A substation inspection scheme generation method and system based on necessity evaluation

Abstract

A substation inspection scheme generation method and system based on necessity evaluation. The method comprises: collecting operation necessity, operation risk, fault information, running state, equipment redundancy and technical update information of each substation to construct a substation inspection index set; performing standardization processing on the substation inspection index set, determining a subjective influence weight of each index by using a BWM analysis method, and determining an objective influence weight of each index by using a CRITIC analysis method; combining the subjective influence weight and the objective influence weight to obtain a combined weight of each index according to a game theory; comprehensively evaluating the inspection necessity of the substation by using a grey correlation method and a TOPSIS decision algorithm; generating an inspection scheme based on an evaluation result and available inspection resources, and optimizing the inspection scheme according to an execution effect of the inspection scheme. The application evaluates the necessity of secondary equipment inspection, formulates an optimal inspection scheme, improves the pertinence and efficiency of the inspection, and guarantees the safe operation of the power system.

Description

Technical Field

[0001] This invention belongs to the field of substation inspection, and specifically relates to a method and system for generating substation inspection plans based on necessity assessment. Background Technology

[0002] Substations are a crucial component of the power system, undertaking the important tasks of power conversion, transmission, and distribution. However, with the continuous development and changes in the power system, the operational status and safety of substation equipment face increasingly complex challenges. To ensure the normal operation and safety of substation equipment, regular inspections are necessary to promptly identify and resolve potential problems, thus guaranteeing the stable operation of the power grid.

[0003] Secondary equipment plays a crucial role in substations, responsible for monitoring, controlling, and protecting the normal operation of the power system. However, due to the wide variety and diverse functions of secondary equipment in substations, traditional substation inspection plans are usually based on fixed periodic inspections, neglecting the differences in the necessity and risk level of secondary equipment within the substation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for generating substation inspection plans based on necessity assessment, thereby solving the technical problem of adaptive generation of inspection plans for secondary equipment in substations.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0006] This invention first discloses a method for generating substation inspection plans based on necessity assessment, the method comprising the following steps:

[0007] Step 1: Collect operational necessity information, operational risk information, fault information, operating status information, equipment redundancy information and technology update information for each substation to construct a set of substation inspection indicators;

[0008] Step 2: Standardize the substation inspection indicator set, use the BWM analysis method to determine the subjective influence weight of each indicator in the substation inspection indicator set, and use the CRITIC analysis method to determine the objective influence weight of each indicator in the substation inspection indicator set.

[0009] Step 3: Based on game theory, combine the subjective influence weights and the objective influence weights to obtain the combined weights for each indicator;

[0010] Step 4: Based on the combined weights, the necessity of inspecting multiple substations is comprehensively evaluated using the grey relational analysis method and the TOPSIS decision algorithm to obtain the inspection necessity evaluation results;

[0011] Step 5: Generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and optimize the inspection plan according to the execution effect of the inspection plan.

[0012] The present invention further includes the following preferred embodiments:

[0013] The collection of operational necessity information, operational risk information, fault information, operating status information, equipment redundancy information, and technology update information for each substation further includes:

[0014] Based on the determined operational necessity information, the operational risk information is determined based on the safety and compliance index and the equipment importance index of each device; the fault information is determined based on the equipment failure degree index and the equipment importance index; the operating status information is determined based on the adaptability, lifespan index and the equipment importance index of a single device; the equipment redundancy information is determined based on the number of redundant and standby devices and the total number of devices; and the technology update information is determined based on the technology update and obsolescence risk index and the equipment importance index of a single device.

[0015] The standardization process for the substation inspection indicator set further includes:

[0016] Establish the original evaluation index matrix X = (x ij ） m×n ;in, x ij For the first i The first substation j The measured or scored values ​​of each evaluation indicator. m The number of substations to be evaluated. n To assess the number of indicators;

[0017] The original evaluation index matrix X The elements in the matrix are standardized to obtain a standardized matrix. X* = （x ij * ） m×n :

[0018]

[0019] For evaluation indicators j The average value, s j For evaluation indicators j The mean squared error;

[0020] .

[0021] The determination of the subjective influence weight of each indicator in the substation inspection indicator set using the BWM analysis method further includes:

[0022] Choose the most important indicator C from n indicators. B And the least important indicator C w Using a numerical scale of 1 to 9 to compare and display C B For other indicators, and for C, other indicators... w The relative importance is then used to obtain the judgment vector A. B =(a B1 a B2 a B3 , ...a Bn ) and A w =(a 1w a 2w a 3w , ...a nw ) T ;

[0023] Among them, a Bi Representing the most important indicator C B Compared to other indicators C i The relative importance of a iw Representing other indicators C i Compared to the least important indicator C w The relative importance;

[0024] A preliminary consistency check is performed based on the aforementioned judgment vector;

[0025] The optimal weights are calculated by constructing a nonlinear programming model. The objective function and constraints are as follows:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] The optimal weight value is obtained based on the above objective function and constraints. W j Consistency index value ξ * .

[0032] The determination of the objective influence weight of each indicator in the substation inspection indicator set using the CRITIC analysis method further includes:

[0033] definition v j Correlation coefficients for each evaluation indicator:

[0034]

[0035] Using the standardized matrix X* Calculate the Pearson correlation coefficients among the evaluation indicators and construct a correlation coefficient matrix. Then from the matrix R Calculate the independence coefficients for each evaluation indicator. η j :

[0036]

[0037] Calculate the necessity coefficient of each evaluation indicator based on its correlation coefficient and independence coefficient. C j :

[0038]

[0039] Calculate the first j The weight of each evaluation indicator W j :

[0040] .

[0041] The step of combining the subjective influence weight and the objective influence weight according to game theory to obtain the combined weight of each indicator further includes:

[0042] The index weights obtained through the BWM method and the improved CRITIC method are denoted as w1 = (w 11 w 12 , ... ,w 1n ) and w2 = (w 21 w 22 , ... , w 2n Then, the initial combination weights are constructed using a linear combination of w1 and w2:

[0043]

[0044] α1 and α2 are the combination coefficients of subjective and objective weights, respectively;

[0045] The objective function and constraints for minimizing the deviation between the initial combined weights and the subjective and objective weights are as follows:

[0046]

[0047] By solving this model, the optimal combined weights are obtained. This problem involves finding the minimum value under equality constraints. A Lagrangian function is constructed, where λ is the penalty factor.

[0048]

[0049] The optimal first derivative condition is:

[0050]

[0051] The corresponding system of linear equations is:

[0052]

[0053] The combination coefficients α1 and α2 are obtained and then normalized.

[0054]

[0055] The final combined weights are:

[0056] .

[0057] The method of comprehensively evaluating the necessity of inspecting multiple substations using grey relational analysis and the TOPSIS decision algorithm further includes:

[0058] Step 4.1: Eliminate the dimensions of the indicator data and perform standardization processing to obtain a standardized matrix. ：

[0059]

[0060] R ij For the first i The first substation j Standard values ​​for each indicator; x ij It is the first i The first substation j The original values ​​of each indicator;

[0061] Step 4.2: Combine the n x 1 weight matrix obtained from game theory combinatorial weighting with the standardized matrix. R Multiply to obtain the standardized matrix V, and then calculate the positive ideal solution V. + and the ideal solution V - ;

[0062]

[0063] vj + and v j - For the first j The best and worst values ​​of each indicator in each substation;

[0064] Step 4.3: Calculate the distance from the substation to the positive ideal solution and the secondary ideal solution:

[0065]

[0066] d i + and d i - The first i Euclidean distances from each substation to the positive and secondary ideal solutions;

[0067] Step 4.4: Calculate the first... i The first substation j Grey relational coefficient of each indicator i(j) + , i(j) - :

[0068]

[0069] i Number of substations; j For indicator quantity; The resolution coefficient; max n、 min n Calculate the maximum and minimum values ​​for dimension n, respectively. m、 min m Calculate the maximum and minimum values ​​for dimension m, respectively;

[0070] Step 4.5: Calculate the correlation degree:

[0071]

[0072] Step 4.6: Calculate the gray distance T i + , T i - and overall relevance C i ;

[0073]

[0074]

[0075]

[0076]

[0077] a and b are the evaluators' preference values ​​for the location of the substation, satisfying a+b=1.

[0078] This invention also discloses a substation inspection plan generation system based on necessity assessment, utilizing the aforementioned substation inspection plan generation method based on necessity assessment, comprising:

[0079] The comprehensive data integration module is used to collect information on the operational necessity, operational risks, faults, operating status, equipment redundancy, and technology updates of each substation in order to construct a set of substation inspection indicators.

[0080] The inspection necessity assessment index calculation module is used to standardize the substation inspection index set, use the BM analysis method to determine the subjective influence weight of each index in the substation inspection index set, and use the CRITIC analysis method to determine the objective influence weight of each index in the substation inspection index set; and combine the subjective influence weight and the objective influence weight according to game theory to obtain the combined weight of each index.

[0081] The inspection necessity comprehensive evaluation module is used to comprehensively evaluate the inspection necessity of multiple substations based on the combined weights, using the grey relational analysis method and the TOPSIS decision algorithm, and obtain the inspection necessity evaluation result.

[0082] The inspection plan generation and optimization module is used to generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and to optimize the inspection plan based on the execution effect of the inspection plan.

[0083] Accordingly, this application also discloses a terminal, including a processor and a storage medium;

[0084] The storage medium is used to store instructions;

[0085] The processor is configured to operate according to the instructions to execute the steps of the aforementioned method for generating a substation inspection plan based on necessity assessment.

[0086] Accordingly, this application also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned method for generating substation inspection plans based on necessity assessment.

[0087] The beneficial effects of this invention are that, compared with the prior art, it provides a method and system for generating substation inspection plans based on necessity assessment. This method comprehensively considers factors such as the necessity of various secondary equipment, historical fault conditions, and operating environment, providing inspection personnel with reasonable and efficient inspection plans. By assessing the necessity of secondary equipment inspections, priority is given to substations that have a greater impact on the stable operation of the power system. This allows for the development of optimal inspection plans with limited resources, improving the targeting and efficiency of inspections, achieving quality and efficiency improvements, and helping to promptly identify and resolve potential problems, thus ensuring the safe operation of the power system. Attached Figure Description

[0088] Figure 1 This is a structural diagram of the substation inspection system based on necessity assessment in this invention.

[0089] Figure 2 This is a schematic diagram of the substation inspection indicator system in this invention.

[0090] Figure 3 This is a flowchart of the comprehensive evaluation process for substation inspection indicators in this invention.

[0091] Figure 4 This is a flowchart of the inspection scheme generation and optimization process in this invention. Detailed Implementation

[0092] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0093] The embodiments described in this application are merely some, not all, embodiments of the present invention. Based on the spirit of the present invention, other embodiments obtained by those skilled in the art without inventive effort are all within the protection scope of the present invention.

[0094] To address the shortcomings of existing technologies, this invention proposes a method and system for generating substation inspection plans based on necessity assessment. This method comprehensively considers factors such as the necessity of various secondary equipment, historical fault conditions, and operating environment, providing inspection personnel with reasonable and efficient inspection plans. By assessing the necessity of inspecting secondary equipment, priority is given to substations that have a greater impact on the stable operation of the power system. This allows for the development of optimal inspection plans with limited resources, improving the targeting and efficiency of inspections, achieving quality and efficiency enhancement, and helping to promptly identify and resolve potential problems, thus ensuring the safe operation of the power system.

[0095] The substation inspection plan generation method based on necessity assessment disclosed in this invention includes the following steps:

[0096] Step 1: Collect information on the operational necessity, operational risk, fault, operating status, equipment redundancy, and technology update of each substation to construct a set of substation inspection indicators.

[0097] See Figure 1 The comprehensive data integration module is used to collect, integrate, and process data from various sources, aiming to ensure that the data acquired by the system is comprehensive, accurate, and real-time, providing reliable data support for subsequent analysis and decision-making. Network security measures are implemented, including access encryption, independent deployment, and isolation from the external network to prevent data leakage and unauthorized access. Data privacy regulations and standards are followed when processing operational data. The module compiles relevant rules and regulations for substation inspections as the basis for data collection and indicator formulation and calculation. It collects information such as the commissioning time, historical maintenance records, fault history records, and maintenance response time of each piece of equipment within each substation. It monitors in real time the number of secondary equipment, operating status, existing defects, technical conditions, safety and compliance status, geographical conditions and external environment, and equipment redundancy and backup status within the substation. The collected data is cleaned, removing irrelevant, erroneous, or duplicate information. Data from different sources is merged to ensure data consistency and integrity. Data is converted into a standard format for easy processing and analysis.

[0098] First, preset the equipment importance index and the equipment failure degree index.

[0099] Regarding the equipment importance index, relay protection and AC / DC equipment are more important than automation equipment, and automation equipment is more important than auxiliary equipment. The equipment importance indices ki for the three types of equipment are 3, 2, and 1, respectively, as shown in Table 1.

[0100]

[0101] Table 1

[0102] The equipment failure severity index categorizes equipment failure types into critical, severe, and general failures. The equipment failure severity indices kf for the three types of failures are 3, 2, and 1, respectively, as shown in Table 2.

[0103]

[0104] Table 2

[0105] The operational necessity information comprehensively considers the substation's position and role in the entire power network, including whether it is a major or minor node, the geographical range and number of users supplied by the substation, the total transformer capacity and transmission capacity of the substation, the key users supplied by the substation, the substation's geographical location, the vulnerability of its environment, and other factors. Experts score the necessity of the substation on a scale of 0 to 2, with 2 being the most important.

[0106] The operational risk information is used to assess whether the equipment meets current safety and compliance standards and whether it uses domestically produced chips. Equipment that does not meet the standards may require higher priority for upgrading or replacement. Experts score the safety and compliance of the substation, with each piece of equipment scored from 2 to 0, and the one with the best safety and compliance (i.e., the lowest necessity for inspection) receiving a score of 0.

[0107] The overall security and compliance index of the entire site is calculated by multiplying the security and compliance index of each individual device by the device importance ki, and then summing the indices of all devices.

[0108] Substation Safety and Compliance Index =

[0109] The fault information includes the current fault index and the historical fault index of the equipment.

[0110] The existing fault index is calculated by summing the existing faults and defects of all equipment, multiplying the fault severity index of the existing faulty equipment by the equipment importance index, and then summing the results to obtain the total existing fault index of all secondary equipment in the station.

[0111] Substation Existing Fault Index = ∑ Existing Fault Equipment Fault Severity Index kf × Equipment Importance Index ki

[0112] The historical failure index is used to analyze the historical failure records of equipment, including the number and type of failures within three years. The historical failure history of all equipment in the substation is accumulated, and the failure severity index of the historically failed equipment is calculated by multiplying it by the equipment importance index, and then summing the results to obtain the historical failure score of all secondary equipment in the substation.

[0113] Substation historical fault index = ∑ historical fault equipment fault severity index kf × equipment importance index ki

[0114] The operational status information includes environmental adaptability and equipment lifespan index. Environmental adaptability considers the impact of environmental factors on equipment operation, such as temperature, humidity, and pollution, on equipment performance. The environmental adaptability of the equipment is rated, taking into account the manufacturer's design standards and actual operating conditions.

[0115] The rating scale is from 0 to 2, with 2 indicating poor adaptability and 0 indicating strong adaptability. The formula for calculating the environmental adaptability of a substation is as follows:

[0116] Substation environmental adaptability = .

[0117] The equipment lifespan index takes into account the service life of the equipment. Aging equipment may require more frequent maintenance and earlier replacement. The lifespan index for a single piece of equipment = years used / design life. The formula for calculating the equipment lifespan index is as follows:

[0118] Substation secondary equipment life index = ki

[0119] The equipment redundancy information refers to the equipment redundancy and standby ratio, which is used to assess whether the equipment has redundancy or backup facilities. Equipment with high redundancy has a lower need for inspection.

[0120] Secondary equipment redundancy and standby ratio = 1 - number of redundant and standby devices / total number of devices

[0121] The technology update information is a technology update and obsolescence risk index, used to consider the impact of technological progress on equipment and assess the risk of equipment becoming obsolete. It comprehensively considers determining the current stage of the technology used by the equipment in its life cycle (e.g., introduction, growth, maturity, or decline), assessing the compatibility of existing equipment with emerging technologies (e.g., new software, hardware standards), and comparing the equipment's performance parameters (e.g., efficiency, speed, capacity) with current industry standards or the performance gap of the latest equipment.

[0122] The rating is from 0 to 2, where 2 indicates a high risk of technological obsolescence and 0 indicates a low risk of technological obsolescence.

[0123] Technology update and obsolescence risk index = .

[0124] Step 2: Standardize the substation inspection indicator set, use the BWM analysis method to determine the subjective influence weight of each indicator in the substation inspection indicator set, and use the CRITIC analysis method to determine the objective influence weight of each indicator in the substation inspection indicator set.

[0125] Specifically, step 2 further includes:

[0126] Step 2.1: Establish the original evaluation index matrix X = (x ij ） m×n .in, x ij For the first i The first substation j The measured or scored values ​​of each evaluation indicator. m The number of substations to be evaluated. n To assess the number of indicators, this embodiment... n It is 8.

[0127] The original evaluation index matrix X The elements in the matrix are standardized according to the following formula to obtain the standardized matrix. X* = （x ij * ） m×n ,in:

[0128]

[0129] In the formula: For evaluation indicators j The average value, s j For evaluation indicators j The mean squared error.

[0130]

[0131] Step 2.2: Determine the subjective influence weight of each indicator based on the BWM method.

[0132] The Brown-Warshall Method (BWM) simplifies the data significantly compared to other subjective weighting methods, ensuring the accuracy of the final weighting results. The process of determining the weights for selected indicators based on the BWM method is as follows:

[0133] Step 2.21: Select the most important indicator C from n indicators. B And the least important indicator C w Using a numerical scale of 1 to 9 to compare and display C B For other indicators, and for C, other indicators... w The relative importance is then used to obtain the judgment vector A. B =(a B1 a B2 a B3 , ...a Bn ) and A w =(a 1w a 2w a 3w , ...a nw ) T .

[0134] Among them, a Bi Representing the most important indicator C B Compared to other indicators C i The relative importance of a iw Representing other indicators C i Compared to the least important indicator C w The relative importance of.

[0135] Step 2.22: Perform a preliminary consistency check based on the judgment vector.

[0136] Theoretically, the judgment vector obtained in step 1 should satisfy a Bj ×a jw =a Bw Therefore, a preliminary one-time test can be conducted based on this. If a Bj ×a jwThe result is the same as a. Bw If the values ​​are equal or very similar, the judgment vector passes the consistency test; otherwise, the judgment vector needs to be reconstructed.

[0137] Step 2.23: Calculate the optimal weights by constructing a nonlinear programming model. The objective function and constraints are as follows:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] The optimal weight value is obtained based on the above objective function and constraints. W j Consistency index value ξ * .

[0144] Step 2.24: Calculate the consistency ratio A final, one-time inspection is conducted, in which From Table 3:

[0145]

[0146] Table 3

[0147] Step 2.3 Determine the subjective influence weight of each indicator based on the CRITIC method.

[0148] Subjective weighting methods are significantly influenced by human factors. To reduce the workload of determining the weights of evaluation indicators and increase the objectivity of the weight coefficients, this invention employs the CRITIC method, modified with correlation coefficients, to determine the weight coefficients for each evaluation indicator. The main steps are as follows:

[0149] definition v j Correlation coefficients for each evaluation indicator:

[0150]

[0151] Using the standardized matrix X* Calculate the Pearson correlation coefficient between each evaluation indicator, which is the quotient of the covariance and standard deviation between the indicators, and construct the correlation coefficient matrix. Then from the matrix R Calculate the independence coefficients for each evaluation indicator. ηj :

[0152]

[0153] Calculate the necessity coefficient of each evaluation indicator based on its correlation coefficient and independence coefficient. C j :

[0154]

[0155] The above formula shows C j The higher the value, the better the evaluation indicator. j The more central a position is in the evaluation indicator system, the higher its necessity and the greater its weight should be. Therefore, the calculation of the first... j The weight of each evaluation indicator W j :

[0156]

[0157] Step 3: Based on game theory, combine the subjective influence weights and the objective influence weights to obtain the combined weights of each indicator.

[0158] Drawing on game theory, this paper treats subjective and objective weights as decision-making agents in a non-cooperative game. The two sides seek a balance of interests through continuous conflict, achieving the optimal weight combination, thus making the weighting of indicators more scientific and reasonable. The specific process is as follows:

[0159] The index weights obtained through the BWM method and the improved CRITIC method are denoted as w1 = (w 11 w 12 , ... ,w 1n ) and w2 = (w 21 w 22 , ... , w 2n Then, the initial combination weights are constructed using a linear combination of w1 and w2, as shown in the formula:

[0160]

[0161] In the formula: α1 and α2 are the combination coefficients of subjective and objective weights, respectively.

[0162] Solving for the Nash equilibrium point based on game theory principles involves finding a balance among different weights, minimizing the deviation between the initial weight combination and the subjective and objective weights. The objective function and constraints are as follows:

[0163]

[0164] By solving this model, the optimal combination of weights that comprehensively considers subjective human factors and objective data patterns is obtained. This problem involves finding the minimum value under equality constraints, and a Lagrangian function is constructed, where λ is the penalty factor.

[0165]

[0166] According to the principle of differentiation, the optimal first derivative condition for the above equation is:

[0167]

[0168] The corresponding system of linear equations is

[0169]

[0170] The combination coefficients α1 and α2 are obtained and then normalized.

[0171]

[0172] The final combined weights are

[0173]

[0174] Step 4: Based on the combined weights, the necessity of inspecting multiple substations is comprehensively evaluated using the grey relational analysis method and the TOPSIS decision algorithm to obtain the inspection necessity evaluation results.

[0175] The TOPSIS method is a multi-objective decision-making method based on ranking the proximity of substations to the optimal solution, essentially judging the relative merits of substations. The core of the Grey Relational Analysis (GRA) method is to use grey relational degrees to describe the strength, magnitude, and order of evaluation indicators. The GRA-TOPSIS method can more systematically and accurately reflect the proximity between alternative solutions and the ideal solution, providing a basis for the final decision. The specific steps are as follows:

[0176] Step 4.1: Eliminate the dimensions of the indicator data and perform standardization processing to obtain a standardized matrix. ：

[0177]

[0178] In the formula: R ij For the first i The first substation j Standard values ​​for each indicator; x ij It is the first i The first substation j The original values ​​of each indicator.

[0179] Step 4.2: Combine the n x 1 weight matrix obtained from game theory combinatorial weighting with the standardized matrix. R Multiply to obtain the standardized matrix V, and then calculate the positive ideal solution V. + and the ideal solution V - .

[0180]

[0181] v j + and v j - For the first j The optimal and worst values ​​of each indicator in each substation.

[0182] Step 4.3: Calculate the distance from the substation to the positive ideal solution and the secondary ideal solution:

[0183]

[0184] d i + and d i - The first i Euclidean distances from each substation to the positive and negative ideal solutions.

[0185] Step 4.4: Based on the convergence or divergence of the grey relational analysis method, describe the development trend of each substation through the correlation degree. Calculate the... i The first substation j Grey relational coefficient of each indicator i(j) + , i(j) - .

[0186]

[0187] i Number of substations; j For indicator quantity; The resolution coefficient is set to 0.5, and the maximum value is [value missing]. n、 min n Calculate the maximum and minimum values ​​for dimension n, respectively. m、 min m Calculate the maximum and minimum values ​​for dimension m, respectively.

[0188] Step 4.5: Calculate the correlation degree:

[0189]

[0190] Step 4.6: Calculate the gray distance T i + , T i - and overall relevance C i .

[0191]

[0192]

[0193]

[0194]

[0195] a and b are the evaluators' preference values ​​for the substation location, satisfying a + b = 1. Preferably, a is 0.5 and b is 0.5. The overall proximity score indicates how close the substation is to the optimal value in terms of location and dynamic changes; the higher the overall proximity score, the better the substation.

[0196] The following is a specific example illustrating the method of the present invention. Ten 110kV substations in a certain regional power grid were selected for inspection necessity evaluation. There are 10 substations to be evaluated, and 8 evaluation indicators. The original calculated values ​​of the indicators are as follows:

[0197]

[0198] Table 4

[0199] The subjective weights were calculated using the Brown-Warshall Method (BWM), and the results are as follows:

[0200]

[0201] The objective weights were calculated using the improved CRITIC algorithm, and the results are as follows:

[0202]

[0203] Table 5

[0204] Using game theory to calculate the combination weights, the results are as follows:

[0205]

[0206] Table 6

[0207] The necessity of substation inspection was ranked by using GRA-TOPSIS for comprehensive evaluation.

[0208] Calculate the relative proximity of each substation:

[0209]

[0210] Table 7

[0211] The comprehensive evaluation ranking results of the substations are as follows:

[0212]

[0213] Table 8

[0214] According to the evaluation results, the inspection of substations 4, 5, and 6 is highly necessary and should be prioritized. It is recommended that they be inspected at least once a month to expedite defect handling and equipment upgrades. The inspection of substations 8, 2, 1, and 9 is of moderate necessity and can be considered once a quarter. The inspection of substations 7 and 10 is of low necessity and can be considered once a quarter.

[0215] Step 5: Generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and optimize the inspection plan according to the execution effect of the inspection plan.

[0216] Specifically, the generated inspection plan includes:

[0217] Step 5.1 Determine the inspection type. This includes comprehensive inspections and specialized inspections. Comprehensive inspections are used to inspect all secondary equipment in the substation. Specialized inspections are used to inspect specific aspects of the substation's secondary systems, such as relay protection, automation, and network security equipment, as well as older equipment and fault conditions.

[0218] Step 5.2: Determine the inspection frequency. Conduct a comprehensive evaluation of the necessity of substation inspections quarterly, ranking the substations by necessity. Substations with high necessity should be inspected more frequently. Other factors should also be considered, such as special power supply protection tasks, technical demonstration sites, etc. High necessity (top 30%): inspected at least once a month; Medium necessity (30%-80%): inspected at least once a quarter; Low necessity (bottom 20%): inspected at least once every six months.

[0219] Step 5.3 Calculate the total patrol time requirement. The personnel capability coefficient is quantified based on the personnel's technical level, fluctuating between 0 and 1, where 1 represents the highest capability and 0 represents the lowest. Allocate the required personnel according to the complexity and safety requirements of the patrol mission.

[0220] The formula for calculating the required inspection time for each substation is as follows:

[0221] T 基本 =N 设备 ×T 单个 / (Number of patrol personnel * Capability coefficient) + T交通 +T 额外

[0222] N 设备 =Number of devices within the substation that require inspection. T 单个 =The average time required to inspect a single device, including recording time. T 交通 = Travel time. T 额外 =Including extra time such as rest time.

[0223] The inspection times for all substations are summarized to determine the total inspection time per day, week, or month.

[0224] Step 5.4: Plan the most efficient patrol route based on geographical location to reduce travel time. Consider flexibility for emergencies and ad-hoc tasks.

[0225] Step 5.5: Calculate additional factors, including the impact of weather and seasonal changes on the inspection schedule, such as the possibility that severe weather may require more frequent inspections. Incorporate equipment maintenance and upgrade plans into the inspection schedule to ensure optimal equipment performance.

[0226] Step 5.6: Use database or management software to track inspection records and results. Utilize automation tools and technologies, such as drones and robotic inspections, to improve efficiency.

[0227] Step 5.7: Create a detailed inspection plan, including the inspection date, inspection time, inspection route, and inspection personnel for each substation.

[0228] By following the steps above, an inspection plan can be developed that takes into account the necessity of each substation and effectively allocates time and resources.

[0229] Step 5.8 The effectiveness of the inspection plan includes inspection completion rate, defect detection rate, and inspection coverage rate. Inspection completion rate is used to check the inspection reports submitted by the inspection personnel, including the inspection route, inspected equipment, and problems found. It confirms that all planned inspection points have been covered and all problems have been properly recorded and responded to. The defect detection rate is the ratio of potential or actual defects found during the inspection to the total number of defects. A high defect detection rate indicates that the inspection work has effectively identified and prevented problems. Inspection coverage rate is the ratio of successfully inspected equipment or areas to the total number of planned inspections. A high coverage rate ensures that all critical equipment and substations have been properly inspected.

[0230] Based on the effectiveness of the inspection plan, the entire process of formulating the above inspection plan will be improved and optimized.

[0231] The beneficial effects of this invention are that, compared with the prior art, it provides a method and system for generating substation inspection plans based on necessity assessment. This method comprehensively considers factors such as the necessity of various secondary equipment, historical fault conditions, and operating environment, providing inspection personnel with reasonable and efficient inspection plans. By assessing the necessity of secondary equipment inspections, priority is given to substations that have a greater impact on the stable operation of the power system. This allows for the development of optimal inspection plans with limited resources, improving the targeting and efficiency of inspections, achieving quality and efficiency improvements, and helping to promptly identify and resolve potential problems, thus ensuring the safe operation of the power system.

[0232] This invention can be a system, method, and / or computer program product. This invention also discloses a substation inspection plan generation system based on necessity assessment, which is based on the aforementioned substation inspection plan generation method based on necessity assessment, comprising:

[0233] The comprehensive data integration module is used to collect information on the operational necessity, operational risks, faults, operating status, equipment redundancy, and technology updates of each substation in order to construct a set of substation inspection indicators.

[0234] The inspection necessity assessment index calculation module is used to standardize the substation inspection index set, use the BM analysis method to determine the subjective influence weight of each index in the substation inspection index set, and use the CRITIC analysis method to determine the objective influence weight of each index in the substation inspection index set; and combine the subjective influence weight and the objective influence weight according to game theory to obtain the combined weight of each index.

[0235] The inspection necessity comprehensive evaluation module is used to comprehensively evaluate the inspection necessity of multiple substations based on the combined weights, using the grey relational analysis method and the TOPSIS decision algorithm, and obtain the inspection necessity evaluation result.

[0236] The inspection plan generation and optimization module is used to generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and to optimize the inspection plan based on the execution effect of the inspection plan.

[0237] Based on the spirit of this invention, those skilled in the art will readily conceive of a computer program product derived from the aforementioned method for generating substation inspection plans based on necessity assessment. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded to enable a processor to implement various aspects of this disclosure. That is, this application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned method for generating substation inspection plans based on necessity assessment.

[0238] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0239] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0240] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0241] 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 protection scope of the claims of the present invention.

Claims

1. A method for generating substation inspection plans based on necessity assessment, characterized in that, Includes the following steps: Step 1: Collect operational necessity information, operational risk information, fault information, operating status information, equipment redundancy information, and technology update information for each substation to construct a set of substation inspection indicators. Operational necessity information includes whether the substation is a primary or secondary node, the geographical range and number of users supplied by the substation, the total transformer capacity and transmission capacity of the substation, the key users supplied by the substation, the geographical location of the substation, and the vulnerability of the surrounding environment. Step 2: Standardize the substation inspection indicator set, use the BWM analysis method to determine the subjective influence weight of each indicator in the substation inspection indicator set, and use the CRITIC analysis method to determine the objective influence weight of each indicator in the substation inspection indicator set. Step 3: Based on game theory, combine the subjective influence weights and the objective influence weights to obtain the combined weights for each indicator; Step 4: Based on the combined weights, the necessity of inspecting multiple substations is comprehensively evaluated using the grey relational analysis method and the TOPSIS decision algorithm to obtain the inspection necessity evaluation results; Step 5: Generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and optimize the inspection plan according to the execution effect of the inspection plan; The method of comprehensively evaluating the necessity of inspecting multiple substations using grey relational analysis and the TOPSIS decision algorithm further includes: After eliminating the dimensions of the aforementioned indicators and performing standardization, a standardized matrix A = (υ ij ) m×n ,in: i=1,2,…,m; j=1,2,…,n;υ ij For the first i The first substation j Standard values ​​for each indicator; It is the first i The first substation j The original values ​​of each indicator; Multiply the n x 1 weight matrix obtained by game theory combinatorial weighting by the standardized matrix A to calculate the standardized matrix V, and then calculate the positive ideal solution V. + and the ideal solution V - ; v j + and v j - For the first j The best and worst values ​​of each indicator in each substation; Calculate the distances from the substation to the positive ideal solution and the negative ideal solution: d i + and d i - The first i Euclidean distances from each substation to the positive and secondary ideal solutions; Calculate the first i The first substation j Grey relational coefficient of each indicator i (j) + , i (j) - : The resolution coefficient; max n、 min n Calculate the maximum and minimum values ​​for dimension n, respectively. m、 min m Calculate the maximum and minimum values ​​for dimension m, respectively; Calculate the correlation: Calculate gray distance T i + , T i - and overall relevance C i ; a and b are the evaluators' preference values ​​for the location of the substation, satisfying a+b=1; The overall proximity score indicates how close a substation is to the optimal value in terms of location and dynamic changes. The higher the overall proximity score, the better the substation.

2. The method for generating substation inspection plans based on necessity assessment according to claim 1, characterized in that, The collection of operational necessity information, operational risk information, fault information, operating status information, equipment redundancy information, and technology update information for each substation further includes: Based on the determined operational necessity information, the operational risk information is determined based on the safety and compliance index and the equipment importance index of each device; the fault information is determined based on the equipment failure degree index and the equipment importance index; the operating status information is determined based on the adaptability, lifespan index and equipment importance index of a single device; the equipment redundancy information is determined based on the number of redundant and standby devices and the total number of devices; and the technology update information is determined based on the technology update and obsolescence risk index and the equipment importance index of a single device.

3. The method for generating substation inspection plans based on necessity assessment according to claim 2, characterized in that, The standardization process for the substation inspection indicator set further includes: Establish the original evaluation index matrix ;in, For the first i The first substation j The measured or scored values ​​of each evaluation indicator. m The number of substations to be evaluated. n To assess the number of indicators; The original evaluation index matrix X The elements in the matrix are standardized to obtain the standardized matrix: , For all substations j The average of the evaluation indicators, For all substations j The mean squared error of each evaluation indicator; 。 4. The method for generating substation inspection plans based on necessity assessment according to claim 3, characterized in that, The determination of the objective influence weight of each indicator in the substation inspection indicator set using the CRITIC analysis method further includes: definition Correlation coefficients for each evaluation indicator: ； Using the standardized matrix Calculate the Pearson correlation coefficients among the evaluation indicators and construct a correlation coefficient matrix. Then from the matrix R Calculate the independence coefficients for each evaluation indicator. : Calculate the necessity coefficient of each evaluation indicator based on its correlation coefficient and independence coefficient. C j : Calculate the first j The weight of each evaluation indicator W j : 。 5. The method for generating substation inspection plans based on necessity assessment according to claim 4, characterized in that, The step of combining the subjective influence weight and the objective influence weight according to game theory to obtain the combined weight of each indicator further includes: The index weights obtained by the BWM method and the improved CRITIC method are denoted as w1 = (w 11 w 12 , ... , w 1n ) and w2 = (w 21 w 22 , ... , w 2n Then, the initial combination weights are constructed using a linear combination of w1 and w2: α1 and α2 are the combination coefficients of subjective and objective weights, respectively; The objective function and constraints for minimizing the deviation between the initial combined weights and the subjective and objective weights are as follows: By solving this model, the optimal combined weights are obtained, the minimum value under equality constraints is found, and the Lagrangian function is constructed, where τ is the penalty factor: The optimal first derivative condition is: The corresponding system of linear equations is: The combination coefficients α1 and α2 are obtained and normalized, where i takes the value of 1 or 2: The final combined weights are: 。 6. A substation inspection plan generation system based on necessity assessment, characterized in that, include: The comprehensive data integration module is used to collect information on the operational necessity, operational risks, faults, operating status, equipment redundancy, and technology updates of each substation in order to construct a set of substation inspection indicators. The inspection necessity assessment index calculation module is used to standardize the substation inspection index set, use the BM analysis method to determine the subjective influence weight of each index in the substation inspection index set, and use the CRITIC analysis method to determine the objective influence weight of each index in the substation inspection index set; and combine the subjective influence weight and the objective influence weight according to game theory to obtain the combined weight of each index. The inspection necessity comprehensive evaluation module is used to comprehensively evaluate the inspection necessity of multiple substations based on the combined weights, using the grey relational analysis method and the TOPSIS decision algorithm, and obtain the inspection necessity evaluation result. The inspection plan generation and optimization module is used to generate an inspection plan based on the inspection necessity evaluation results and available inspection resources, and to optimize the inspection plan based on the execution effect of the inspection plan. The method of comprehensively evaluating the necessity of inspecting multiple substations using grey relational analysis and the TOPSIS decision algorithm further includes: After eliminating the dimensions of the aforementioned indicators and performing standardization, a standardized matrix A = (υ ij ) m×n ,in: i=1,2,…,m; j=1,2,…,n;υ ij For the first i The first substation j Standard values ​​for each indicator; It is the first i The first substation j The original values ​​of each indicator; Multiply the n x 1 weight matrix obtained by game theory combinatorial weighting by the standardized matrix A to calculate the standardized matrix V, and then calculate the positive ideal solution V. + and the ideal solution V - ; v j + and v j - For the first j The best and worst values ​​of each indicator in each substation; Calculate the distances from the substation to the positive ideal solution and the negative ideal solution: d i + and d i - The first i Euclidean distances from each substation to the positive and secondary ideal solutions; Calculate the first i The first substation j Grey relational coefficient of each indicator i (j) + , i (j) - : The resolution coefficient; max n、 min n Calculate the maximum and minimum values ​​for dimension n, respectively. m、 min m Calculate the maximum and minimum values ​​for dimension m, respectively; Calculate the correlation: Calculate gray distance T i + , T i - and overall relevance C i ; a and b are the evaluators' preference values ​​for the location of the substation, satisfying a+b=1; The overall proximity score indicates how close a substation is to the optimal value in terms of location and dynamic changes. The higher the overall proximity score, the better the substation.

7. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the substation inspection plan generation method based on necessity assessment according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the substation inspection plan generation method based on necessity assessment as described in any one of claims 1-5.

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