143, a Kolmogorov-Smirnov test is applied to compare the difference between the actual data distribution and the fitted normal distribution. The test returns a statisticAnd corresponding toValues. If it isValues greater than the significance level (e.g., 0.05), the actual data cannot be rejected from the assumption of a normal distribution of the fit.
If it isThe value is larger, which indicates that the distribution of the actual data is similar to the normal distribution, and the fitting effect is better. If it isThe value is smaller, which indicates that the distribution of the actual data is different from the normal distribution, and the fitting effect is poor. And the change gradient sequence of the preprocessed working physical data of the electric energy meter is obtained from the monitoring terminal. An autocorrelation function of the sequence of varying gradients is calculated to evaluate the correlation between different points in time in the sequence. The autocorrelation function of the gradient sequence is thatWherein, the method comprises the steps of,Is shown in the lag phaseIs a coefficient of autocorrelation of (a); Is shown at the time point Is a variable gradient sequence value of (1); representing the mean value of the sequence of varying gradients; Is shown at the time point Front partA data value for each time; Representing the length of the varying gradient sequence; Indicating a lag phase; representing the index variable. Periodic patterns in the data can be aided in identifying, as periodic data can exhibit correlation at different lags. And calculating a partial autocorrelation function of the change gradient sequence to exclude the influence of other lag phases, and directly examining the correlation between the specific lag phase and the current value. The partial autocorrelation function of the variation gradient sequence has a calculation formula ofWherein, the method comprises the steps of,Is shown in the lag phaseIs a partial autocorrelation coefficient of (c).Is shown at the time pointValues of the gradient sequence of variation; representing the mean value of the sequence of varying gradients; representing the length of the time series; Is shown at the time point Front partData values for each time. Autocorrelation functionThe identification of the hysteresis relationship in the time series is facilitated, especially for the autoregressive model. By observing autocorrelation functionsAndCan identify trends (e.g., persistent positive or negative correlations) and periodicity (e.g., autocorrelation functions) in the dataPeaks that occur regularly in the figure).
Step 144, based on the time series analysis result, a trend model, such as a linear trend model, is constructed.
Step 145, measuring the difference between the predicted trend model value and the actual gradient value by using a difference quantization index (e.g. mean square error MSE): Wherein, the method comprises the steps of, Mean square error representing voltage variation gradient prediction; representing the total number of data points; Is shown at the time point Actual observations of the voltage change gradient of (a); Representing an index variable; Is shown at the time point Model predictive value of the voltage change gradient of (c). And comparing the actual change gradient value with a preset threshold value, and marking the point exceeding the threshold value as an abnormal value. For example, for a voltage change gradient, the outlier may be defined as:
Outlier value Wherein, the method comprises the steps of, wherein,Representing a voltage anomaly detection threshold.
Step 146, integrating the statistical distribution analysis, trend analysis and anomaly detection results to generate an analysis result containing the anomaly list, statistics (such as mean, standard deviation) and trend model parameter information.
Assuming a set of data for voltage gradients, the following results were obtained:
Mean value of =0.05 Standard deviation=0.1。
The normal distribution fits well and the Kolmogorov-Smirnov testThe value is greater than 0.05.
Time series analysis shows that there is a significant upward trend, and the parameters of the linear trend model are 0.03 and 0.002. The mean square error is 0.01, which shows that the trend model has better prediction effect.
Outlier detection found that, at the time point=50 SumAt=100, the voltage change gradient exceeds a preset threshold, marked as an outlier.
By detecting the abnormal value, abnormal data caused by equipment faults, data transmission errors and the like can be identified and removed, so that the quality of the whole data is improved. Statistical distribution analysis and time series analysis provide a means for deep understanding of data, helping users to better grasp the change rules and potential patterns of data.
Based on accurate data analysis and trend prediction, more powerful decision support such as equipment maintenance planning, energy management strategies and the like can be provided for enterprises or institutions. Through the prediction of the trend model, potential problems or anomalies can be found in time, so that measures are taken in advance, and the system performance is prevented from being reduced or faults are avoided. The whole analysis flow can be integrated into an automatic system, so that real-time analysis of data and automatic abnormal alarm are realized, and the intelligent level of the system is improved.
In another preferred embodiment of the present invention, the calculation formula of the differential quantization index is:
;
Wherein, Representing a difference quantization index; Representing global adjustment coefficients; 、 Representing the weight coefficient; representing the total number of data points; Representing an index variable; Represent the first Actual change gradient values at the respective time points; Represent the first Predicted values for each time point; Representing data points; representing data points in an actual data distribution Probability of (2); representing data points in a fitting distribution Is a probability of (2).
In the embodiment of the invention, the sequence of the gradient values of the existing actual change is ensuredAnd a predicted value sequence corresponding to the time pointWhereinFor indexing variable, from 1 to,Is the total number of data points. Inputting a sequence of actual gradient valuesPredicted value sequenceTotal number of data points. Determining global adjustment coefficientsWeight coefficientAnd. These parameters may be set according to actual conditions or experience to adjust the relative importance of the different parts of the differential quantization index. Inputting global adjustment coefficientsWeight coefficientAnd. For each point in timeCalculating the actual change gradient valueAnd predicted valueSquare error between. Then, the average of the square errors at all time points is calculated, i.e. The average of the square error term is output.
First, each data point is calculated according to the actual data distribution and the fitting distributionProbability in two distributionsAnd. Then, for each data pointCalculating a probability distribution difference term. Finally, the probability distribution difference terms of all data points are summed, i.e。
Multiplying the average of the square error terms by a weight coefficientMultiplying the sum of the probability distribution difference terms by the weight coefficient. The two weighted terms are then added and multiplied by a global adjustment coefficientObtaining the final difference quantization index。
Differential quantization indexDeviations between the actual data and the predicted data (represented by square error terms) and differences between the actual data distribution and the fitted distribution (represented by probability distribution difference terms) can be comprehensively considered. This comprehensive assessment capability enablesThe method can reflect the difference condition among the data more comprehensively and provide more accurate data difference measurement for analysts. By globally adjusting coefficientsAnd weight coefficientDifferential quantization indexHas high flexibility. These coefficients can be adjusted according to the actual requirements to highlight or balance the differences in different aspects. For example, if the accuracy of the predicted value is more concerned, it may be appropriately increasedIf the fitting of the data distribution is of greater concern, the value of (2) can be increasedIs a value of (2). Differential quantization indexHas sensitivity to small changes in data. Since the computation of both the squared error term and the probability distribution difference term involves a specific value for each data point, even if the data changes,The values will react accordingly. This results inAbnormal conditions can be found in time in the data monitoring and analyzing process. The squared error term reflects the average degree of deviation between the predicted value and the actual value, while the probability distribution difference term reveals the similarity or difference between the actual data distribution and the fitted distribution. This interpretability helps the analyst understand the nature and source of the data differences to make more reasonable decisions.
In another preferred embodiment of the present invention, the calculation formula of the gradient of change of each data point relative to the previous data point is:
;
Wherein, Represent the firstGradient of change of data point relative to previous data point; Represent the first Physical measurement of data points; Represent the first Physical measurement of data points;、、、、 Representing the adjustment parameters; representing the maximum value of the physical quantity in the dataset; Represents angular frequency; Represent the first Measuring time of the data points; Represent the first -A measurement time of 1 data point; Representing phase; representing a time interval of adjacent data points; representing a maximum value of the time interval in the dataset; An index representing data points; Representing a time sensitivity coefficient; Representing the magnification factor.
In an embodiment of the invention, a sequence of physical quantity measurements of existing data points is ensuredAnd a time series of measurements corresponding to the data points. Wherein, Is an index of data points. Input physical quantity measurement sequenceMeasuring time series. Determining adjustment parameters、、、、Angular frequencyPhase, phaseAnd the maximum value of the physical quantity in the datasetAnd a maximum value of the time interval. Input device、、、、、、、、. For each data pointCalculating the difference of the physical quantity measurement valuesAnd measuring the difference in time. Note that for the first data point=1), Since there is no previous data point, its gradient of change can be set to 0. Outputting a physical quantity measurement difference sequence and a measurement time difference sequence. For each data pointCalculating the molecular fractionA varying gradient of the molecular moiety sequence is obtained. For each data pointFirst, the time interval between adjacent data points is calculated. Then, calculateThe denominator partial sequence of the varying gradient is obtained. For each data pointDividing the numerator portion of the variation gradient by the denominator portion to obtain the variation gradient of the corresponding data point relative to the previous data point。
The formula comprises a plurality of adjustment parameters、、、、The parameters can be flexibly adjusted according to the actual data characteristics and analysis requirements, so that the data change under different scenes can be better adapted.
By amplifying the coefficientAnd time sensitivity coefficientThe formula can nonlinearly adjust the effect of the data point physical quantity measurement and the time interval on the variation gradient. This nonlinear adaptability enables the model to more accurately capture complex patterns of changes in the data.The term takes into account possible periodically varying factors. This is particularly useful for analyzing datasets having periodic features (e.g., seasonal variations, periodic fluctuations, etc.), which more accurately reflect the relative changes between the data points.
In a preferred embodiment of the present invention, the step 15 of performing feedback control on the electric energy meter according to the analysis result, and adjusting the working state in real time may include:
Step 151, according to the analysis result, whether the current working state of the electric energy meter is stable or not and whether the current working state of the electric energy meter is in a preset normal range or not is evaluated, and an evaluation result is obtained;
Step 152, if the evaluation result shows that the working state of the electric energy meter is abnormal, performing an abnormality detection procedure, and calculating an adjusted control parameter and a corresponding adjustment quantity, including a voltage set value and a current distribution proportion, in the abnormality detection process to obtain a feedback control instruction;
Step 153, according to the feedback control instruction, performing a corresponding adjustment operation, including adjusting the voltage output and optimizing the current distribution.
In the embodiment of the invention, statistics (such as mean value and standard deviation) and trend analysis indexes in the analysis result are compared with a preset normal range. And comparing the trend in the time sequence analysis result with the periodic component to judge whether the working state of the electric energy meter is stable. If all indexes are in a normal range and the working state is stable, the evaluation result is normal, otherwise, the evaluation result is abnormal.
Step 152, when the evaluation result is abnormal, starting an abnormality detection program. Set the original voltage set valueThe adjusted voltage set point is expressed as′=+Wherein, the method comprises the steps of, wherein,Is the voltage adjustment amount. Let the original current distribution ratio beThe adjusted current distribution ratio is expressed as′=+WhereinIs the current distribution proportional adjustment amount. The calculated adjusted voltage set value' Sum current split ratio' As a feedback control command.
Step 153, according to the feedback control instruction' The voltage output of the electric energy meter is adjusted in real time. According to feedback control instructions' Optimizing the current distribution of the electric energy meter in real time.
Assuming an original voltage set point of 220V for the power meter, the original current split ratio is 50:50 (split between the two circuits). Through data analysis, the abnormal working state of the electric energy meter is found, and adjustment is needed.
The operation state of the electric energy meter is unstable and the voltage fluctuation is large. Through calculation, the voltage is determined to be required to be adjusted to 5V, and the current distribution proportion is determined to be adjusted to be 60:40. Thus, a feedback control command is generated' =225V sum' =60:40. According to the feedback control instruction, the electric energy meter adjusts the voltage output to 225V in real time, and optimizes the current distribution ratio to be 60:40.
The working state of the electric energy meter is monitored in real time, and the electric energy meter is adjusted when abnormal, so that the stability of the system can be remarkably improved. When the working state of the electric energy meter is detected to be unstable or exceed the normal range, potential faults or performance degradation can be prevented by timely adjusting voltage output and current distribution, and continuous and stable operation of the electric energy meter is ensured.
The working parameters of the electric energy meter are timely adjusted, so that the equipment can be prevented from running under adverse conditions for a long time, and the abrasion and ageing of the equipment are reduced. By optimizing the current distribution, overload conditions in certain portions can be reduced, thereby extending the useful life of the meter and its associated components. Optimizing the voltage set point and the current distribution ratio can improve the energy use efficiency. Reasonable voltage and current settings can reduce energy waste, reduce line loss, and improve the overall efficiency of the electric energy meter. The energy saving and emission reduction device is beneficial to energy saving and emission reduction, and can save electricity charge for users. The abnormal working state of the electric energy meter may cause potential safety hazards such as electric fire. By means of real-time feedback control, the abnormal conditions can be timely found and processed, so that the risk of electrical accidents is reduced, and the safety of the system is enhanced. The stable operation of the electric energy meter directly relates to the electricity consumption experience of a user. By adjusting the working state in real time, the problems of power failure, voltage fluctuation and the like can be reduced, so that more stable and reliable power supply is provided, and the user satisfaction is improved. Such feedback control systems are an important component of smart grids. Through data analysis and automatic adjustment, intelligent management of the electric energy meter can be realized, and the self-adaptive capacity and response speed of the power grid are improved.
As shown in fig. 2, an embodiment of the present invention further provides an electric energy meter operation control system 20, including:
The data acquisition module 21 is used for acquiring electricity consumption data of the electric energy meter in real time;
the central processing module 22 is configured to receive and process the data of the data acquisition module, and obtain processed data;
A data management module 23 for storing the processed data;
The intelligent control module 24 is used for monitoring the electric energy use condition in real time and remotely performing electric appliance control, data analysis and fault early warning;
The data analysis and optimization module 25 is used for analyzing the data of the data management module and determining the power consumption mode and the peak value period;
An alarm and notification module 26 is provided for monitoring for anomalies in power usage, including sudden high power consumption, equipment failure.
It should be noted that, the system is a system corresponding to the above method, and all implementation manners in the above method embodiment are applicable to the embodiment, so that the same technical effects can be achieved.
Embodiments of the invention also provide a computing device comprising a processor, a memory storing a computer program which, when executed by the processor, performs a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
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