Power user side line loss monitoring and diagnosis method, system, electronic equipment and storage medium

Abstract

The embodiment of the present invention provides a method, system, electronic device and storage medium for monitoring and diagnosing line losses on the side of electric power users, and belongs to the field of line loss monitoring and diagnosis. The method includes: collecting active and reactive power data of the total meter and sub-meters; using the total meter power prediction model to obtain the predicted total meter power data, comparing it with the measured total meter power data, and making abnormal judgments in combination with preset monitoring rules; when an abnormality is detected, using different correction methods to correct the sub-meter data, and by optimizing and minimizing the prediction error, obtaining the prediction residuals under different correction methods, and selecting the correction methods with the residuals from small to large as the diagnosis results. By establishing a total meter power prediction model, the overall power consumption of the power system is estimated without relying on the real-time branch head and end meter readings. When an abnormality is detected, different correction methods are used to locate the sub-meter power data, thereby improving the positioning accuracy and rapid response.

Description

Technical Field

[0001] The present invention relates to the technical field of line loss monitoring and diagnosis, and in particular to a method, system, electronic equipment and storage medium for monitoring and diagnosing line losses on the side of an electric power user. Background Art

[0002] Calculation based on a fixed ratio or power consumption ratio is a method of allocation after calculating the actual line loss. For abnormal line loss, a comprehensive investigation is initiated only when the fluctuation of the proportion of the settlement cycle (i.e. the ratio to the total power consumption) exceeds a certain threshold. First, the cycle is long, which means it is difficult to detect abnormalities in time; second, it is difficult to quickly diagnose the abnormal location, which requires comprehensive manual investigation.

[0003] The traditional power flow calculation method solves the equation group to obtain the line loss rate, which requires a large number of detection quantities (detection instruments) and fixed quantities (line topology, electrical parameters, etc.), increasing the instrument installation and maintenance costs and parameter detection costs. It is also difficult to update the line topology and parameters in a timely manner when they change due to time or human influence, resulting in difficulty in solving the problem. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, system, electronic device and storage medium for monitoring and diagnosing line losses on the power user side, which are used to completely or at least partially solve the technical problems existing in the above-mentioned prior art.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for monitoring and diagnosing line loss at a power user side, comprising:

[0006] Collect active power and reactive power data of the total meter and sub-meters that are operating normally over a period of time;

[0007] The pre-built total meter power prediction model is used to predict the collected sub-meter power consumption active power and reactive power data to obtain the predicted total meter power data, and the predicted total meter power data is compared with the measured total meter power data, and abnormal judgment is made in combination with the preset monitoring rules;

[0008] When an abnormality is detected, different correction methods are used to correct the sub-meter power data, and a new prediction is made based on the corrected data. By optimizing and minimizing the prediction error, the prediction residuals under different correction methods are obtained. The correction methods with the residuals from small to large are selected as the diagnosis results, the diagnosis results are output, and alarm processing is performed according to the set threshold conditions.

[0009] Optionally, the process of constructing the total meter power prediction model includes:

[0010] The MLP neural network model is used as the basic framework of the total meter power prediction model. The active power and reactive power data of the sub-meter electricity consumption are used as input, and the active power and reactive power data of the total meter electricity consumption are used as output. The total meter power prediction model is established, wherein the MLP neural network model includes an input layer, a hidden layer and an output layer.

[0011] Optionally, the predicted total meter power data is compared with the measured total meter power data to obtain the prediction residual, and the predicted normal fluctuation range parameters are determined based on the prediction residual.

[0012] Optionally, the correction processing steps include:

[0013] Establish a correction equation to correct the power data of the terminal meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction;

[0014] The least square method is used to determine the best fitting coefficient of the correction equation to minimize the prediction residual after correction;

[0015] Compare the prediction residuals after correction at different ends, determine the order of the line segments with abnormalities, and conduct inspections in the order of the line segments with abnormalities.

[0016] Optionally, the correction equation is in the form of a linear combination equation, including a linear combination of active power and reactive power, and data correction is achieved by adjusting coefficients.

[0017] Optionally, the best fitting coefficients of the correction equation are determined using the least squares method to minimize the correction prediction residuals, including:

[0018] Step 1: Choose a coding for problem solving;

[0019] Step 2: Randomly generate N feasible solutions, that is, generate the first generation of chromosomes. The parameter N is the population size of chromosomes.

[0020] Step 3: Then use the fitness function to calculate the fitness of each chromosome, and determine whether the chromosome group meets the termination condition at this time. If it does, go to step 8;

[0021] Step 4: Calculate the probability of each chromosome being selected in the next evolution based on the fitness level and calculation formula;

[0022] Step 5: Perform replication. From the current chromosome group consisting of N chromosomes, select chromosomes according to the probability of each chromosome being selected calculated in step 4, and select M chromosomes as part of the next generation chromosome group, where M <N;

[0023] Step 6: Perform a crossover operation with the mating probability in this group of M chromosomes to generate NM chromosomes, then perform a mutation operation on the NM chromosomes generated after the crossover, and merge the mutated NM chromosomes with the M chromosomes generated by replication to generate a new chromosome group;

[0024] Step 7: Return to step 3 and check whether the newly generated chromosome population meets the termination condition. If it does, proceed to step 8. If not, proceed to the next round of iteration.

[0025] Step 8: Terminate the calculation and output the optimal result.

[0026] On the other hand, the present invention also provides a power user side line loss monitoring and diagnosis system, comprising:

[0027] A collection unit is used to collect the active power and reactive power data of the total meter and sub-meters that are operating normally over a period of time;

[0028] The prediction unit is used to predict the collected active power and reactive power data of the sub-meter electricity consumption by using the pre-built total meter power prediction model to obtain the predicted total meter power data, and compare the predicted total meter power data with the measured total meter power data, and make an abnormal judgment in combination with the preset monitoring rules;

[0029] The diagnostic unit is used to correct the sub-meter power data using different correction methods when an abnormality is detected, and to determine the coefficient of the correction method based on minimizing the prediction error of the corrected data, and to compare the prediction residuals under different correction methods, select the correction methods with residuals from small to large as the diagnostic results, output the diagnostic results, and perform alarm processing according to the set threshold conditions.

[0030] Optionally, the diagnostic unit is specifically configured to:

[0031] Establish a correction equation to correct the power data of the terminal meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction;

[0032] The least square method is used to determine the best fitting coefficient of the correction equation to minimize the prediction residual after correction;

[0033] Compare the prediction residuals after correction at different ends, determine the order of the line segments with abnormalities, and conduct inspections in the order of the line segments with abnormalities.

[0034] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for monitoring and diagnosing line losses on the power user side when executing the program.

[0035] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for monitoring and diagnosing line losses on the power user side are implemented.

[0036] Through the above technical solution, the basic configuration of the electric meter with a total meter plus branch meters does not need to calculate the line loss rate, which reduces the cost. In addition, by establishing a total meter power prediction model, the overall power consumption of the power system can be estimated without relying on the real-time total meter readings. When an abnormality is detected, different correction methods can be used to correct the sub-meter power data. It has high flexibility, enabling it to cope with different types of abnormalities and find the most appropriate solution.

[0037] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0039] Figure 1 It is an implementation flow chart of a method for monitoring and diagnosing line losses on the power user side provided by an embodiment of the present invention;

[0040] Figure 2 is a correction processing implementation flow chart provided by an embodiment of the present invention;

[0041] Figure 3 is an implementation flow chart of determining the best fitting coefficient of a correction equation provided by an embodiment of the present invention;

[0042] Figure 4 It is a detailed implementation flow chart of a method for monitoring and diagnosing line losses on the power user side provided by an embodiment of the present invention;

[0043] Figure 5 It is a power topology diagram provided by an embodiment of the present invention;

[0044] Figure 6 It is a structural schematic diagram of a power user side line loss monitoring and diagnosis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0046] See also Figure 1 As shown, it is an implementation flow chart of a method for monitoring and diagnosing line loss on the power user side provided by an embodiment of the present invention, which includes the following execution steps:

[0047] Step 100: Collect active power and reactive power data of the main meter and sub-meters that operate normally over a period of time.

[0048] Step 101: Use the pre-built total meter power prediction model to predict the collected sub-meter power consumption active power and reactive power data to obtain the predicted total meter power data, compare the predicted total meter power data with the measured total meter power data, and make an abnormal judgment in combination with the preset monitoring rules.

[0049] In some embodiments, the construction process of the total meter power prediction model includes: using the MLP neural network model as the basic architecture of the total meter power prediction model, using the active power and reactive power data of the sub-meter electricity consumption as input, and the active power and reactive power data of the total meter electricity consumption as output, to establish the total meter power prediction model, wherein the MLP neural network model includes an input layer, a hidden layer, and an output layer.

[0050] In some implementations, when executing step 101, the predicted total meter power data is compared with the measured total meter power data to obtain a prediction residual, and a predicted normal fluctuation range parameter is determined based on the prediction residual.

[0051] Specifically, the preset monitoring rules used when executing step 101 for abnormal judgment are multiple judgment criteria, including but not limited to: a single point exceeds area A, multiple points continuously exceed area B or area C, multiple points continuously increase or decrease, and multiple points continuously are distributed on one side of the center line but do not fall within area C, wherein area A, area B, and area C are determined by normal fluctuation range parameters.

[0052] In some embodiments, when the standard deviation is used: Region C is [-σ, σ], Region B is [-2σ, -σ] and [σ, 2σ], and Region A is [-3σ, -2σ] and [2σ, 3σ]. Where σ is substituted into σp and σQ, respectively. When the quantile is used, Region C is [q1-0.5IQR, q3+0.5IQR], Region B is [q1-IQR, q1-0.5IQR] and [q3+0.5IQR, q3+IQR], and Region A is [q1-1.5IQR, q1-IQR] and [q3+IQR, q3+1.5IQR]. Where IQR = q3–q1, substituted into the fractional digits of P and Q, respectively.

[0053] Step 102: When an abnormality is detected, different correction methods are used to correct the sub-meter power data, and a new prediction is made based on the corrected data. By minimizing the prediction error, the prediction residuals under different correction methods are obtained, and the correction methods with the residuals from small to large are selected as the diagnosis results. The diagnosis results are output, and alarm processing is performed according to the set threshold conditions.

[0054] In some embodiments, when performing step 102, refer to Figure 2 As shown, a correction process implementation flow chart provided by an embodiment of the present invention includes the following execution steps:

[0055] S1020: Establish a correction equation to correct the power data of the terminal electric meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction.

[0056] The correction equation is in the form of a linear combination equation, including a linear combination of active power and reactive power, and data correction is achieved by adjusting coefficients.

[0057] S1021: Use the least squares method to determine the best fitting coefficient of the correction equation so as to minimize the correction prediction residual.

[0058] S1022: Compare the prediction residuals after correction at different terminals, determine the line segment where the abnormality occurs, and conduct investigations in the order of the line segments where the abnormality occurs.

[0059] In some embodiments, see Figure 3 As shown, when executing step S1021, the following steps may be specifically performed:

[0060] Step 1: Choose a coding for problem solving;

[0061] Step 2: Randomly generate N feasible solutions, that is, generate the first generation of chromosomes. The parameter N is the population size of chromosomes.

[0062] Step 3: Then use the fitness function to calculate the fitness of each chromosome, and determine whether the chromosome group meets the termination condition at this time. If it does, go to step 8, otherwise, go to step 4;

[0063] Step 4: Calculate the probability of each chromosome being selected in the next evolution according to the fitness level and calculation formula;

[0064] Step 5: Perform replication. From the current chromosome group consisting of N chromosomes, select chromosomes according to the probability of each chromosome being selected calculated in step 4, and select M chromosomes as part of the next generation chromosome group, where M <N;

[0065] Step 6: Perform a crossover operation with the mating probability in this group of M chromosomes to generate NM chromosomes, then perform a mutation operation on the NM chromosomes generated after the crossover, and merge the mutated NM chromosomes with the M chromosomes generated by replication to generate a new chromosome group;

[0066] Step 7: Return to step 3 and check whether the newly generated chromosome population meets the termination condition. If it does, proceed to step 8. If not, proceed to the next round of iteration.

[0067] Step 8: Terminate the calculation and output the optimal result.

[0068] The calculation formula in step 4 is: the probability of chromosome i being selected = the fitness of chromosome i / the sum of the fitness of all chromosomes.

[0069] See also Figure 4 As shown, it is a detailed implementation flow chart of a method for monitoring and diagnosing line loss on the power user side provided by an embodiment of the present invention, including the following execution steps:

[0070] S400: Collecting active power and reactive power data of the main meter and sub-meters that are operating normally over a period of time.

[0071] S401: Using the MLP neural network model as the basic framework of the total meter power prediction model, using the active power and reactive power data of the sub-meters as input and the active power and reactive power data of the total meter as output, a total meter power prediction model is established, wherein the MLP neural network model includes an input layer, a hidden layer and an output layer.

[0072] S402: Use the pre-built total meter power prediction model to predict the collected sub-meter power consumption active power and reactive power data to obtain the predicted total meter power data, compare the predicted total meter power data with the measured total meter power data, and make an abnormal judgment in combination with the preset monitoring rules.

[0073] S403: When an abnormality is detected, a correction equation is established to correct the power data of the terminal electric meter, and the corrected power data is combined with the non-corrected data and re-input into the total meter power prediction model for prediction.

[0074] S404: randomly giving values ​​of fitting coefficients of multiple sets of correction equations, and substituting them into the multiple sets of correction equations to obtain new sub-meter power data and errors.

[0075] S405: Select a correction equation with a small error from the multiple groups of correction equations, and fine-tune its fitting coefficient to obtain a new error.

[0076] S406: Select a correction equation with a smaller error among the new errors, continue fine-tuning and selecting until the stop condition is reached, and select the fitting coefficient corresponding to the result with the smallest error as the best fitting coefficient obtained by optimization.

[0077] S407: Using the least square method to determine the best fitting coefficient of the correction equation so as to minimize the prediction residual after correction.

[0078] S408: Select the correction method of the residual from small to large as the diagnosis result, output the diagnosis result, and perform alarm processing according to the set threshold condition.

[0079] In one specific embodiment, see Figure 5 As shown in FIG. 1 , a power topology diagram provided by an embodiment of the present invention is provided for reference. Figure 5 Taking the power topology diagram shown in FIG. as an example, a method for monitoring and diagnosing line loss on the power user side provided by the present invention is further explained in detail. Figure 5 In the figure, 0 is the total meter, 1 to 5 are the end-user sub-meters, and A, B, C, D, E, F, and G are line nodes respectively.

[0080] 1. Collect the active power and reactive power records of the main meter and sub-meter for a long period of time (such as 1 year, 3 months, etc.) of normal operation: P0~P5, Q1~Q5. Perform data preprocessing: such as deleting the records with missing measurements (null values), deleting the records with abnormal values ​​(such as exceeding the meter range, etc.), etc.

[0081] 2. Take P1~P5 and Q1~Q5 as input, P0 and Q0 as output, establish an MLP neural network model, and use the collected data for training to obtain the total meter power prediction model. At the same time, the difference between the predicted value and the actual value of each record, that is, the residual, can be obtained. Perform statistical processing on the residual to obtain the standard deviation σP and σQ of the two outputs. Or the first quartile q1P, q1Q and the third quartile q3P, q3Q, etc. Then obtain the monitoring index of the predicted power difference. When using the standard deviation: Area C is [-σ, σ], Area B is [-2σ, -σ] and [σ, 2σ], and Area A is [-3σ, -2σ] and [2σ, 3σ]. Substitute σ into σp and σQ respectively. When using quantiles, area C is [q1-0.5IQR, q3+0.5IQR], area B is [q1-IQR, q1-0.5IQR] and [q3+0.5IQR, q3+IQR], and area A is [q1-1.5IQR, q1-IQR] and [q3+IQR, q3+1.5IQR]. Where IQR = q3 – q1, with the fractional digits of P and Q substituted in respectively.

[0082] 3. Use the obtained model to monitor the real-time periodic data collection, that is, use the model input to collect P1~P5 and Q1~Q5 in real time, get the predicted P'0 and Q'0, and compare them with the real-time collected P0 and Q0 to get their differences. After collecting a set of data (such as half an hour, 6 period measurements), calculate the average value. Use the judgment criteria to judge the average of multiple groups, for example:

[0083] Criterion 1: 1 point is outside the A zone (greater than Ucl or less than Lcl);

[0084] Criterion 2: 2 out of 3 consecutive points are outside of area B on one side;

[0085] Criterion 3: 4 out of 5 consecutive points are outside Zone C;

[0086] Criterion 4: 6 consecutive points increasing or decreasing.

[0087] Considering that abnormalities are generally persistent, in order to reduce false alarms, a threshold for the number of consecutive triggers can be set (such as more than 5 times), and an abnormal alarm will be issued only when it is exceeded.

[0088] 4. When there is an abnormal alarm, perform diagnosis and processing.

[0089] 4.1. Correct the terminal meter data in sequence: For example, when correcting terminal 1, establish the correction equation for P1 and Q1: P'1 = aP1 + bQ1 + c; Q'1 = dP1 + eQ1 + f, where a, b, c, d, e, f are the coefficients to be fitted. The corrected abnormal group data is combined with other non-corrected data, such as P2, Q2, P3, Q3... and the neural network model to obtain a new total meter output.

[0090] 4.2. Use genetic algorithm for optimization (1. Randomly give multiple groups of ab values, substitute the new P'1Q'1 to replace P1Q1, and use the method in step 2 to get the new model and the error of the model output (the sum of the squares of the difference between the output value and the actual value) 2. Select several groups with smaller errors from multiple groups, and randomly (genetically, mutate) fine-tune ab (taking coefficient ab as an example), and get new errors again 3. Select several groups with smaller errors from the new results and previous results, and continue to fine-tune and select until the stopping condition is reached (number limit or no improvement in the result) 4. Select the corresponding ab of the result with the smallest error, which is the best fitting coefficient obtained by optimization), and get the best fitting formula (coefficient) to minimize the error between the output value of the neural network model input after correction and the actual measurement.

[0091] 4.3. Compare the minimum errors of different terminal corrections to determine the priority detection terminal. For example, if the error after correction of terminal 3 is the smallest, it is more likely that the branch (DE segment) where terminal 3 is located has an abnormality.

[0092] 4.4. According to the topological structure, make corrections in sequence along the selected priority terminal direction, such as the active power of the CD segment is: (P2+P3), and the reactive power is: (Q2+Q3); the active power of the BC segment is: (P2+P3+P4+P5), and the reactive power is: (Q1+Q2+Q3+Q4), etc. Establish the correction equation similar to 4.1. If the CD segment contains two terminals 2 and 3, make corrections together:

[0093] P'2 = aP2+bQ2+c;

[0094] Q'2 = dP2+eQ2+f;

[0095] P'3 = aP3+bQ3+c;

[0096] Q'3 = dP3+eQ3+f;

[0097] Similarly, the error calculation function is established by combining the non-corrected (except for the terminals 2 and 3) data and the neural grid. 4.5. Similar to 4.2, the prediction residual under the optimal fitting coefficient is obtained. 4.6. Similar to 4.3, the line segment most likely to have an abnormality is sorted and determined.

[0098] 5. Check the abnormal line sections according to the results in 4.6.

[0099] 6. Summarize and accumulate experience, that is, the relationship between different abnormal locations, causes and fitting coefficients. For example, an increase in the corresponding resistance will cause the active fitting coefficient to increase, and an increase in reactance will cause the reactive fitting coefficient to increase. This can improve the pertinence and efficiency of the investigation in step 5.

[0100] See also Figure 6 FIG. 1 is a schematic diagram of a system for monitoring and diagnosing power user-side line losses provided by an embodiment of the present invention, comprising:

[0101] The collection unit 60 is used to collect the active power and reactive power data of the main meter and sub-meters that are operating normally over a period of time;

[0102] The prediction unit 61 is used to predict the collected active power and reactive power data of the sub-meter electricity consumption by using the pre-built total meter power prediction model to obtain the predicted total meter power data, and compare the predicted total meter power data with the measured total meter power data, and make an abnormality judgment in combination with the preset monitoring rules;

[0103] The diagnostic unit 62 is used to correct the sub-meter power data using different correction methods when an abnormality is detected, and determine the coefficient of the correction method based on minimizing the prediction error of the corrected data, and compare the prediction residuals under different correction methods, select the correction method with the residual from small to large as the diagnosis result, output the diagnosis result, and perform alarm processing according to the set threshold conditions.

[0104] In some embodiments, the diagnosis unit 62 is specifically configured to:

[0105] Establish a correction equation to correct the power data of the terminal meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction;

[0106] The least square method is used to determine the best fitting coefficient of the correction equation to minimize the prediction residual after correction;

[0107] Compare the prediction residuals after correction at different ends, determine the order of the line segments with abnormalities, and conduct inspections in the order of the line segments with abnormalities.

[0108] Through the above technical solution, the basic configuration of the electric meter with a total meter plus branch meters does not need to calculate the line loss rate, which reduces the cost. In addition, by establishing a total meter power prediction model, the system can estimate the overall power consumption of the power system without relying on the real-time total meter readings. When an abnormality is detected, the system can use different correction methods to correct the sub-meter power data. It has high flexibility, enabling the system to respond to different types of abnormalities and find the most appropriate solution.

[0109] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for monitoring and diagnosing line losses on the power user side when executing the program.

[0110] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for monitoring and diagnosing line losses on the power user side are implemented.

[0111] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0119] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for monitoring and diagnosing line loss on the power user side, characterized in that: include: Collect active power and reactive power data of the total meter and sub-meters that are operating normally over a period of time; The pre-built total meter power prediction model is used to predict the collected sub-meter power consumption active power and reactive power data to obtain the predicted total meter power data, and the predicted total meter power data is compared with the measured total meter power data, and abnormal judgment is made in combination with the preset monitoring rules; When an abnormality is detected, different correction methods are used to correct the sub-meter power data, and a new prediction is made based on the corrected data. By optimizing and minimizing the prediction error, the prediction residuals under different correction methods are obtained. The correction methods with the residuals from small to large are selected as the diagnosis results, the diagnosis results are output, and alarm processing is performed according to the set threshold conditions.

2. The method for monitoring and diagnosing line loss at the power user side according to claim 1, characterized in that: The construction process of the total meter power prediction model includes: The MLP neural network model is used as the basic framework of the total meter power prediction model. The active power and reactive power data of the sub-meter electricity consumption are used as input, and the active power and reactive power data of the total meter electricity consumption are used as output. The total meter power prediction model is established, wherein the MLP neural network model includes an input layer, a hidden layer and an output layer.

3. The method for monitoring and diagnosing line loss at the power user side according to claim 1, characterized in that: The predicted total meter power data is compared with the measured total meter power data to obtain the predicted residual, and the predicted normal fluctuation range parameters are determined based on the predicted residual.

4. The method for monitoring and diagnosing line loss at the power user side according to claim 1, characterized in that: The correction process steps include: Establish a correction equation to correct the power data of the terminal meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction; The least square method is used to determine the best fitting coefficient of the correction equation to minimize the prediction residual after correction; Compare the prediction residuals after correction at different ends, determine the order of the line segments with abnormalities, and conduct inspections in the order of the line segments with abnormalities.

5. The method for monitoring and diagnosing line loss at the power user side according to claim 4, characterized in that: The correction equation is in the form of a linear combination equation, including a linear combination of active power and reactive power, and data correction is achieved by adjusting coefficients.

6. The method for monitoring and diagnosing line loss at the power user side according to claim 4, characterized in that: The least squares method is used to determine the best fitting coefficients of the correction equation to minimize the corrected prediction residuals, including: Step 1: Choose a coding for problem solving; Step 2: Randomly generate N feasible solutions, that is, generate the first generation of chromosomes. The parameter N is the population size of chromosomes. Step 3: Then use the fitness function to calculate the fitness of each chromosome, and determine whether the chromosome group meets the termination condition at this time. If it does, go to step 8; Step 4: Calculate the probability of each chromosome being selected in the next evolution based on the fitness level and calculation formula; Step 5: Perform replication. From the current chromosome group consisting of N chromosomes, select chromosomes according to the probability of each chromosome being selected calculated in step 4, and select M chromosomes as part of the next generation chromosome group, where M <N; Step 6: Perform a crossover operation with the mating probability in this group of M chromosomes to generate NM chromosomes, then perform a mutation operation on the NM chromosomes generated after the crossover, and merge the mutated NM chromosomes with the M chromosomes generated by replication to generate a new chromosome group; Step 7: Return to step 3 and check whether the newly generated chromosome population meets the termination condition. If it does, proceed to step 8. If not, proceed to the next round of iteration. Step 8: Terminate the calculation and output the optimal result.

7. A power user side line loss monitoring and diagnosis system, characterized in that: include: A collection unit is used to collect the active power and reactive power data of the total meter and sub-meters that are operating normally over a period of time; The prediction unit is used to predict the collected active power and reactive power data of the sub-meter electricity consumption by using the pre-built total meter power prediction model to obtain the predicted total meter power data, and compare the predicted total meter power data with the measured total meter power data, and make an abnormal judgment in combination with the preset monitoring rules; The diagnostic unit is used to correct the sub-meter power data using different correction methods when an abnormality is detected, and to determine the coefficient of the correction method based on minimizing the prediction error of the corrected data, and to compare the prediction residuals under different correction methods, select the correction methods with residuals from small to large as the diagnostic results, output the diagnostic results, and perform alarm processing according to the set threshold conditions.

8. The power user side line loss monitoring and diagnosis system according to claim 7 is characterized in that: The diagnostic unit is specifically used for: Establish a correction equation to correct the power data of the terminal meter, and re-input the corrected power data and the non-corrected data into the total meter power prediction model for prediction; The least square method is used to determine the best fitting coefficient of the correction equation to minimize the prediction residual after correction; Compare the prediction residuals after correction at different ends, determine the order of the line segments with abnormalities, and conduct inspections in the order of the line segments with abnormalities.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the power user side line loss monitoring and diagnosis method as described in any one of claims 1-6 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring and diagnosing line losses at the power user side as described in any one of claims 1 to 6 are implemented.

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