Power enterprise transformer area line loss evaluation method based on improved fuzzy decision theory

Abstract

The invention relates to the technical field of power systems, in particular to a power enterprise transformer area line loss evaluation method based on an improved fuzzy decision theory, and the method comprises the steps: constructing a transformer area line loss evaluation system which comprises a target layer, a criterion layer and an index layer, and calculating the period difference degree based on the evaluation index data of the last period; generating an adaptive membership function to obtain a first membership matrix; and constructing a basic weight matrix, calculating the basic weight matrix and the first membership matrix to obtain a basic transformer area line loss evaluation value, inputting the basic transformer area line loss evaluation value into a transformer area line loss evaluation model, and optimizing the basic weight matrix by using a weight optimization layer of the model through a particle swarm algorithm to obtain a first line loss weight matrix. A comprehensive evaluation coefficient calculation layer of the model is used to calculate a comprehensive transformer area line loss evaluation value according to the first line loss weight matrix and the first membership matrix; and according to the comprehensive line loss evaluation value and a preset threshold value, determining a transformer area line loss level and performing early warning. According to the invention, the accuracy of transformer area line loss evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a method for evaluating line losses in power enterprise substations based on improved fuzzy decision theory. Background Art

[0002] A substation refers to the power supply range or area of ​​one or more transformers in the power system. End users are connected through low-voltage lines in the area to form a power supply network radiating to the surrounding areas. As a terminal connecting users, the line loss problem becomes more prominent; therefore, reasonable substation line loss management is directly related to the economic operation and energy utilization efficiency of the power grid.

[0003] At present, the existing line loss evaluation methods of the substation are mainly based on a single evaluation index, which cannot comprehensively evaluate the comprehensive line loss of the substation. Therefore, it is easy to lead to insufficient evaluation of the line loss of the power company's substation, which directly affects the efficiency optimization of the substation and the overall operation quality of the power grid.

[0004] Therefore, a line loss evaluation method for power enterprises based on improved fuzzy decision theory is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating the line loss of a power enterprise substation based on improved fuzzy decision theory. The method comprises the following steps: constructing a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer, calculating the period difference based on the evaluation indicator data of the previous period, and generating an adaptive membership function to obtain a first membership matrix; constructing a basic weight matrix, and calculating it with the first membership matrix to obtain a basic substation line loss evaluation value, inputting the substation line loss evaluation model, and optimizing the basic weight matrix by using the particle swarm algorithm of the weight optimization layer of the model to obtain a first line loss weight matrix. The comprehensive evaluation coefficient calculation layer of the model is used to calculate the comprehensive substation line loss evaluation value according to the first line loss weight matrix and the first membership matrix; determining the substation line loss level and issuing an early warning according to the comprehensive line loss evaluation value and the preset threshold. The present invention improves the accuracy of substation line loss evaluation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating line loss in a power enterprise area based on improved fuzzy decision theory, the method for evaluating line loss in a power enterprise area based on improved fuzzy decision theory comprising:

[0008] S1. Constructing a line loss evaluation system for the substation area, the evaluation system includes a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria;

[0009] S2. Compare the evaluation index data of the previous period obtained under each index layer with the standard reference data to obtain the period difference of each index, and obtain an adaptive membership function based on the difference according to the period difference and the traditional triangular membership function; and obtain a first membership matrix according to the adaptive membership function;

[0010] S3. Construct a basic weight matrix, obtain a basic area line loss evaluation value according to the first membership matrix and the basic weight matrix, and input the area line loss evaluation model, the area line loss evaluation model includes a preprocessing layer, a weight optimization layer and a comprehensive evaluation coefficient calculation layer, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm; obtain a first line loss weight matrix;

[0011] S4. Analyze the first line loss weight matrix and the first membership matrix according to the comprehensive evaluation coefficient calculation layer of the model to calculate the comprehensive area line loss evaluation value;

[0012] S5. Determine the line loss level of the substation based on the comprehensive substation line loss evaluation value and the preset threshold, and issue a warning in a timely manner based on the warning level.

[0013] Preferably, the indicator layer under the equipment attribute criteria includes equipment update frequency, equipment failure response time and equipment cost; the indicator layer under the management attribute criteria includes the average educational level of managers, the number of managers and the experience index of managers; the indicator layer under the line attribute criteria includes line quality, line design and line wear coefficient; the indicator layer under the load attribute criteria includes average load, maximum load and load fluctuation rate; the indicator layer under the user attribute criteria includes user scale, user type and frequency of illegal electricity use.

[0014] Preferably, managers are divided into levels according to their years of work experience, and experience level weights of each manager are generated to obtain the manager experience index; the user types include residential users, commercial users and industrial users.

[0015] Preferably, the adaptive membership function based on difference is:

[0016]

[0017] Among them, u(i) represents the adaptive membership value of the i-th evaluation index; a new , b new and c new is a set of adjustment parameters for constructing the adaptive membership function; a 0 , b 0 and c 0 is a set of initial parameters for constructing the traditional triangular membership function; αi , β i and γ i are three adjustment parameters; x i represents the i-th evaluation index data of the previous cycle; x i,ref The standard reference data representing the i-th evaluation index.

[0018] Preferably, the generation process of the first membership matrix is:

[0019] According to the adaptive membership function based on the difference, the adaptive membership value of each evaluation index is obtained, and the adaptive membership value of each evaluation index is generated into a matrix according to M rows and N columns to obtain the first membership matrix; wherein M represents the number of principle layers; N represents the number of evaluation indexes of the index layer;

[0020] The first membership matrix is:

[0021]

[0022] Among them, R M*N Represented as the first membership matrix.

[0023] Preferably, the basic station area line loss evaluation value is:

[0024] E base =R M*N T *W base ;

[0025] Among them, E base It is expressed as the line loss evaluation value of the basic substation area; R M*N T W is represented as the transposed matrix of the first membership matrix; base Represented as the basic weight matrix.

[0026] Preferably, the weight optimization layer uses a particle swarm optimization algorithm to optimize the basic weight matrix to obtain a first line loss weight matrix. The specific process includes: initializing a particle swarm, including the position and velocity of the particle, each particle represents a set of weight combinations, and calculating a fitness function based on weight optimization according to the basic substation line loss evaluation value and the actual substation line loss data, and obtaining the global optimal weight combination by continuously iteratively updating the position and velocity of the particles, and obtaining the first line loss weight matrix according to the global optimal weight combination.

[0027] Preferably, the fitness function based on weight optimization is obtained by taking the absolute value of the error between the basic substation line loss evaluation value and the actual line loss value, and the actual line loss value is obtained according to the line loss report of the previous period of the substation.

[0028] Preferably, the comprehensive area line loss evaluation value is:

[0029] E com =R M*N T *W opt ;

[0030] Among them, E com It is expressed as the comprehensive area line loss evaluation value; R M*N T The device matrix represented as the first membership matrix; W opt Represented as the first line loss weight matrix.

[0031] Preferably, the line loss level of the substation is compared with a preset threshold value according to the comprehensive substation line loss evaluation value, the line loss level includes light line loss, medium line loss and heavy line loss, and the preset threshold value includes a first line loss level judgment threshold value and a second line loss level judgment threshold value; the specific calculation process of the line loss level is:

[0032]

[0033] Wherein, ALo represents the comprehensive line loss level; LineLoss low Indicates light line loss; LineLoss intermediate Indicates intermediate line loss; LineLoss high Indicates heavy line loss; E com It is expressed as the comprehensive area line loss evaluation value; Thres 1 Indicates the first line loss level judgment threshold; Thres 2 Indicates the second line loss level judgment threshold.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention constructs a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; each criterion layer includes setting each evaluation indicator, analyzing the difference between each evaluation indicator data and the standard reference data in the previous cycle, and based on the traditional triangular membership function, obtaining the membership value of each evaluation indicator for the substation line loss evaluation target, converting the complex and changeable substation line loss evaluation indicators into fuzzy quantities, thereby quantifying the membership of each evaluation indicator, improving the comprehensiveness and flexibility of the substation line loss evaluation, and further improving the accuracy of the substation line loss evaluation.

[0036] 2. The present invention obtains the first membership matrix of the substation according to the adaptive membership function, and constructs a basic weight matrix to obtain the basic substation line loss evaluation value of the substation, and obtains the optimization objective function according to the basic substation line loss evaluation value and the actual line loss value of the substation, and utilizes the particle swarm optimization algorithm to optimize the optimization objective function to obtain the first line loss weight matrix. Through the first line loss evaluation matrix, a good foundation is laid for realizing the evaluation of substation line loss, and the accuracy of substation line loss evaluation is further improved.

[0037] 3. The present invention calculates the first line loss weight matrix and the first membership matrix through optimization to obtain a comprehensive substation line loss evaluation value, thereby improving the comprehensiveness of the substation line loss evaluation. The comprehensive evaluation value is compared with a preset threshold to obtain the line loss level of the substation, and timely warnings are issued according to the line loss level, thereby improving the management efficiency of the substation line loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of a method for evaluating line loss in a power enterprise area based on improved fuzzy decision theory provided by an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of a substation line loss evaluation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1

[0042] In order to improve the accuracy of line loss evaluation, a line loss evaluation method for power enterprises based on improved fuzzy decision theory was applied in A station area.

[0043] Reference Figure 1 , which is a flow chart of a line loss evaluation method for power enterprises based on improved fuzzy decision theory, including:

[0044] S1. Constructing a line loss evaluation system for the substation area, the evaluation system includes a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria;

[0045] Reference Figure 2, which is a schematic diagram of a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer, wherein the target layer is the substation line loss evaluation; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; the indicator layer under the equipment attribute criteria includes equipment update frequency, equipment failure response time and equipment cost; the indicator layer under the management attribute criteria includes the average education level of managers, the number of managers and the experience index of managers; the indicator layer under the line attribute criteria includes line quality, line design and line wear coefficient; the indicator layer under the load attribute criteria includes average load, maximum load and load fluctuation rate; the indicator layer under the user attribute criteria includes user scale, user type and frequency of illegal electricity use.

[0046] Furthermore, the equipment update frequency is obtained by providing the equipment update record report of the A substation; the equipment fault response time automatically collects the start and end time of each fault in the A substation and calculates the response time. The equipment cost is obtained through the equipment procurement record of the A substation; the average educational level of the managers is obtained by dividing the different educational levels into grades, and the quotient of the sum of the grades and the number of people; the managers are divided into grades according to their years of work, and the experience level weight of each manager is generated to obtain the manager experience index; the line quality can detect the corrosion of the line through the remote monitoring equipment installed on the line, and obtain the non The corroded line segment is obtained by dividing the non-corroded line segment by the total line segment to obtain the line quality; the line design is analyzed by the line design experts of the power enterprise to obtain the qualified rate of the substation line design; the line wear coefficient is obtained by subtracting the line quality value from 1; the average load, maximum load and load fluctuation rate are obtained through the power load report of the A substation; the user scale is obtained through the number of power users in the A substation; the user types include residential users, commercial users and industrial users; different type weight values ​​are assigned to each type; the frequency of illegal electricity use behavior is obtained by dividing the number of illegal electricity use behaviors of users during the monitoring period by the period time;

[0047] S2. Compare the evaluation index data of the previous period obtained under each index layer with the standard reference data to obtain the period difference of each index, and obtain an adaptive membership function based on the difference according to the period difference and the traditional triangular membership function; and obtain a first membership matrix according to the adaptive membership function;

[0048] S3. Construct a basic weight matrix, obtain a basic area line loss evaluation value according to the first membership matrix and the basic weight matrix, and input the area line loss evaluation model, the area line loss evaluation model includes a preprocessing layer, a weight optimization layer and a comprehensive evaluation coefficient calculation layer, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm; obtain a first line loss weight matrix;

[0049] S4. Analyze the first line loss weight matrix and the first membership matrix according to the comprehensive evaluation coefficient calculation layer of the model to calculate the comprehensive area line loss evaluation value;

[0050] S5. Determine the comprehensive line loss level of the substation based on the comprehensive substation line loss evaluation value and the preset threshold, and issue a warning in a timely manner based on the warning level.

[0051] The traditional triangular membership function is:

[0052]

[0053] Wherein, u(x) represents the traditional triangular membership function; a, b and c are a set of initial parameters for constructing the traditional triangular membership function; α i , β i and γ i There are three adjustment parameters;

[0054] Furthermore, the adaptive membership function based on the difference is:

[0055]

[0056] Among them, u(i) represents the adaptive membership value of the i-th evaluation index; a new , b new and c new is a set of adjustment parameters for constructing the adaptive membership function; a 0 , b 0 and c 0 is a set of initial parameters for constructing the traditional triangular membership function; α i , β i and γ i are three adjustment parameters; x i represents the i-th evaluation index data of the previous cycle; x i,ref The standard reference data representing the i-th evaluation index.

[0057] Furthermore, the generation process of the first membership matrix is:

[0058] According to the adaptive membership function based on the difference, the adaptive membership value of each evaluation index is obtained, and the adaptive membership value of each evaluation index is generated into a matrix according to M rows and N columns to obtain the first membership matrix; wherein M represents the number of principle layers; N represents the number of evaluation indexes of the index layer;

[0059] The first membership matrix is:

[0060]

[0061] Among them, R M*N Represented as the first membership matrix;

[0062] This embodiment constructs a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; each criterion layer includes setting each evaluation indicator, analyzing the difference between each evaluation indicator data and the standard reference data in the previous cycle, and based on the traditional triangular membership function, obtaining the membership value of each evaluation indicator to the substation line loss evaluation target, and converting the complex and changeable substation line loss evaluation indicators into fuzzy quantities, thereby quantifying the membership of each evaluation indicator, improving the comprehensiveness and flexibility of the substation line loss evaluation, and further improving the accuracy of the substation line loss evaluation.

[0063] Furthermore, the basic substation line loss evaluation value is:

[0064] E base =R M*N T *W base ;

[0065] Among them, E base It is expressed as the line loss evaluation value of the basic substation area; R M*N T W is represented as the transposed matrix of the first membership matrix; base Represented as the basic weight matrix;

[0066] The basic weight matrix is:

[0067]

[0068] Among them, W base Represented as the basic weight matrix; w 1 、w 2 、w 3 、w 4 and w 5 Five weight parameters for constructing the basic weight matrix;

[0069] Furthermore, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm to obtain a first line loss weight matrix. The specific process includes: initializing a particle swarm, including the position and velocity of a particle, each particle representing a set of weight combinations, calculating a fitness function based on weight optimization according to the basic substation line loss evaluation value and the actual line loss data of the substation, obtaining a global optimal weight combination by continuously iteratively updating the position and velocity of the particle, and obtaining the first line loss weight matrix according to the global optimal weight combination;

[0070] The fitness function is:

[0071] F=|E base -E actual |;

[0072] Where F represents the fitness function; E base It is represented by the line loss evaluation value of the basic substation area; E actual It is expressed as the actual line loss value;

[0073] Furthermore, the fitness function based on weight optimization is obtained by taking the absolute value of the error between the basic substation line loss evaluation value and the actual line loss value, and the actual line loss value is obtained according to the line loss report of the previous period in the A substation.

[0074] This embodiment obtains the first membership matrix of the substation according to the adaptive membership function, and constructs a basic weight matrix to obtain the basic substation line loss evaluation value of the substation, and obtains the optimization objective function according to the basic substation line loss evaluation value and the actual line loss value of the substation, and uses the particle swarm optimization algorithm to optimize the optimization objective function to obtain the first line loss weight matrix. The first line loss evaluation matrix lays a good foundation for realizing the evaluation of substation line loss and further improves the accuracy of substation line loss evaluation.

[0075] Furthermore, the comprehensive area line loss evaluation value is:

[0076] E com =R M*N T *W opt ;

[0077] Among them, E com It is expressed as the comprehensive area line loss evaluation value; R M*N T The device matrix represented as the first membership matrix; W opt Represented as the first line loss weight matrix;

[0078] Furthermore, the line loss level of the substation is compared with a preset threshold value according to the comprehensive substation line loss evaluation value, the line loss level includes light line loss, medium line loss and heavy line loss, and the preset threshold value includes a first line loss level judgment threshold value and a second line loss level judgment threshold value; the specific calculation process of the line loss level is:

[0079]

[0080] Wherein, ALo represents the comprehensive line loss level; LineLoss low Indicates light line loss; LineLoss intermediate Indicates intermediate line loss; LineLoss high Indicates heavy line loss; E com It is expressed as the comprehensive area line loss evaluation value; Thres 1 Indicates the first line loss level judgment threshold; Thres 2 Indicates the second line loss level judgment threshold;

[0081] In this embodiment, when the comprehensive line loss level of the A substation area is severe, an early warning is issued.

[0082] This embodiment calculates the first line loss weight matrix and the first membership matrix through optimization to obtain a comprehensive substation line loss evaluation value, thereby improving the comprehensiveness of the substation line loss evaluation. The comprehensive evaluation value is compared with the preset threshold to obtain the line loss level of the substation, and timely warnings are issued according to the line loss level, thereby improving the management efficiency of the substation line loss.

[0083] Embodiment 2

[0084] In order to improve the accuracy of line loss evaluation, a line loss evaluation method for power enterprise areas based on improved fuzzy decision theory was applied in Area B.

[0085] Reference Figure 1 , which is a flow chart of a line loss evaluation method for power enterprises based on improved fuzzy decision theory, including:

[0086] S1. Constructing a line loss evaluation system for the substation area, the evaluation system includes a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria;

[0087] Reference Figure 2, which is a schematic diagram of a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer, wherein the target layer is the substation line loss evaluation; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; the indicator layer under the equipment attribute criteria includes equipment update frequency, equipment failure response time and equipment cost; the indicator layer under the management attribute criteria includes the average education level of managers, the number of managers and the experience index of managers; the indicator layer under the line attribute criteria includes line quality, line design and line wear coefficient; the indicator layer under the load attribute criteria includes average load, maximum load and load fluctuation rate; the indicator layer under the user attribute criteria includes user scale, user type and frequency of illegal electricity use.

[0088] Furthermore, the equipment update frequency is obtained by providing the equipment update record report of the B station area; the equipment fault response time automatically collects the start and end time of each fault in the B station area and calculates the response time. The equipment cost is obtained through the equipment procurement record of the B station area; the average educational level of the managers is obtained by dividing the different educational levels into grades, and the quotient of the sum of the grades and the number of people; the managers are divided into grades according to their years of work, and the experience grade weight of each manager is generated to obtain the manager experience index; the line quality can detect the corrosion of the line through the remote monitoring equipment installed on the line, and obtain the non The corroded line segment is obtained by dividing the non-corroded line segment by the total line segment to obtain the line quality; the line design is analyzed by the line design experts of the power enterprise to obtain the qualified rate of the line design in the substation area; the line wear coefficient is obtained by subtracting the line quality value from 1; the average load, maximum load and load fluctuation rate are obtained through the power load report of the B substation area; the user scale is obtained through the number of power users in the B substation area; the user types include residential users, commercial users and industrial users; different type weight values ​​are assigned to each type; the frequency of illegal electricity use behavior is obtained by dividing the number of illegal electricity use behaviors of users during the monitoring period by the period time;

[0089] S2. Compare the evaluation index data of the previous period obtained under each index layer with the standard reference data to obtain the period difference of each index, and obtain an adaptive membership function based on the difference according to the period difference and the traditional triangular membership function; and obtain a first membership matrix according to the adaptive membership function;

[0090] S3. Construct a basic weight matrix, obtain a basic area line loss evaluation value according to the first membership matrix and the basic weight matrix, and input the area line loss evaluation model, the area line loss evaluation model includes a preprocessing layer, a weight optimization layer and a comprehensive evaluation coefficient calculation layer, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm; obtain a first line loss weight matrix;

[0091] S4. Analyze the first line loss weight matrix and the first membership matrix according to the comprehensive evaluation coefficient calculation layer of the model to calculate the comprehensive area line loss evaluation value;

[0092] S5. Determine the comprehensive line loss level of the substation based on the comprehensive substation line loss evaluation value and the preset threshold, and issue a warning in a timely manner based on the warning level.

[0093] The traditional triangular membership function is:

[0094]

[0095] Wherein, u(x) represents the traditional triangular membership function; a, b and c are a set of initial parameters for constructing the traditional triangular membership function; α i , β i and γ i There are three adjustment parameters;

[0096] Furthermore, the adaptive membership function based on the difference is:

[0097]

[0098] Among them, u(i) represents the adaptive membership value of the i-th evaluation index; a new 、b new and c new is a set of adjustment parameters for constructing the adaptive membership function; a 0 、b 0 and c 0 is a set of initial parameters for constructing the traditional triangular membership function; α i , β i and γ i are three adjustment parameters; x i represents the i-th evaluation index data of the previous cycle; x i,ref The standard reference data representing the i-th evaluation index.

[0099] Furthermore, the generation process of the first membership matrix is:

[0100] According to the adaptive membership function based on the difference, the adaptive membership value of each evaluation index is obtained, and the adaptive membership value of each evaluation index is generated into a matrix according to M rows and N columns to obtain the first membership matrix; wherein M represents the number of principle layers; N represents the number of evaluation indexes of the index layer;

[0101] The first membership matrix is:

[0102]

[0103] Among them, R M*N Represented as the first membership matrix;

[0104] This embodiment constructs a substation line loss evaluation system, including a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; each criterion layer includes setting each evaluation indicator, analyzing the difference between each evaluation indicator data and the standard reference data in the previous cycle, and based on the traditional triangular membership function, obtaining the membership value of each evaluation indicator to the substation line loss evaluation target, and converting the complex and changeable substation line loss evaluation indicators into fuzzy quantities, thereby quantifying the membership of each evaluation indicator, improving the comprehensiveness and flexibility of the substation line loss evaluation, and further improving the accuracy of the substation line loss evaluation.

[0105] Furthermore, the basic substation line loss evaluation value is:

[0106] E base =R M*N T *W base ;

[0107] Among them, E base It is expressed as the line loss evaluation value of the basic substation area; R M*N T W is represented as the transposed matrix of the first membership matrix; base Represented as the basic weight matrix;

[0108] The basic weight matrix is:

[0109]

[0110] Among them, W base Represented as the basic weight matrix; w 1 、w 2 、w 3 、w 4 and w 5 Five weight parameters for constructing the basic weight matrix;

[0111] Furthermore, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm to obtain a first line loss weight matrix. The specific process includes: initializing a particle swarm, including the position and velocity of a particle, each particle representing a set of weight combinations, calculating a fitness function based on weight optimization according to the basic substation line loss evaluation value and the actual line loss data of the substation, obtaining a global optimal weight combination by continuously iteratively updating the position and velocity of the particle, and obtaining the first line loss weight matrix according to the global optimal weight combination;

[0112] The fitness function is:

[0113] F=|E base -E actual |;

[0114] Where F represents the fitness function; E base It is represented by the line loss evaluation value of the basic substation area; E actual It is expressed as the actual line loss value;

[0115] Furthermore, the fitness function based on weight optimization is obtained by taking the absolute value of the error between the basic substation line loss evaluation value and the actual line loss value, and the actual line loss value is obtained according to the line loss report of the previous period in the B substation.

[0116] This embodiment obtains the first membership matrix of the substation according to the adaptive membership function, and constructs a basic weight matrix to obtain the basic substation line loss evaluation value of the substation, and obtains the optimization objective function according to the basic substation line loss evaluation value and the actual line loss value of the substation, and uses the particle swarm optimization algorithm to optimize the optimization objective function to obtain the first line loss weight matrix. The first line loss evaluation matrix lays a good foundation for realizing the evaluation of substation line loss and further improves the accuracy of substation line loss evaluation.

[0117] Furthermore, the comprehensive area line loss evaluation value is:

[0118] E com =R M*N T *W opt ;

[0119] Among them, E com It is expressed as the comprehensive area line loss evaluation value; R M*N T The device matrix represented as the first membership matrix; W opt Represented as the first line loss weight matrix;

[0120] Furthermore, the line loss level of the substation is compared with a preset threshold value according to the comprehensive substation line loss evaluation value, the line loss level includes light line loss, medium line loss and heavy line loss, and the preset threshold value includes a first line loss level judgment threshold value and a second line loss level judgment threshold value; the specific calculation process of the line loss level is:

[0121]

[0122] Wherein, ALo represents the comprehensive line loss level; LineLoss low Indicates light line loss; LineLoss intermediate Indicates intermediate line loss; LineLoss high Indicates heavy line loss; E com It is expressed as the comprehensive area line loss evaluation value; Thres 1 Indicates the first line loss level judgment threshold; Thres 2 Indicates the second line loss level judgment threshold;

[0123] In this embodiment, when the comprehensive line loss level of the B station area is severe, an early warning is issued.

[0124] This embodiment calculates the first line loss weight matrix and the first membership matrix through optimization to obtain a comprehensive substation line loss evaluation value, thereby improving the comprehensiveness of the substation line loss evaluation. The comprehensive evaluation value is compared with the preset threshold to obtain the line loss level of the substation, and timely warnings are issued according to the line loss level, thereby improving the management efficiency of the substation line loss.

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory, characterized in that: The power enterprise area line loss evaluation method based on improved fuzzy decision theory includes: S1. Constructing a line loss evaluation system for the substation area, the evaluation system includes a target layer, a criterion layer and an indicator layer; the criterion layer includes equipment attribute criteria, management attribute criteria, line attribute criteria, load attribute criteria and user attribute criteria; S2. Compare the evaluation index data of the previous period obtained under each index layer with the standard reference data to obtain the period difference of each index, and obtain an adaptive membership function based on the difference according to the period difference and the traditional triangular membership function; and obtain a first membership matrix according to the adaptive membership function; S3. Construct a basic weight matrix, obtain a basic area line loss evaluation value according to the first membership matrix and the basic weight matrix, and input the area line loss evaluation model, the area line loss evaluation model includes a preprocessing layer, a weight optimization layer and a comprehensive evaluation coefficient calculation layer, the weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm; obtain a first line loss weight matrix; S4. Analyze the first line loss weight matrix and the first membership matrix according to the comprehensive evaluation coefficient calculation layer of the model to calculate the comprehensive area line loss evaluation value; S5. Determine the comprehensive line loss level of the substation based on the comprehensive substation line loss evaluation value and the preset threshold, and issue a warning in a timely manner based on the warning level.

2. According to the method for evaluating line loss in power enterprise area based on improved fuzzy decision theory in claim 1, it is characterized in that: The index layer under the equipment attribute criteria includes equipment update frequency, equipment failure response time, and equipment cost; the index layer under the management attribute criteria includes the average education level of managers, the number of managers, and the management experience index; The indicator layer under the line attribute criteria includes line quality, line design and line wear coefficient; the indicator layer under the load attribute criteria includes average load, maximum load and load fluctuation rate; The indicator layer under the user attribute criteria includes user scale, user type and frequency of illegal electricity usage behavior.

3. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 2 is characterized in that: The managers are divided into different levels according to their years of experience, and the experience level weight of each manager is generated to obtain the manager experience index; the user types include residential users, commercial users and industrial users.

4. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1 is characterized in that: The adaptive membership function based on difference is: Among them, u(i) represents the adaptive membership value of the i-th evaluation index; a new 、b new and c new is a set of adjustment parameters for constructing the adaptive membership function; a0, b0 and c0 are a set of initial parameters for constructing the traditional triangular membership function; α i , β i and γ i are three adjustment parameters; x i represents the i-th evaluation index data of the previous cycle; x i,ref The standard reference data representing the i-th evaluation index.

5. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1 is characterized in that: The generation process of the first membership matrix is: According to the adaptive membership function based on the difference, the adaptive membership value of each evaluation index is obtained, and the adaptive membership value of each evaluation index is generated into a matrix according to M rows and N columns to obtain the first membership matrix; wherein M represents the number of principle layers; N represents the number of evaluation indexes of the index layer; The first membership matrix is: Among them, R M*N Represented as the first membership matrix.

6. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1 is characterized in that: The line loss evaluation value of the basic station area is: E base ZR M*N T *W base 100. Among them, E base It is expressed as the line loss evaluation value of the basic substation area; R M*N T W is represented as the transposed matrix of the first membership matrix; base Represented as the basic weight matrix.

7. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1 is characterized in that: The weight optimization layer optimizes the basic weight matrix using a particle swarm optimization algorithm to obtain a first line loss weight matrix. The specific process includes: initializing a particle swarm, including the position and velocity of the particles, each particle representing a set of weight combinations, calculating a fitness function based on weight optimization according to the basic substation line loss evaluation value and the actual substation line loss data, and obtaining a global optimal weight combination by continuously iteratively updating the position and velocity of the particles. Based on the global optimal weight combination, the first line loss weight matrix is ​​obtained.

8. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 7 is characterized in that: The fitness function based on weight optimization is obtained by taking the absolute value of the error between the basic substation line loss evaluation value and the actual line loss value, and the actual line loss value is obtained according to the line loss report of the previous period of the substation.

9. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1, characterized in that: The comprehensive area line loss evaluation value is: E com ZR M*N T *W opt 100. Among them, E com It is expressed as the comprehensive area line loss evaluation value; R M*N T The device matrix represented as the first membership matrix; W opt Represented as the first line loss weight matrix.

10. The method for evaluating line loss in power enterprise areas based on improved fuzzy decision theory according to claim 1, characterized in that: The comprehensive line loss level of the substation is compared with the preset threshold value according to the comprehensive substation line loss evaluation value. The line loss level includes light line loss, medium line loss and heavy line loss. The preset threshold value includes a first line loss level judgment threshold value and a second line loss level judgment threshold value. The specific calculation process of the line loss level is as follows: Wherein, ALo represents the comprehensive line loss level; LineLoss low Indicates light line loss; LineLoss intermediate Indicates intermediate line loss; LineLoss high Indicates heavy line loss; E com It is represented as the comprehensive substation line loss evaluation value; Thres1 represents the first line loss level judgment threshold; Thres2 represents the second line loss level judgment threshold.

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