Substation line loss evaluation method, device and equipment based on improved fuzzy decision theory

Abstract

The present invention discloses a method, device and equipment for evaluating line loss in a substation based on improved fuzzy decision theory, which relates to the field of multi-attribute decision-making in power systems. The method includes collecting corresponding data according to the evaluation principle of substation line loss in power enterprises and constructing a substation line loss evaluation index system for substation line loss management in power enterprises based on multiple dimensions; characterizing the uncertainty variables in the substation line loss evaluation index system based on triangular fuzzy numbers integrating interval factors; calculating the weights of substation line loss evaluation indicators in the substation line loss evaluation index system through an index weight perception algorithm; constructing a substation line loss evaluation model based on improved fuzzy decision theory based on the characterized uncertainty variables and the calculated weights, so as to realize the perception and evaluation of the substation line loss management situation of power enterprises. The present application can comprehensively construct a substation line loss evaluation index system.

Description

Technical Field

[0001] The present application relates to the field of multi-attribute decision-making in power systems, and specifically to a method, device and equipment for evaluating line loss in a substation based on improved fuzzy decision-making theory. Background Art

[0002] In recent years, with the continuous advancement of energy transformation, the access of massive renewable energy sources to the power grid and the continuous popularization of electric vehicles have brought severe challenges to the management of line losses in power companies. As the terminal connecting the power grid to users, the power grid is a key link in ensuring the consumption of clean energy and continuously promoting electric energy substitution. The evaluation of line losses in the power grid is an objective evaluation of the effect of line loss management in the power grid, which helps companies understand the operation quality and economic benefits of their power grids and provides a basis for formulating reasonable management strategies. Through the evaluation, the problem of line loss in the power grid can be discovered and solved in a timely manner, the power supply reliability and energy utilization efficiency of the power grid can be improved, and energy waste and environmental pollution can be reduced. At the same time, the evaluation results can also be used as a reference for internal management and external supervision of power companies to promote standardized operations and continuous improvement of companies. Therefore, the evaluation of line loss management in the power grid is of great significance to the sustainable development and overall competitiveness of power companies.

[0003] At present, in the process of evaluating line losses in power companies, the following problems mainly exist: First, there is a lack of a comprehensive and effective evaluation system to objectively reflect the management of line losses in power companies. Secondly, the management of line losses in the substation is related to many factors, involving deterministic factors and uncertain factors. In the current information acquisition process, it is difficult to accurately characterize uncertain information. Finally, the evaluation of substation line losses involves the influence of multi-dimensional factors, and traditional deterministic evaluation methods are difficult to effectively characterize them. It can be seen that how to achieve accurate perception of substation line losses in a fuzzy environment is a problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device and equipment for evaluating line loss in a substation based on improved fuzzy decision theory, which can comprehensively construct an evaluation index system for line loss in a substation.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating line loss in a transformer area based on an improved fuzzy decision theory, and the method for evaluating line loss in a transformer area based on an improved fuzzy decision theory includes:

[0006] According to the line loss evaluation principle of power enterprises, a line loss evaluation index system for power enterprises is constructed based on multiple dimensions to collect corresponding data;

[0007] Based on the triangular fuzzy number integrating interval factors, the uncertainty variables in the line loss evaluation index system of the substation are characterized.

[0008] The weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated through the index weight perception algorithm;

[0009] Based on the characterized uncertainty variables and calculated weights, a substation line loss evaluation model based on improved fuzzy decision-making theory is constructed to realize the perception and evaluation of the power company's substation line loss management situation.

[0010] In combination with the first aspect, in one implementation,

[0011] The substation line loss evaluation index system includes primary indicators and secondary indicators corresponding to the primary indicators;

[0012] The first-level indicators include personnel situation, equipment situation, load situation and management situation;

[0013] The secondary indicators corresponding to the personnel situation include the average education level of the personnel in charge of the substation, the number of persons in charge of the substation and the population covered by the substation;

[0014] The secondary indicators corresponding to the equipment conditions include the equipment purchase cost of the substation area, the equipment update frequency of the substation area, and the advanced level of the metering equipment in the substation area;

[0015] The secondary indicators corresponding to the load conditions include the annual average load of the substation area and the maximum load of the substation area;

[0016] The secondary indicators corresponding to the management situation include the maintenance interval of the substation area and the annual failure frequency of the substation area equipment.

[0017] In combination with the first aspect, in one implementation,

[0018] By integrating the interval theory with the traditional triangular fuzzy number, we can obtain the triangular fuzzy number based on the fusion interval factor, namely the new triangular fuzzy number, where the new triangular fuzzy number is specifically:

[0019]

[0020] Among them, μ A (x) represents the new triangular fuzzy number, x represents the variable, a1 and a2 represent the left endpoints of the triangular fuzzy number, c1 and c2 represent the right endpoints of the triangular fuzzy number, b represents the horizontal coordinate corresponding to the vertex of the triangular fuzzy number, μ A1 , μ A2 Represents a constant;

[0021] The clarification method of the new triangular fuzzy number is expressed as:

[0022]

[0023] Among them, f μ The clarity method for representing the new triangular fuzzy number, μ a1represents the horizontal coordinate of a1, μ a2 represents the horizontal coordinate of a2, μ c1 represents the horizontal coordinate of c1, μ c2 Represents the horizontal coordinate of c2.

[0024] In combination with the first aspect, in one implementation, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the subjective weight of the index is calculated by the G1 method, specifically:

[0025] Select an important indicator X1 from the n indicators of the area line loss evaluation index, and record the corresponding indicator weight as α1. Select an important indicator X2 from the remaining n-1 indicators, and record the corresponding indicator weight as α2. This is repeated until all indicators are selected, and the indicator sequence (X1, X2, ..., X2) ranked by relative importance is obtained. n ), and the corresponding indicator weights (α1, α2, ..., α n );

[0026] Calculate the relative importance ratio of indicators, specifically:

[0027]

[0028] Among them, r j Represents the relative importance ratio of the jth indicator, α j represents the indicator weight of the jth indicator;

[0029] The calculation of the nth indicator weight is as follows:

[0030]

[0031] Among them, α n represents the weight of the nth indicator, and k represents the intermediate amount;

[0032] According to the relative importance ratio of the indicators, the weights of the other n-1 indicators are calculated, specifically:

[0033] α j-1 =r j α j

[0034] Among them, α j-1 Represents the weight of the j-1th indicator.

[0035] Setting α pq is the weight coefficient of the qth index under the pth criterion layer in the overall target layer in the area line loss evaluation index system, α p is the weight coefficient of the pth criterion layer in the total target layer, α (p)q is the weight coefficient of the qth indicator in the pth criterion layer, then we get:

[0036] α pq =α p ×α (p) q .

[0037] In combination with the first aspect, in one implementation, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the objective weight of the index is calculated by the grey correlation method. For the grey correlation method, the specific calculation steps include:

[0038] Determine the system main behavior reference sequence X0(k) and the comparison sequence X0(k) of system related factors i (k);

[0039] Compare the sequence X i The data in (k) are normalized to convert the data into a pure digital sequence;

[0040] Calculate the correlation coefficient, specifically:

[0041]

[0042] Among them, ξ 0i represents the correlation coefficient of the ith indicator in the area line loss evaluation index, ρ represents the resolution coefficient, and its value range is [0,1];

[0043] Calculate the correlation, specifically:

[0044]

[0045] Among them, γ(X0,X i ) represents the correlation between the reference sequence and the comparison sequence, and n represents the total number of indicators of the line loss evaluation index of the substation area;

[0046] The calculated correlation degrees are sorted according to size to reflect the strength of the relationship between the comparison sequence and the reference sequence.

[0047] In combination with the first aspect, in one implementation, the calculated subjective weight results and objective weight results of the indicators are integrated to obtain the comprehensive weights of the indicators in the substation line loss evaluation index.

[0048] In combination with the first aspect, in one implementation,

[0049] Before constructing the line loss evaluation model of the substation area, it also includes: pre-processing the indicators;

[0050] The specific steps of the pretreatment include:

[0051] Assume that there are n indicators (X1, X2, ..., X n ), there are m groups of observations x ij (where i = 1, 2, ..., m; j = 1, 2, ..., n), if X n If is the minimization index, then the maximization process is performed to obtain If X n is the maximization index, then the maximization process is performed to obtain x ij ′=x ij , where x ij ′ represents the ij The value obtained after maximization processing, d represents a constant;

[0052] Based on the standardized method, x ij 'Standardization processing, specifically:

[0053]

[0054] Among them, m j Indicates the minimum data in the indicator, M j Indicates the maximum data in the indicator.

[0055] In combination with the first aspect, in one implementation, the step of evaluating the line loss management situation of the power enterprise's area by the area line loss evaluation model includes:

[0056] Obtain the evaluation data corresponding to the indicators in the substation line loss evaluation system;

[0057] For uncertainty indicators, triangular fuzzy numbers based on fusion interval factors are used to characterize uncertainty indicators;

[0058] Based on the specific steps of the preprocessing, the indicators are preprocessed to construct a fuzzy decision matrix;

[0059] The fuzzy decision matrix is ​​calculated based on the fuzzy comprehensive evaluation method to obtain the evaluation result.

[0060] In a second aspect, an embodiment of the present application provides a station area line loss evaluation device based on an improved fuzzy decision theory, and the station area line loss evaluation device based on the improved fuzzy decision theory includes:

[0061] A construction module is used to construct a line loss evaluation index system for power enterprises based on the line loss evaluation principle of power enterprises and based on multiple dimensions to collect corresponding data;

[0062] A characterization module is used to characterize the uncertainty variables in the line loss evaluation index system of the substation based on the triangular fuzzy number of the fusion interval factor;

[0063] A calculation module, which is used to calculate the weight of the substation line loss evaluation index in the substation line loss evaluation index system through an index weight perception algorithm;

[0064] The execution module is used to construct a substation line loss evaluation model based on the improved fuzzy decision theory based on the characterized uncertainty variables and calculated weights, so as to realize the perception and evaluation of the substation line loss management situation of the power enterprise.

[0065] In a third aspect, an embodiment of the present application provides a substation line loss evaluation device based on improved fuzzy decision theory, wherein the substation line loss evaluation device based on improved fuzzy decision theory comprises a processor, a memory, and a substation line loss evaluation program based on improved fuzzy decision theory stored in the memory and executable by the processor, wherein when the substation line loss evaluation program based on improved fuzzy decision theory is executed by the processor, the steps of the substation line loss evaluation method based on improved fuzzy decision theory described above are implemented.

[0066] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0067] (1) For the evaluation of line loss in the substation area, a comprehensive line loss evaluation index system is constructed from the aspects of personnel, load, equipment, and management;

[0068] (2) The definition and calculation method of the indicator are clear, and the data required for calculation are available and can be processed and calculated, which conforms to the construction principles of the indicator system;

[0069] (3) The combined weighting method based on grey correlation analysis combines subjective weights and objective weights, fully combining the advantages of both in qualitative and quantitative aspects. While absorbing the advantages of the two methods, it can overcome their respective shortcomings, achieve complementary advantages, and obtain more reasonable, scientific, and objective evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flowchart of a method for evaluating line loss in a transformer area based on improved fuzzy decision theory is provided for this application;

[0071] Figure 2 This is a schematic diagram of the new triangular fuzzy number for this application;

[0072] Figure 3 This is a schematic diagram of the structure of a substation line loss evaluation device based on improved fuzzy decision theory in this application;

[0073] Figure 4 This is a schematic diagram of the hardware structure of the substation line loss evaluation equipment based on improved fuzzy decision theory in this application. DETAILED DESCRIPTION

[0074] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0076] On the first aspect, the embodiment of the present application provides a substation line loss evaluation method based on improved fuzzy decision-making theory, involving a multi-dimensional and comprehensive power grid substation management evaluation system, an uncertainty information characterization method, an indicator weight calculation method and a fuzzy decision-making model, thereby achieving effective evaluation of power enterprise substation line losses and effective perception of the power enterprise substation line loss management effect, providing effective support for the safe and low-carbon development of enterprises.

[0077] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the method for evaluating the line loss in the transformer area based on the improved fuzzy decision theory in this application. Figure 1 As shown in the figure, the line loss evaluation method of the substation area based on the improved fuzzy decision theory includes:

[0078] S1: According to the line loss evaluation principle of power enterprises, a line loss evaluation index system for power enterprises is established based on multiple dimensions to collect corresponding data;

[0079] Regarding the evaluation principle of line loss in power enterprises, it should be noted that the evaluation of line loss in power stations is a comprehensive evaluation under the influence of multiple factors, and the index system is the basis for comprehensive evaluation. The evaluation index system is not composed of a single index, but an organic whole formed by multiple indicators with certain correlations and a certain hierarchical structure. The index system will be bound by many factors, including the evaluation object and target, as well as the effect of the evaluation subject's values ​​on it. Constructing an evaluation index system is the core of comprehensive evaluation. When establishing an index system and an evaluation model, its accuracy and comprehensiveness should be considered. When determining the credibility of the comprehensive results, whether the evaluation index system is reasonable should be considered. In order to construct an accurate, true and scientific index system, it is necessary not only to consider the characteristics of the line loss in the station itself, but also to comply with the general principles of index selection, including the scientific principle, comprehensive principle, purpose principle, simplicity principle, independence principle, hierarchy principle, feasibility principle, and the principle of combining qualitative and quantitative indicators.

[0080] Regarding the principle of scientificity, a large amount of data and information will be used when conducting research on a project. In the process of collecting and processing these data and information, it is necessary to base them on objective scientific evaluation and comply with objective laws and principles, and then combine them with the actual situation. In order to ensure the objective authenticity of the results, the growing data should also be processed accurately.

[0081] Regarding the principle of comprehensiveness, the selected indicators should cover as many aspects of evaluating the line loss level of the substation as possible, comprehensively reflect various effective influencing factors, and reflect the line loss level of the substation from different aspects. Otherwise, the evaluation conclusion will be biased.

[0082] Regarding the principle of purpose, the purpose of the selected indicators should be very clear and must focus on the evaluation target of the line loss of the substation group. Avoid selecting indicators that are irrelevant to the evaluation object or content, so as to ensure that the evaluation results can truly reflect the status of the line loss level in the substation area.

[0083] Regarding the principle of simplicity, under the premise of meeting the principle of comprehensiveness, the selection of indicators is not the more the better. A refined indicator system can not only prevent duplication of information, but also reduce the difficulty of evaluation calculation and analysis, which is conducive to the development of evaluation activities.

[0084] Regarding the principle of independence, various indicators should remain relatively independent, and the overlap between indicators should be reduced or avoided as much as possible, and the correlation between indicators should be reduced to avoid interference with evaluation conclusions caused by repeated information.

[0085] Regarding the hierarchical principle, the evaluation of line loss levels in substations involves a wide range and more content. The evaluation objectives should be multi-level. Establishing a hierarchical structure of the indicator system can clearly and intuitively characterize the internal connections between indicators, while facilitating the calculation and expression of indicators. This plays an important role in improving the efficiency and effectiveness of the evaluation.

[0086] Regarding the feasibility principle, the indicators should be practical and easy to operate. Specifically, the definition of the indicators should be clear and standardized, the degree of excellence should be easy to measure, and they should be fair and comparable. The data of the indicators should be easy to collect and have a stable data source, and the statistical caliber should be unified.

[0087] Regarding the principle of combining qualitative and quantitative indicators, there are many factors that affect the comprehensive evaluation of projects, and many of them are difficult to quantify. Therefore, in order to obtain an accurate and comprehensive evaluation, qualitative and quantitative methods should be combined.

[0088] In this application, the constructed substation line loss evaluation index system needs to fully consider the multi-dimensional factors from management, operation and maintenance, equipment and load in the process of substation line loss management, take the economic operation of the substation as the benchmark, and fully meet the assessment requirements of the power companies for the substations.

[0089] Specifically, the substation line loss evaluation index system includes primary indicators and secondary indicators corresponding to the primary indicators; the primary indicators include personnel situation, equipment situation, load situation and management situation; the secondary indicators corresponding to the personnel situation include the average educational level of substation responsible personnel, the number of substation responsible persons and the population covered by the substation; the secondary indicators corresponding to the equipment situation include the substation equipment procurement cost, the substation equipment update frequency and the advancement level of the substation metering equipment; the secondary indicators corresponding to the load situation include the annual average load of the substation and the maximum load of the substation; the secondary indicators corresponding to the management situation include the substation maintenance interval and the annual failure frequency of substation equipment.

[0090] S2: Based on the triangular fuzzy numbers of the fused interval factors, the uncertainty variables in the substation line loss evaluation index system are characterized; that is, the triangular fuzzy numbers of the fused interval factors are used to characterize the uncertainty variables in the index system.

[0091] Considering the singleness of the traditional triangular fuzzy number three-dimensional data representation for fuzzy information, this application uses interval theory to integrate interval theory with traditional triangular fuzzy numbers to construct new triangular fuzzy numbers to represent the specific information of some uncertainty indicators.

[0092] Specifically, the interval theory is integrated with the traditional triangular fuzzy number to obtain the triangular fuzzy number based on the integrated interval factor, that is, the new triangular fuzzy number, where the new triangular fuzzy number is specifically:

[0093]

[0094] Among them, μ A (x) represents the new triangular fuzzy number, x represents the variable, a1 and a2 represent the left endpoints of the triangular fuzzy number, c1 and c2 represent the right endpoints of the triangular fuzzy number, b represents the horizontal coordinate corresponding to the vertex of the triangular fuzzy number, μ A1 , μ A2 represents a constant; the new triangular fuzzy number formed is Figure 2 As shown;

[0095] The clarification method of the new triangular fuzzy number is expressed as:

[0096]

[0097] Among them, f μ The clarity method for representing the new triangular fuzzy number, μ a1 represents the horizontal coordinate of a1, μ a2 represents the horizontal coordinate of a2, μ c1 represents the horizontal coordinate of c1, μ c2 Represents the horizontal coordinate of c2.

[0098] S3: Calculate the weights of the substation line loss evaluation indicators in the substation line loss evaluation indicator system through the indicator weight perception algorithm; that is, use the indicator weight perception algorithm that takes into account the influence of subjective and objective factors to calculate the weights of the indicators in the indicator system.

[0099] Furthermore, in one embodiment, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by an index weight perception algorithm, wherein the subjective weight of the index is calculated by the G1 method (a subjective weighting method), specifically:

[0100] S301: Determine the index sequence, select an important index X1 from the n indicators of the substation line loss evaluation index, and record the corresponding index weight as α1, select an important index X2 from the remaining n-1 indicators, and record the corresponding index weight as α2, and so on until all indicators are selected, and obtain the index sequence (X1, X2, ..., X2) sorted by relative importance. n ), and the corresponding indicator weights (α1, α2, ..., α n );

[0101] S302: Determine the relative importance ratio of the indicators. j The weight of the indicators was determined after discussion among all the evaluators. The description of the relative importance of the indicators is shown in Table 1 below.

[0102] Table 1 Relative importance of indicators

[0103]

[0104] Calculate the relative importance ratio of indicators, specifically:

[0105]

[0106] Among them, r j Represents the relative importance ratio of the jth indicator, α j represents the indicator weight of the jth indicator;

[0107] S303: Calculate the nth indicator weight. Calculate the nth indicator weight, specifically:

[0108]

[0109] Among them, α n represents the weight of the nth indicator, and k represents the intermediate amount;

[0110] S304: Calculate the weights of all indicators, and calculate the weights of other n-1 indicators according to the relative importance ratio of the indicators. Specifically:

[0111] αj-1 =r j α j

[0112] Among them, α j-1 Represents the weight of the j-1th indicator.

[0113] S305: Determine the weight coefficient of each indicator to the total target layer, set α pq is the weight coefficient of the qth index under the pth criterion layer in the overall target layer in the area line loss evaluation index system, α p is the weight coefficient of the pth criterion layer in the total target layer, α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then we get:

[0114] α pq =α p ×α (p) q .

[0115] It should be noted that the ordinal relationship analysis method is to rank the importance of indicators by experts and rationally weight the importance of adjacent indicators, which is highly subjective and arbitrary. Therefore, while improving the ranking and scoring method, the grey correlation analysis method is introduced to determine the objective weighting coefficient of the indicator, and the indicator combination weight coefficient is calculated through linear operation.

[0116] Furthermore, in one embodiment, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by an index weight perception algorithm, wherein the objective weight of the index is calculated by a grey correlation method.

[0117] The basic principle of using the grey correlation method is: for the influencing factors of the system, the value of the correlation size that changes with time or different objects is the correlation degree. In the development process of the system, if the data series of two factors have a consistent degree of change, the higher the correlation between the two, and vice versa. Correlation analysis is a quantitative analysis of the dynamic development process of the system, which measures the proximity between factors according to the same or different degree of development and change between the influencing factors. Grey correlation analysis (GRA) is a statistical analysis method used to describe the strength of the relationship between multiple factors in the system. The degree of influence of multiple factors in the system and their contribution to the system can be analyzed and determined by grey correlation. Grey correlation analysis provides quantitative analysis for the dynamic indicators of the system, which is very suitable for the analysis of dynamic systems.

[0118] For the grey correlation method, the specific calculation steps include:

[0119] S311: Determine the system main behavior reference sequence X0(k) and the comparison sequence X0(k) of system related factorsi (k);

[0120] S312: Compare sequence X i The data in (k) are normalized to convert the data into a pure digital sequence;

[0121] Since the physical meanings of different factors in the system are different and the dimensions of their data are also different, it is difficult to draw correct conclusions when making comparisons. Therefore, it is necessary to normalize the data and convert it into a pure digital sequence to facilitate calculation and comparative analysis.

[0122] Normalization methods include initialization, averaging, and intervalization. Initialization is to divide the sequence data by the initial value; averaging is to divide the sequence data by the mean; intervalization is to return the sequence data to a range.

[0123] S313: Calculate the correlation coefficient, specifically:

[0124]

[0125] Among them, ξ 0i It represents the correlation coefficient of the ith indicator in the area line loss evaluation index, ρ represents the resolution coefficient, the value range is [0,1], and it is generally taken as 0.5;

[0126] S314: Calculate the correlation degree, specifically:

[0127]

[0128] Among them, γ(X0,X i ) represents the correlation between the reference sequence and the comparison sequence, and n represents the total number of indicators of the line loss evaluation index of the substation area;

[0129] S315: Sort the calculated association degrees according to their sizes to reflect the strength of the relationship between the comparison sequence and the reference sequence.

[0130] S4: Based on the characterized uncertainty variables and calculated weights, a substation line loss evaluation model based on improved fuzzy decision theory is constructed to realize the perception and evaluation of the power company's substation line loss management situation.

[0131] Specifically, the fuzzy comprehensive evaluation method is used to construct a substation line loss evaluation model based on the improved fuzzy decision-making theory to perceive the line loss management status of multiple substations of power companies.

[0132] In this application, the calculated subjective weight results and objective weight results of the indicators are integrated to obtain the comprehensive weights of the indicators in the substation line loss evaluation index.

[0133] Furthermore, in one embodiment, before constructing the substation line loss evaluation model, it also includes: preprocessing the index;

[0134] The specific steps of the pretreatment include:

[0135] S401: Assume that there are n indicators (X1, X2, ..., X n ), there are m groups of observations x (where i = 1, 2, ..., m; j = 1, 2, ..., n), if X n If is the minimization index, then the maximization process is performed to obtain If X n is the maximization index, then the maximization process is performed to obtain x ij ′=x ij , where x ij ′ represents the ij The value obtained after maximization processing, d represents a constant;

[0136] S402: Considering the impact of the size of the original data of the indicator, the min-max standardization method is used to standardize it. Specifically, based on the standardization method, x ij 'Standardization processing, specifically:

[0137]

[0138] Among them, m j Indicates the minimum data in the indicator, M j Indicates the maximum data in the indicator.

[0139] Furthermore, in one embodiment, the step of evaluating the line loss management situation of the power enterprise's area by the area line loss evaluation model includes:

[0140] S411: Obtaining evaluation data corresponding to indicators in the substation line loss evaluation system;

[0141] S412: For uncertainty indicators, triangular fuzzy numbers based on fusion interval factors are used to characterize the uncertainty indicators;

[0142] S413: Based on the specific steps of the preprocessing, preprocess the indicators to construct a fuzzy decision matrix;

[0143] S414: Calculate the fuzzy decision matrix based on the fuzzy comprehensive evaluation method to obtain an evaluation result.

[0144] The following is a specific description of the substation line loss evaluation method based on improved fuzzy decision theory of the present application with reference to an example.

[0145] The line loss related data of multiple substations of a power company are analyzed as an example. First, a comprehensive evaluation model of substation line loss related data with company characteristics is used to evaluate and analyze it. For the convenience of representation, the indicators of personnel dimension, such as the average education level of substation responsible personnel, the number of substation responsible persons, and the population covered by the substation, are represented by C1, C2, and C3 respectively. The indicators of equipment dimension, such as substation equipment procurement cost, substation equipment update frequency, and substation metering equipment advanced level, are represented by C4, C5, and C6 respectively. The indicators of load dimension, such as substation annual average load and substation maximum load, are represented by C7 and C8 respectively. The indicators of management dimension, such as substation maintenance interval and substation equipment annual failure frequency, are represented by C9, C10, C110, C120, C130, C140, C150, C160, C170, C180, C190, C200, C210, C220, C23 ... 10 The basic data are shown in Table 2 below.

[0146] Table 2 Basic data of the line loss assessment index system in the substation area

[0147]

[0148]

[0149] Then, data processing and weight calculation are carried out.

[0150] The indicator uncertainty conversion is shown in Table 3 below.

[0151] Table 3

[0152]

[0153] For indicator initialization, see Table 4 below.

[0154] Table 4

[0155]

[0156]

[0157] The indicator weight is calculated and the subjective weight of the indicator is calculated using the G1 method.

[0158] Table 5 Subjective weights of the line loss assessment index system with grid company characteristics

[0159]

[0160] The objective weights of indicators are calculated using the grey correlation analysis method.

[0161] Table 6 Objective weights of the line loss assessment index system with grid company characteristics

[0162]

[0163] Get the comprehensive weight of the indicators.

[0164] Table 7 Comprehensive weights of the line loss assessment index system with grid company characteristics

[0165]

[0166] The fuzzy comprehensive evaluation results of the line loss in the substation area are taken as an example. Based on the weights and the index initialization results, the fuzzy comprehensive evaluation method is used to calculate the results as shown in Table 8 below.

[0167] Table 8

[0168]

[0169]

[0170] Hi=Ci×V(i=1,2,3)

[0171]

[0172]

[0173]

[0174]

[0175] After calculation, the result of area 1 is 0.806.

[0176] Then the fuzzy comprehensive evaluation results of line loss of substation 2, substation 3 and substation 4 are calculated respectively, and finally the result is: substation 3 < substation 4 < substation 1 < substation 2.

[0177] The method for evaluating line loss in a substation based on improved fuzzy decision-making theory in the embodiment of the present application first clarifies the main contents involved in the construction of an evaluation index system for line loss in a substation of a power enterprise, and clarifies its specific principles; secondly, based on the above-mentioned construction principles, a multi-factor index evaluation system for line loss management in a substation of a power enterprise is constructed from multiple dimensions such as personnel, equipment, and load; then, based on fuzzy decision theory, a triangular fuzzy number that integrates interval factors is proposed to characterize the uncertainty variables in the index system; then, an index weight perception method that considers the influence of subjective and objective factors is proposed to calculate the weight of each index in the substation line loss evaluation index system; finally, a fuzzy comprehensive evaluation method is used to construct a substation line loss evaluation model based on improved fuzzy decision theory.

[0178] In a second aspect, an embodiment of the present application also provides a substation line loss evaluation device based on improved fuzzy decision theory.

[0179] In one embodiment, referring to Figure 3 , Figure 3This is a functional module diagram of the station area line loss evaluation device based on the improved fuzzy decision theory in this application. Figure 3 As shown, the substation line loss evaluation device based on improved fuzzy decision theory includes a construction module, a characterization module, a calculation module and an execution module.

[0180] The construction module is used to collect corresponding data according to the power enterprise's power enterprise line loss evaluation principle and based on multiple dimensions to construct a power station line loss evaluation index system for power enterprise's power station line loss management; the characterization module is used to characterize the uncertainty variables in the power station line loss evaluation index system based on the triangular fuzzy number of the fusion interval factor; the calculation module is used to calculate the weights of the power station line loss evaluation indicators in the power station line loss evaluation index system through the indicator weight perception algorithm; the execution module is used to construct a power station line loss evaluation model based on the improved fuzzy decision theory based on the characterized uncertainty variables and the calculated weights, so as to realize the perception and evaluation of the power enterprise's power station line loss management situation.

[0181] Among them, the functional implementation of each module in the above-mentioned substation line loss evaluation device based on improved fuzzy decision theory corresponds to the steps in the above-mentioned substation line loss evaluation method embodiment based on improved fuzzy decision theory, and its functions and implementation process will not be repeated here one by one.

[0182] On the third aspect, the embodiment of the present application provides a substation line loss evaluation device based on improved fuzzy decision theory. The substation line loss evaluation device based on improved fuzzy decision theory can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0183] Reference Figure 4 , Figure 4 The hardware structure diagram of the station area line loss evaluation device based on the improved fuzzy decision theory involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the station area line loss evaluation device based on the improved fuzzy decision theory may include a processor, a memory, a communication interface and a communication bus.

[0184] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0185] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to realize the interconnection of devices inside the area line loss evaluation device based on the improved fuzzy decision theory, and an interface for realizing the interconnection between the area line loss evaluation device based on the improved fuzzy decision theory and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0186] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0187] The processor may be a general-purpose processor, which may call the station line loss evaluation program based on the improved fuzzy decision theory stored in the memory, and execute the station line loss evaluation method based on the improved fuzzy decision theory provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the station line loss evaluation program based on the improved fuzzy decision theory is called may refer to the various embodiments of the station line loss evaluation method based on the improved fuzzy decision theory of the present application, which will not be repeated here.

[0188] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0189] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0190] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0191] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0192] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0193] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0195] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating line loss in a transformer area based on improved fuzzy decision theory, characterized in that: The station area line loss evaluation method based on improved fuzzy decision theory includes: According to the line loss evaluation principle of power enterprises, a line loss evaluation index system for power enterprises is constructed based on multiple dimensions to collect corresponding data; Based on the triangular fuzzy number integrating interval factors, the uncertainty variables in the line loss evaluation index system of the substation are characterized. The weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated through the index weight perception algorithm; Based on the characterized uncertainty variables and calculated weights, a line loss evaluation model for the substation area based on improved fuzzy decision theory is constructed to realize the perception and evaluation of the line loss management situation of the power company's substation area; Among them, the interval theory is integrated with the traditional triangular fuzzy number to obtain the triangular fuzzy number based on the fusion interval factor, that is, the new triangular fuzzy number, wherein the new triangular fuzzy number is specifically: Among them, μ A (x) represents the new triangular fuzzy number, x represents the variable, a1 and a2 represent the left endpoints of the triangular fuzzy number, c1 and c2 represent the right endpoints of the triangular fuzzy number, b represents the horizontal coordinate corresponding to the vertex of the triangular fuzzy number, μ A1 , μ A2 Represents a constant; The clarification method of the new triangular fuzzy number is expressed as: Among them, f μ The clarity method for representing the new triangular fuzzy number, μ a1 represents the horizontal coordinate of a1, μ a2 represents the horizontal coordinate of a2, μ c1 represents the horizontal coordinate of c1, μ c2 represents the horizontal coordinate of c2; Among them, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the subjective weight of the index is calculated by the G1 method, specifically: Select an important indicator X1 from the n indicators of the area line loss evaluation index, and record the corresponding indicator weight as α1. Select an important indicator X2 from the remaining n-1 indicators, and record the corresponding indicator weight as α2. This is repeated until all indicators are selected, and the indicator sequence (X1, X2, ..., X2) ranked by relative importance is obtained. n ), and the corresponding indicator weights (α1, α2, ..., α n ); Calculate the relative importance ratio of indicators, specifically: Among them, r j Represents the relative importance ratio of the jth indicator, α j represents the indicator weight of the jth indicator; Calculate the weight of the nth indicator, specifically: Among them, α n represents the weight of the nth indicator, and k represents the intermediate amount; According to the relative importance ratio of the indicators, the weights of the other n-1 indicators are calculated, specifically: α j-1 ＝r j α j Among them, α j-1 Represents the weight of the j-1th indicator. Setting α pq is the weight coefficient of the qth index under the pth criterion layer in the overall target layer in the area line loss evaluation index system, α p is the weight coefficient of the pth criterion layer in the total target layer, α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then we get: α pq ＝α p ×α (p) q ； The weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the objective weight of the index is calculated by the grey correlation method. For the grey correlation method, the specific calculation steps include: Determine the system main behavior reference sequence X0(k) and the comparison sequence X0(k) of system related factors i (k); Compare the sequence X i The data in (k) are normalized to convert the data into a pure digital sequence; Calculate the correlation coefficient, specifically: Among them, ξ 0i represents the correlation coefficient of the ith indicator in the area line loss evaluation index, ρ represents the resolution coefficient, and its value range is [0,1]; Calculate the correlation, specifically: Among them, γ(X0,X i ) represents the correlation between the reference sequence and the comparison sequence, and n represents the total number of indicators of the line loss evaluation index of the substation area; The calculated association degrees are sorted according to size to reflect the strength of the relationship between the comparison sequence and the reference sequence; in, The substation line loss evaluation index system includes primary indicators and secondary indicators corresponding to the primary indicators; The first-level indicators include personnel situation, equipment situation, load situation and management situation; The secondary indicators corresponding to the personnel situation include the average education level of the personnel in charge of the substation, the number of persons in charge of the substation and the population covered by the substation; The secondary indicators corresponding to the equipment conditions include the equipment purchase cost of the substation area, the equipment update frequency of the substation area, and the advanced level of the metering equipment in the substation area; The secondary indicators corresponding to the load conditions include the annual average load of the substation area and the maximum load of the substation area; The secondary indicators corresponding to the management situation include the maintenance interval of the substation area and the annual failure frequency of the substation area equipment.

2. A method for evaluating line loss in a transformer area based on improved fuzzy decision theory as claimed in claim 1, characterized in that: The calculated subjective weight results and objective weight results of the indicators are integrated to obtain the comprehensive weights of the indicators in the substation line loss evaluation index.

3. The method for evaluating line loss in a transformer area based on improved fuzzy decision theory according to claim 1, characterized in that: Before constructing the line loss evaluation model of the substation area, it also includes: pre-processing the indicators; The specific steps of the pretreatment include: Assume that there are n indicators (X1, X2, ..., X n ), there are m groups of observations x ij (where i = 1, 2, ..., m; j = 1, 2, ..., n), if X n If is the minimization index, then the maximization process is performed to obtain If X n is the maximization index, then the maximization process is performed to obtain x ij ′=x ij , where x ij ′ represents the ij The value obtained after maximization processing, d represents a constant; Based on the standardized method, x ij 'Standardization processing, specifically: Among them, m j Indicates the minimum data in the indicator, M j Indicates the maximum data in the indicator.

4. A method for evaluating line loss in a transformer area based on improved fuzzy decision theory as claimed in claim 3, characterized in that: The steps of evaluating the line loss management situation of the power enterprise's area by the area line loss evaluation model include: Obtain the evaluation data corresponding to the indicators in the substation line loss evaluation system; For uncertainty indicators, triangular fuzzy numbers based on fusion interval factors are used to characterize uncertainty indicators; Based on the specific steps of the preprocessing, the indicators are preprocessed to construct a fuzzy decision matrix; The fuzzy decision matrix is ​​calculated based on the fuzzy comprehensive evaluation method to obtain the evaluation result.

5. A substation line loss evaluation device based on improved fuzzy decision theory, characterized in that: The station area line loss evaluation device based on improved fuzzy decision theory includes: A construction module is used to construct a line loss evaluation index system for power enterprises based on the line loss evaluation principle of power enterprises and based on multiple dimensions to collect corresponding data; A characterization module is used to characterize the uncertainty variables in the line loss evaluation index system of the substation based on the triangular fuzzy number of the fusion interval factor; A calculation module, which is used to calculate the weight of the substation line loss evaluation index in the substation line loss evaluation index system through an index weight perception algorithm; An execution module is used to construct a substation line loss evaluation model based on the improved fuzzy decision theory based on the characterized uncertainty variables and calculated weights, so as to realize the perception and evaluation of the substation line loss management situation of the power enterprise; Among them, the interval theory is integrated with the traditional triangular fuzzy number to obtain the triangular fuzzy number based on the fusion interval factor, that is, the new triangular fuzzy number, wherein the new triangular fuzzy number is specifically: Among them, μ A (x) represents the new triangular fuzzy number, x represents the variable, a1 and a2 represent the left endpoints of the triangular fuzzy number, c1 and c2 represent the right endpoints of the triangular fuzzy number, b represents the horizontal coordinate corresponding to the vertex of the triangular fuzzy number, μ A1 , μ A2 Represents a constant; The clarification method of the new triangular fuzzy number is expressed as: Among them, f μ The clarity method for representing the new triangular fuzzy number, μ a1 represents the horizontal coordinate of a1, μ a2 represents the horizontal coordinate of a2, μ c1 represents the horizontal coordinate of c1, μ c2 represents the horizontal coordinate of c2; Among them, the weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the subjective weight of the index is calculated by the G1 method, specifically: Select an important indicator X1 from the n indicators of the area line loss evaluation index, and record the corresponding indicator weight as α1. Select an important indicator X2 from the remaining n-1 indicators, and record the corresponding indicator weight as α2. This is repeated until all indicators are selected, and the indicator sequence (X1, X2, ..., X2) ranked by relative importance is obtained. n ), and the corresponding indicator weights (α1, α2, ..., α n ); Calculate the relative importance ratio of indicators, specifically: Among them, r j Represents the relative importance ratio of the jth indicator, α j represents the indicator weight of the jth indicator; Calculate the weight of the nth indicator, specifically: Among them, α n represents the weight of the nth indicator, and k represents the intermediate amount; According to the relative importance ratio of the indicators, the weights of the other n-1 indicators are calculated, specifically: α j-1 ＝r j α j Among them, α j-1 Represents the weight of the j-1th indicator. Setting α pq is the weight coefficient of the qth index under the pth criterion layer in the overall target layer in the area line loss evaluation index system, α p is the weight coefficient of the pth criterion layer in the total target layer, α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then we get: α pq ＝α p ×α (p) q ； The weight of the substation line loss evaluation index in the substation line loss evaluation index system is calculated by the index weight perception algorithm, wherein the objective weight of the index is calculated by the grey correlation method. For the grey correlation method, the specific calculation steps include: Determine the system main behavior reference sequence X0(k) and the comparison sequence X0(k) of system related factors i (k); Compare the sequence X i The data in (k) are normalized to convert the data into a pure digital sequence; Calculate the correlation coefficient, specifically: Among them, ξ 0i represents the correlation coefficient of the ith indicator in the area line loss evaluation index, ρ represents the resolution coefficient, and its value range is [0,1]; Calculate the correlation, specifically: Among them, γ(X0,X i ) represents the correlation between the reference sequence and the comparison sequence, and n represents the total number of indicators of the line loss evaluation index of the substation area; The calculated association degrees are sorted according to size to reflect the strength of the relationship between the comparison sequence and the reference sequence; in, The substation line loss evaluation index system includes primary indicators and secondary indicators corresponding to the primary indicators; The first-level indicators include personnel situation, equipment situation, load situation and management situation; The secondary indicators corresponding to the personnel situation include the average education level of the personnel in charge of the substation, the number of persons in charge of the substation and the population covered by the substation; The secondary indicators corresponding to the equipment conditions include the equipment purchase cost of the substation area, the equipment update frequency of the substation area, and the advanced level of the metering equipment in the substation area; The secondary indicators corresponding to the load conditions include the annual average load of the substation area and the maximum load of the substation area; The secondary indicators corresponding to the management situation include the maintenance interval of the substation area and the annual failure frequency of the substation area equipment.

6. A substation line loss evaluation device based on improved fuzzy decision theory, characterized in that: The substation line loss evaluation device based on improved fuzzy decision theory includes a processor, a memory, and a substation line loss evaluation program based on improved fuzzy decision theory stored in the memory and executable by the processor, wherein when the substation line loss evaluation program based on improved fuzzy decision theory is executed by the processor, the steps of the substation line loss evaluation method based on improved fuzzy decision theory as described in any one of claims 1 to 4 are implemented.

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