Distribution network automation equipment operation state evaluation method, device, equipment and medium

Abstract

The invention belongs to the field of distribution network automation, and particularly relates to a distribution network automation equipment operation state evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining the index data of distribution network automation equipment; eliminating the seasonal influence of the standard data by adopting an STL decomposition algorithm to obtain seasonal data; based on the seasonal data, an improved smooth entropy weight method is adopted to calculate an objective weight; collecting expert scoring data, and calculating a subjective weight by adopting an analytic hierarchy process; and based on the objective weight, the subjective weight and the seasonal data, calculating the operation state index of the automation equipment by adopting a superior and inferior solution distance method. An STL decomposition algorithm is introduced, and seasonal fluctuation of monthly data is stripped, so that a trend component and a residual component reflect the inherent state of equipment more truly, and misjudgment of periodic factors on health scores is avoided; subjective and objective weights are fused, data rules and domain knowledge are considered at the same time, deviation of a single weighting method is avoided, and the accuracy and reliability of an evaluation result are improved.

Description

Technical Field

[0001] This invention belongs to the field of distribution network automation, and specifically relates to methods, devices, equipment and media for evaluating the operating status of distribution network automation equipment. Background Technology

[0002] Urban distribution network automation equipment is crucial for improving the reliability of power supply in medium and large cities. These devices integrate telemetry, remote signaling, and remote control functions ("three-remote" functions) to achieve comprehensive monitoring of the distribution network. Currently, the replacement of distribution network automation equipment mainly relies on the manufacturer's identification of family-related defects and the actual troubleshooting experience of operation and maintenance units. This leads to instances of incorrect replacement of some normal equipment and omission of problematic equipment. When problematic automation equipment loses its "three-remote" functions, the fault assessment range of the integrated dual-core system expands, affecting dispatchers' operation of primary equipment and consequently increasing the scope of power outages. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and medium for evaluating the operating status of distribution network automation equipment, thereby solving the problem of missed or incorrect replacement of distribution network automation equipment in the background art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for evaluating the operating status of distribution network automation equipment, comprising: Obtain indicator data from power distribution network automation equipment; The STL decomposition algorithm is used to eliminate the seasonality of the indicator data, resulting in deseasonalized data. An improved smoothing entropy weighting method is used to calculate the objective weights of deseasonalized data; Collect expert scoring data and use the analytic hierarchy process to calculate the subjective weights of seasonal data. Based on objective weights, subjective weights, and de-seasonalized data, the operating status index of automated equipment is calculated using the superior-inferior solution distance method.

[0005] Preferably, in the step of obtaining the indicator data of the distribution network automation equipment, the indicator data includes monthly online rate, monthly commissioning and decommissioning frequency, monthly communication traffic, monthly indicator dashboard scores of each operation and maintenance unit, and number of days in operation.

[0006] Preferably, the step of using the STL decomposition algorithm to eliminate the seasonality of indicator data and obtain deseasonalized data includes: The indicator data is processed into time series data, and the time series data is averaged to obtain an initial estimate of the seasonal component. Based on the initial estimates of the seasonal components, the seasonal components are calculated using local weighted regression. Subtracting the seasonal component from the indicator data yields de-seasonalized data.

[0007] Preferably, the step of calculating the objective weights of deseasonalized data using the improved smoothing entropy weighting method based on deseasonalized data includes: The de-seasonalized data is then normalized to obtain a normalized matrix; By introducing a smoothing factor, the information entropy of the normalized matrix is ​​calculated. Based on information entropy, calculate the objective weights of deseasonalized data.

[0008] Preferably, the step of collecting expert scoring data and calculating the subjective weights of seasonal data using the analytic hierarchy process includes: Construct a priority judgment matrix for the criterion layer and a priority judgment matrix for the indicator layer; Collect expert scoring data; the expert scoring data is obtained by inviting 12 professional experts to conduct pairwise comparisons and scores on the criterion layer and the indicator layer using the 1-9 scale method; Based on the scoring data and the priority judgment matrix of the criterion layer, the weight of the criterion layer is calculated; Based on the scoring data and the priority judgment matrix of the indicator layer, the weight of the indicator layer is calculated. The subjective weight is obtained by multiplying the weights of the criterion layer and the weights of the indicator layer.

[0009] Preferably, the step of calculating the operating status index of automated equipment based on objective weights, subjective weights, and de-seasonalized data using the superior-inferior solution distance method includes: The objective weights and subjective weights are combined using a multiplicative synthesis method to form a comprehensive weight. A standardized evaluation matrix is ​​constructed based on comprehensive weights and seasonal data. Determine the positive and negative ideal values ​​of the operational status index of the evaluation object in the standardized evaluation matrix; Calculate the Euclidean distance between the evaluation object and the positive and negative ideal values; Based on Euclidean distance, the degree of closeness between the evaluated object and the ideal value is calculated, which serves as an index of the operating status of automated equipment.

[0010] Preferably, the step of calculating the degree of closeness between the evaluation object and the ideal value based on Euclidean distance includes: Calculate the degree of similarity between each evaluation object and the optimal solution: ; in, This is an index representing the operating status of distribution network automation equipment. The standardized evaluation is the Euclidean distance between the positive ideal value and the positive ideal value. The Euclidean distance between the standardized evaluation and the negative ideal value.

[0011] In a second aspect, the present invention provides a device for evaluating the operating status of distribution network automation equipment, comprising: The acquisition module is used to acquire indicator data from distribution network automation equipment. The elimination module is used to eliminate the seasonality of indicator data using the STL decomposition algorithm to obtain deseasonalized data. The objective weighting module is used to calculate the objective weights of deseasonalized data using the improved smoothing entropy weighting method. The subjective weighting module is used to collect expert scoring data and calculate the subjective weights of seasonal data using the analytic hierarchy process. The evaluation module is used to calculate the operating status index of automated equipment based on objective weights, subjective weights, and de-seasonalized data, using the superior-inferiority distance method.

[0012] In a third aspect, the present invention provides an electronic device, including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the aforementioned method for evaluating the operating status of distribution network automation equipment.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the method for evaluating the operating status of distribution network automation equipment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: By introducing the STL decomposition algorithm, seasonal fluctuations in monthly data are removed, allowing trend and residual components to more accurately reflect the inherent state of the equipment and avoiding misjudgments of health scores due to periodic factors. Subjective and objective weights are integrated, taking into account both data patterns and domain knowledge, avoiding the bias of a single weighting method and improving the accuracy and reliability of the evaluation results. The entropy weight method is used to calculate objective weights based on the inherent dispersion of the data, and a smoothing factor is used to suppress the influence of extreme values ​​and enhance the stability of the data. The AHP method integrates the experience of 12 experts to construct a two-layer weighting system, and ensures the logical rationality of subjective weights through consistency checks. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the method for evaluating the operating status of distribution network automation equipment according to Embodiment 1 of the present invention; Figure 2This is a structural block diagram of the power distribution network automation equipment operation status evaluation device according to Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0017] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0018] AHP: Analytic Hierarchy Process; TOPSIS: Technique for Order Preference by Similarity to Ideal Solution. STL: Seasonal and Trend decomposition using Loess, a seasonal trend decomposition method based on local weighted regression; Example 1 like Figure 1 As shown, the method for evaluating the operating status of distribution network automation equipment includes the following specific steps: S1. Obtain the indicator data of power distribution network automation equipment; The metrics data include monthly online rate, monthly commissioning and decommissioning frequency, monthly communication traffic, monthly dashboard scores for each operation and maintenance unit, and number of days in operation. The indicator data is standardized to obtain standard data; specifically, the indicator data is divided into positive indicators and negative indicators, wherein: Monthly uptime and monthly scores on dashboards for each operations and maintenance unit are positive indicators, and their standardized calculation formula is as follows: ; Monthly commissioning and decommissioning frequency, monthly communication traffic, and operational days are all inverse indicators, and their standardized calculation formulas are as follows: ; in, and They represent the first i The first power distribution automation devicej The indicator values ​​before and after standardization of the indicator data. and They represent The maximum and minimum values.

[0019] S2. Use the STL decomposition algorithm to eliminate the seasonality of the indicator data and obtain deseasonalized data.

[0020] In distribution network automation data processing, the STL decomposition algorithm can effectively analyze the seasonality, trend, and residual characteristics of the data. The process includes initializing seasonal components, estimating seasonal components using locally weighted regression, calculating deseasonalized data to obtain trend components, performing robust iterative optimization, and completing the iteration by setting convergence conditions, ultimately obtaining the seasonal, trend, and residual components. These steps support a deeper understanding of distribution network automation data and help to more accurately assess equipment operating status.

[0021] S21. The standardized indicator data is processed into time series data, and the time series data is averaged to obtain an initial estimate of the seasonal component. Specific steps include: Assume the time series data of each standard data point in the power distribution network automation is: , T The time series length is [length], and the seasonal period is [period]. m =4, corresponding to the four quarters of a year. During initialization, the data within each seasonal cycle are averaged to obtain an initial estimate of the seasonal component. : ; in, Indicates the current time point, when hour, The corresponding value is obtained through periodic extension.

[0022] S22. Based on the initial estimates of the seasonal components, estimate the seasonal components using local weighted regression. At each time point Select with A local window centered on the user, with a window size of [size missing]. To ensure the validity and time-bound nature of the data, select =7. Weights are applied to the data points within the window. Based on data points and The distance is determined, and the closer the distance, the greater the weight. This invention introduces the data standard deviation to dynamically and adaptively adjust the weights. Calculate each quarter... Standard deviation within the sliding window : , ; in, For the first i Time series data for a period of time, This represents the average value of the data within the window.

[0023] When calculating weights in local weighted regression, the adaptive weight function used in this invention is: ; in, This represents the average of the standard deviations of the entire time series. The bandwidth parameter controls the rate at which the weights decay with distance. After multiple tests on the data involved in this paper, setting it to 0.5 yielded the best results.

[0024] Within a local window, a linear polynomial is fitted by minimizing the weighted sum of squared residuals. By continuously fitting and calculating coefficients and The minimum value of the superposition of the polynomials is obtained: ; S23. Solve using weighted least squares method and : First, construct the design matrix and weighted data vector. Let the data points within the local window be... For each data point ( ), construct the design matrix and weighted data vector Design Matrix It is The matrix has each row corresponding to a data point, the first column is all 1s, and the second column is... The value of, i.e.: ; Weighted data vector It is the raw data With corresponding weights The vector obtained by multiplication is:

[0025] Then, the present invention uses the weighted least squares method to solve the problem. and In the weighted least squares method, the normal equation is solved as follows: ; Obtain the coefficient vector ,in It is a diagonal matrix, where the diagonal elements are weights. That is, by substituting the above matrix into the normal equation and solving, we obtain... The estimated value, of which The first element is The second element is .

[0026] Obtain the fitting coefficients and Then, estimates of the seasonal components at all time points were obtained. .

[0027] S24. Calculate the seasonally removed data and estimate the trend component: From the original time series Subtract the currently estimated seasonal component from To obtain the raw deseasonalized data : ; For raw deseasonalized data We will use the locally weighted regression method again to estimate the trend component. Select window size as Local window, determine weights, and fit a polynomial by minimizing the weighted sum of squared residuals. :

[0028] in, To remove the weights of seasonal data within a local window, the calculation method is similar to that used when estimating seasonal components. The fitting coefficients are then obtained by solving for them. and Thus, the trend component estimate is obtained. .

[0029] S25, Robust Iterative Optimization The seasonal and trend decomposition method based on locally weighted regression improves the robustness of the decomposition and reduces the impact of outliers through multiple iterations. This invention employs an iterative weighted least squares method, adjusting the weights of the locally weighted regression in each iteration based on the residual information obtained from the previous iteration. Let the... The seasonal component estimate at the next iteration is: The estimated value of the trend component is The residual is .

[0030] Repeat steps S22-S24, adjusting the weights based on the magnitude and distribution of the residuals. The iteration stops when the change in the sum of squared residuals is less than 0.001, at which point a stable trend component is obtained. Seasonal ingredients and residual components These components can be used to analyze the characteristics of distribution network automation data in depth, providing a more accurate data foundation for subsequent equipment operation status evaluation.

[0031] The seasonal components obtained from decomposition For standard data Seasonal adjustments are performed to obtain deseasonalized data. : .

[0032] S3. The improved smoothing entropy weight method is used to calculate the objective weights of deseasonalized data.

[0033] First of all Normalization is performed to obtain the normalized matrix. : ; in, n This indicates the total number of distribution network automation devices.

[0034] To avoid the impact of data fluctuations or extreme values ​​on information entropy calculation, a smoothing factor is introduced. =10 -6 Calculate the information entropy of the normalized matrix. : ; Calculate the objective weights of deseasonalized data : .

[0035] S4. Collect expert scoring data and use the analytic hierarchy process (AHP) to calculate the subjective weights of the seasonal data.

[0036] S41. Construct the criterion-level first-judgment matrix and the indicator-level priority-judgment matrix. This invention first divides the problem into three levels: the target level, the criteria level, and the indicator level, and then constructs a judgment matrix based on this substructure. To facilitate expert scoring, this invention derives the annual values ​​of each indicator as scoring criteria. The criteria level consists of four layers: annual online rate (B1), annual commissioning / discontinuation frequency (B2), annual communication traffic (B3), and other influencing factors (B4). The key indicators targeted by the indicator level are: annual online rate over four years (C1-C4), annual commissioning / discontinuation frequency over four years (C5-C8), annual communication traffic over four years (C9-C12), indicator dashboard scores for each maintenance unit over four years (C13-C16), and number of days in operation (C17).

[0037] Taking 2020-2023 as an example: The table shows the following metrics from 2020 to 2023: annual online rate (D1-D4), annual commissioning and decommissioning frequency (D5-D8), annual communication traffic (D9-D12), dashboard scores for each operations and maintenance unit (D13-D16), and number of days in operation (D17). The resulting structure is shown in Table 1. Table 1. Structure of Status Indicators for Distribution Network Automation Equipment

[0038] A priority judgment matrix for the criterion layer was constructed based on four key indicators. B The matrix is ​​a 4×5 matrix: ; Among them, the layer priority judgment matrix B No. p The weight of the index is , p =1,2,3,4; A priority judgment matrix for the indicator layer was constructed based on 17 key indicators. D The matrix is ​​a 17×17 matrix: ; Among them, the indicator layer priority judgment matrix D No. The weight of the index is , q =1,2,3,…,17; Judgment Matrix B No. p The column vectors are calculated using the square root method, resulting in a matrix. : ; in, p For matrix Number of elements in the middle.

[0039] matrix After normalization, the index weight column vector is obtained. ,Right now: ; Similarly, calculate the column vectors of the judgment matrix D to obtain the matrix. ; Calculate the criterion weight column vector using the method described above. .

[0040] The weights of the indicator layer are represented as The criterion layer weights are expressed as .

[0041] S42. Based on the criterion weight column vector and the indicator weight column vector, construct the criterion layer priority judgment matrix through expert scoring.

[0042] To populate the elements of the judgment matrix, a comparison scale needs to be defined to quantify the relative importance between two criteria or options. A scale of 1-9 is typically used; for example, 1 indicates that both are equally important, 3 indicates that the former is slightly more important than the latter, 5 indicates that the former is moderately more important than the latter, and 9 indicates that the former is extremely more important than the latter. Each element aij in the matrix represents the importance comparison between element i and element j, and its value is an integer between 1 and 9, as shown in Table 2.

[0043] Table 2: Meaning of Priority Judgment Matrix Data

[0044] This invention invited 12 experts from the State Grid Beijing Electric Power Company, specializing in the transmission, operation and maintenance, and acceptance of distribution network automation equipment, to participate in the construction of the judgment matrix, ensuring that the weights of each indicator conform to objective reality.

[0045] The experts first assigned weights to the criteria layer, resulting in a scoring table as shown in Table 3. Table 3 Criterion Layer Judgment Matrix B

[0046] Among them, B4 is the annual online rate, B3 is the annual number of cancellations, B2 is the annual communication traffic, and B1 is other influencing factors.

[0047] Based on the experts' judgment matrix, a consistency check is performed to prevent a situation where B1 > B2 > B3 > B4. A(1)CR=0.0542<0.1A(2)CR=0.0751<0.1A(3)CR=0.0959<0.1 A(4)CR=0.0487<0.1A(5)CR=0.0182<0.1A(6)CR=0.0136<0.1 A(7)CR=0.0319<0.1A(8)CR=0.0413<0.1A(9)CR=0.047<0.1 A(10)CR=0.0977<0.1A(11)CR=0.0755<0.1A(12)CR=0.0272<0.1 After testing, the A(n)CR results of the 12 experts were all less than 0.1, passing the consistency test. Then, the geometric mean method was used to summarize the results to obtain the priority judgment matrix for the criterion layer: Table 4. Criterion Layer Judgment Matrix B

[0048] S43: Forming the priority judgment matrix of the indicator layer The rule for forming the priority judgment matrix of the indicator layer is the same as that of S42. The final judgment matrix, calculated using the geometric mean method, is shown in the table below: Table 4. Indicator-level judgment matrix D1 (Annual online rate from 2020 to 2023)

[0049] Table 5. Judgment Matrix D2 of the Indicator Layer (Number of Investments and Withdrawals per Year from 2020 to 2023)

[0050] Table 6. Indicator Layer Judgment Matrix D3 (Annual Communication Traffic from 2020 to 2023)

[0051] Table 7. Indicator Layer Judgment Matrix D4 (Others)

[0052] S44: Calculation of the Overall Weight of AHP Indicators The above results indicate that all judgment matrices pass the consistency test. By weighting the weights of the indicator layer D relative to the criterion layer B, the subjective weights of each evaluation indicator can be obtained. The specific calculation method is as follows: Subjective weight =Criterion Layer Weights *Indicator Layer Weights

[0053] Table 8. Calculation of Overall Weights using the Analytic Hierarchy Process (AHP)

[0054] S5. Based on subjective and objective weights, the operating status index of automated equipment is calculated using the Top-Side Distance Method (TOPSIS, Technique for Order Preference by Similarity to Ideal Solution). S51. Decomposing the subjective weight matrix of annual parameters Monthly weight matrix , combined and Calculate the overall weight of each indicator. Decomposition of the annual parameter subjective weight matrix Monthly weight matrix Soon Each element in the matrix is ​​expanded to 12, and then concatenated in the original order to obtain a new monthly weight matrix. Then, the multiplicative composition method is used to combine them. and Forming a comprehensive weight : ; S52. Constructing a standardized evaluation matrix : ; S53. Determine the positive ideal value R+ and negative ideal value R- of the operating status index of the evaluation object in the standardized evaluation matrix.

[0055] ; ; S54. Calculate the Euclidean distance between the evaluation object and the positive and negative ideal values. , .

[0056] ; ; S55. Calculate the degree of similarity between each evaluation object and the optimal solution, i.e., the automated equipment operation status index.

[0057] ; in, For the first i The operating status index of distribution network automation equipment represents the health level of the distribution network automation equipment. The higher the value, the better the health of the distribution network automation equipment this year.

[0058] when When the value is less than the set threshold, the corresponding power distribution automation equipment is replaced.

[0059] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a distribution network automation equipment operation status evaluation device, comprising: The acquisition module is used to acquire indicator data from distribution network automation equipment. The elimination module is used to eliminate the seasonality of indicator data using the STL decomposition algorithm to obtain deseasonalized data. The objective weighting module is used to calculate the objective weights of deseasonalized data using the improved smoothing entropy weighting method. The subjective weighting module is used to collect expert scoring data and calculate the subjective weights of seasonal data using the analytic hierarchy process. The evaluation module is used to calculate the operating status index of automated equipment based on objective weights, subjective weights, and de-seasonalized data, using the superior-inferiority distance method.

[0060] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a method for evaluating the operating status of distribution network automation equipment; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0061] Memory 101 can be used to store computer program 103. Processor 102 implements Embodiment 1 by running or executing the computer program stored in memory 101 and calling data stored in memory 101. step.

[0062] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0063] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0064] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for evaluating the operating status of distribution network automation equipment, and the processor 102 can execute multiple instructions to achieve the following: Obtain indicator data from power distribution network automation equipment; The STL decomposition algorithm is used to eliminate the seasonality of the indicator data, resulting in deseasonalized data. An improved smoothing entropy weighting method is used to calculate the objective weights of deseasonalized data; Collect expert scoring data and use the analytic hierarchy process to calculate the subjective weights of seasonal data. Based on objective weights, subjective weights, and de-seasonalized data, the operating status index of automated equipment is calculated using the superior-inferior solution distance method.

[0065] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0066] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the operational status of distribution network automation equipment, characterized in that, include: Obtain indicator data from power distribution network automation equipment; The STL decomposition algorithm is used to eliminate the seasonality of the indicator data, resulting in deseasonalized data. An improved smoothing entropy weighting method is used to calculate the objective weights of deseasonalized data; Collect expert scoring data and use the analytic hierarchy process to calculate the subjective weights of seasonal data. Based on objective weights, subjective weights, and de-seasonalized data, the operating status index of automated equipment is calculated using the superior-inferior solution distance method.

2. The method for evaluating the operating status of distribution network automation equipment according to claim 1, characterized in that, In the step of obtaining the indicator data of the distribution network automation equipment, the indicator data includes monthly online rate, monthly commissioning and decommissioning frequency, monthly communication traffic, monthly indicator dashboard scores of each operation and maintenance unit, and number of days in operation.

3. The method for evaluating the operating status of distribution network automation equipment according to claim 1, characterized in that, The step of using the STL decomposition algorithm to eliminate the seasonality of indicator data and obtain deseasonalized data includes: The indicator data is processed into time series data, and the time series data is averaged to obtain an initial estimate of the seasonal component. Based on the initial estimates of the seasonal components, the seasonal components are calculated using local weighted regression. Subtracting the seasonal component from the indicator data yields de-seasonalized data.

4. The method for evaluating the operating status of distribution network automation equipment according to claim 1, characterized in that, The step of calculating the objective weights of deseasonalized data using the improved smoothing entropy weight method based on deseasonalized data includes: The de-seasonalized data is then normalized to obtain a normalized matrix; By introducing a smoothing factor, the information entropy of the normalized matrix is ​​calculated. Based on information entropy, calculate the objective weights of deseasonalized data.

5. The method for evaluating the operating status of distribution network automation equipment according to claim 1, characterized in that, The process of collecting expert scoring data and calculating the subjective weights of seasonal data using the analytic hierarchy process includes: Construct a priority judgment matrix for the criterion layer and a priority judgment matrix for the indicator layer; Collect expert scoring data; the expert scoring data is obtained by inviting 12 professional experts to conduct pairwise comparisons and scores on the criterion layer and the indicator layer using the 1-9 scale method; Based on the scoring data and the priority judgment matrix of the criterion layer, the weight of the criterion layer is calculated; Based on the scoring data and the priority judgment matrix of the indicator layer, the weight of the indicator layer is calculated. The subjective weight is obtained by multiplying the weights of the criterion layer and the weights of the indicator layer.

6. The method for evaluating the operating status of distribution network automation equipment according to claim 1, characterized in that, The steps for calculating the operating status index of automated equipment using the superior-inferior solution distance method based on objective weights, subjective weights, and de-seasonalized data include: The objective weights and subjective weights are combined using a multiplicative synthesis method to form a comprehensive weight. A standardized evaluation matrix is ​​constructed based on comprehensive weights and seasonal data. Determine the positive and negative ideal values ​​of the operational status index of the evaluation object in the standardized evaluation matrix; Calculate the Euclidean distance between the evaluation object and the positive and negative ideal values; Based on Euclidean distance, the degree of closeness between the evaluated object and the ideal value is calculated, which serves as an index of the operating status of automated equipment.

7. The method for evaluating the operating status of distribution network automation equipment as described in claim 6, characterized in that, The step of calculating the degree of closeness between the evaluation object and the ideal value based on Euclidean distance includes: Calculate the degree of similarity between each evaluation object and the optimal solution: ； in, This is an index representing the operating status of distribution network automation equipment. The standardized evaluation is the Euclidean distance between the positive ideal value and the positive ideal value. The Euclidean distance between the standardized evaluation and the negative ideal value.

8. A device for evaluating the operational status of distribution network automation equipment, characterized in that, include: The acquisition module is used to acquire indicator data from distribution network automation equipment. The elimination module is used to eliminate the seasonality of indicator data using the STL decomposition algorithm to obtain deseasonalized data. The objective weighting module is used to calculate the objective weights of deseasonalized data using the improved smoothing entropy weighting method. The subjective weighting module is used to collect expert scoring data and calculate the subjective weights of seasonal data using the analytic hierarchy process. The evaluation module is used to calculate the operating status index of automated equipment based on objective weights, subjective weights, and de-seasonalized data, using the superior-inferiority distance method.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for evaluating the operating status of distribution network automation equipment as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the method for evaluating the operating status of distribution network automation equipment as described in any one of claims 1 to 7.

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