Power quality evaluation method based on dynamic empowerment and related equipment

Abstract

The invention relates to the technical field of electric energy quality evaluation, and provides an electric energy quality evaluation method based on dynamic empowerment and related equipment. The method comprises the steps of obtaining values of a plurality of electric energy indexes of a target electric energy system; determining a plurality of target electric energy indexes from all the electric energy indexes; calculating the subjective weight of each target electric energy index by using an analytic hierarchy process combined with a fuzzy theory; calculating the objective weight of each target electric energy index by using an entropy weight method; and calculating an electric energy quality evaluation value of the target electric energy system based on all the subjective weights, all the objective weights and all the values of the target electric energy indexes, and obtaining an electric energy quality evaluation result of the target electric energy system according to the electric energy quality evaluation value. According to the method, the accuracy of power quality evaluation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power quality assessment, and in particular to a power quality assessment method based on dynamic weighting and related equipment. Background Art

[0002] With the rapid development of power systems and the growing demand for electricity, power quality, as an important indicator to ensure the stable and reliable operation of power systems and power-consuming equipment, has become increasingly important. Power quality issues not only affect the operating efficiency and life of power equipment, but may also have a serious impact on production safety, product quality and even people's daily lives. Therefore, a comprehensive and accurate assessment of power quality and the adoption of corresponding optimization measures have become important issues that need to be urgently addressed in the power industry.

[0003] Traditional power quality assessment methods mostly rely on a single data source and fixed weight allocation, which makes it difficult to fully reflect the complexity and dynamics of power quality. On the one hand, with the access of various new power equipment and loads, power quality issues are becoming more diverse and complex, and a single data source can no longer meet the assessment needs; on the other hand, the importance of different indicators in the assessment may change with time and conditions, and fixed weight allocation methods are difficult to adapt to such dynamic changes. This leads to inaccurate weight allocation, which in turn leads to low accuracy in power quality assessment. Summary of the invention

[0004] The present application provides a power quality assessment method based on dynamic weighting and related equipment, which can solve the problem of low accuracy of power quality assessment.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating power quality based on dynamic weighting, the method comprising:

[0006] Obtaining values ​​of multiple power indicators of a target power system;

[0007] A plurality of target power indicators are determined from all power indicators; the influence of each target power indicator on power quality is greater than the influence of all other power indicators on power quality;

[0008] The subjective weight of each target power indicator is calculated by using the hierarchical analysis method combined with fuzzy theory; the subjective weight is used to describe the importance of the target power indicator in power quality assessment;

[0009] The objective weight of each target power indicator is calculated using the entropy weight method; the objective weight is used to describe the amount of information of the target power indicator in power quality assessment;

[0010] Calculate the power quality evaluation value of the target power system based on the values of all subjective weights, all objective weights, and all target power indices, and obtain the power quality evaluation result of the target power system according to the power quality evaluation value; the power quality evaluation result is used to describe the power quality status of the target power system.

[0011] Optionally, use the analytic hierarchy process combined with fuzzy theory to calculate the subjective weight of each target power index, including:

[0012] Define multiple expert models, and use Bayes' theorem to calculate the evaluation value of each expert model for each target power index;

[0013] For each expert model respectively, use the expert model to conduct a secondary evaluation on each evaluation value to obtain the fuzzy number of each expert model for each evaluation value;

[0014] Aggregate all the fuzzy numbers to obtain a fuzzy judgment matrix; the elements in the fuzzy judgment matrix are the final fuzzy numbers of each expert model for each target power index;

[0015] Standardize and transform the fuzzy judgment matrix to obtain a fuzzy consistent matrix;

[0016] Calculate the initial subjective weight of each target power index according to the fuzzy consistent matrix, and dynamically adjust each initial subjective weight to obtain the subjective weight of each target power index.

[0017] Optionally, aggregate all the fuzzy numbers to obtain a fuzzy judgment matrix, including:

[0018] Through the formula:

[0019]

[0020] Calculate the fuzzy judgment matrix A;

[0021] Among them, K represents the number of expert models, represents the fuzzy number of the secondary evaluation of the k-th expert model for q ij , q ij represents the evaluation value of the i-th expert model for the j-th target power index, and n represents the number of target power indices.

[0022] Optionally, standardize and transform the fuzzy judgment matrix to obtain a fuzzy consistent matrix, including:

[0023] Through the formula:

[0024]

[0025] Calculate the element r' in the fuzzy consistent matrix ij ;

[0026] Among them, r' ij represents the final fuzzy number of the i-th expert model for the j-th target power quality index, and r ij represents the standard fuzzy number of the i-th expert model for the j-th target power quality index, and a ij is an element in the fuzzy judgment matrix, representing the initial fuzzy number of the i-th expert model for the j-th target power quality index, and a zj represents the initial fuzzy number of the z-th expert model for the j-th target power quality index, and r jK represents the standard fuzzy number of the K-th expert model for the j-th target power quality index;

[0027] Calculate the initial subjective weight of each target power quality index according to the fuzzy consistent matrix, including:

[0028] Through the formula:

[0029]

[0030] Calculate the initial subjective weight of the j-th target power quality index

[0031] Dynamically adjust each initial subjective weight to obtain the subjective weight of each target power quality index, including:

[0032] Through the formula:

[0033]

[0034] Calculate the subjective weight w of the j-th target power quality index sub,j ;

[0035] Among them, represents the triangular fuzzy upper limit value corresponding to the j-th target power quality index, a j represents the triangular fuzzy lower limit value corresponding to the j-th target power quality index, x represents the value within the interval inside, represents the membership function of, represents the dynamic subjective weight of the j-th target power quality index, represents the normalized initial subjective weight of the j-th target power quality index, c1 represents the credibility of the first expert model, c2 represents the credibility of the second expert model, and c K represents the credibility of the K-th expert model.

[0036] Optionally, calculate the power quality evaluation value of the target power system based on all subjective weights, all objective weights, and the values of all target power quality indices, including:

[0037] Calculate the comprehensive weight of each target power index according to the subjective weight and objective weight corresponding to each target power index;

[0038] Based on all the comprehensive weights, sum up the values of all the target power indexes with weighting to obtain the power quality evaluation value of the target power system.

[0039] Optionally, calculating the comprehensive weight of the target power index according to the subjective weight and objective weight corresponding to each target power index includes:

[0040] Introduce the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introduce the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight;

[0041] According to the adjusted subjective weight and adjusted objective weight corresponding to each target power index, introduce the fusion coefficient to calculate the candidate comprehensive weight of each target power index;

[0042] Calculate the difference value according to all the adjusted objective weights and all the adjusted subjective weights;

[0043] Judge whether the difference value is less than or equal to the difference preset value;

[0044] If so, take each candidate comprehensive weight as the comprehensive weight of the corresponding target power index;

[0045] Otherwise, adjust the values of the subjective factor weight coefficient, data characteristic influence coefficient and fusion coefficient, and return to the step of introducing the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introducing the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight.

[0046] Optionally, introducing the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight includes:

[0047] Through the formula:

[0048] w' obj,j =α j ·w obj,j

[0049] Calculate the adjusted objective weight w'; obj,j ;

[0050] where, w obj,j represents the objective weight of the jth target power index, α j represents the subjective factor weight coefficient corresponding to the jth target power index, j = 1, 2,..., n, and n represents the number of target power indexes;

[0051] The data characteristic influence coefficient is introduced to adjust each subjective weight, and the adjusted subjective weight is obtained, including:

[0052] Through the formula:

[0053] w' sub,j = w sub,j + β j ·(w obj,j - w sub,j )

[0054] Calculate the adjusted subjective weight w' sub,j ;

[0055] Among them, w sub,j represents the subjective weight of the j-th target power quality index, and β j represents the data characteristic influence coefficient corresponding to the j-th target power quality index.

[0056] According to the adjusted subjective weight and the adjusted objective weight corresponding to each target power quality index, a fusion coefficient is introduced to calculate the candidate comprehensive weight of each target power quality index, including:

[0057] Through the formula:

[0058] w com,j = γ·w' sub,j +(1 - γ)·w' obj,j

[0059] Calculate the candidate comprehensive weight w com,j of the j-th target power quality index;

[0060] Among them, γ represents the fusion coefficient, w' sub,j represents the adjusted subjective weight of the j-th target power quality index, and w' obj,j represents the adjusted objective weight of the j-th target power quality index.

[0061] Optionally, calculate the difference value according to all the adjusted objective weights and all the adjusted subjective weights, including:

[0062] Through the formula:

[0063]

[0064] Calculate the difference value D;

[0065] Among them, n represents the number of target power quality indexes.

[0066] In a second aspect, an embodiment of the present application provides a power quality assessment device based on dynamic weighting, including:

[0067] An acquisition module that acquires values of multiple power quality indicators of a target power system;

[0068] A determination module that determines multiple target power quality indicators from all power quality indicators; the influence degree of each target power quality indicator on power quality is greater than that of all other power quality indicators on power quality;

[0069] A subjective weight calculation module that calculates the subjective weight of each target power quality indicator using the analytic hierarchy process combined with the fuzzy theory; the subjective weight is used to describe the importance degree of the target power quality indicator in power quality assessment;

[0070] An objective weight calculation module that calculates the objective weight of each target power quality indicator using the entropy weight method; the objective weight is used to describe the amount of information of the target power quality indicator in power quality assessment;

[0071] A calculation module that calculates the power quality assessment value of the target power system based on all subjective weights, all objective weights, and the values of all target power quality indicators, and obtains the power quality assessment result of the target power system according to the power quality assessment value; the power quality assessment result is used to describe the power quality status of the target power system.

[0072] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned power quality assessment method based on dynamic weight assignment is implemented.

[0073] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned power quality assessment method based on dynamic weight assignment is implemented.

[0074] The above solution of the present application has the following beneficial effects:

[0075] In the embodiments of the present application, by obtaining the values of multiple power quality indicators of a target power system, then determining multiple target power quality indicators from all the power quality indicators, calculating the subjective weights of each target power quality indicator using the analytic hierarchy process combined with fuzzy theory, then calculating the objective weights of each target power quality indicator using the entropy weight method, and finally calculating the power quality evaluation value of the target power system based on all the subjective weights, all the objective weights and the values of all the target power quality indicators, the power quality evaluation result of the target power system is obtained according to the power quality evaluation value. Among them, obtaining the values of multiple power quality indicators expands the data source and improves the comprehensiveness of the data. Determining the target power quality indicators can improve the scientificity and effectiveness of the target power quality indicators. Calculating the subjective weights and objective weights can accurately and comprehensively express the weights of the target power quality indicators. Conducting power quality evaluation based on the subjective weights and objective weights can effectively improve the accuracy of power quality evaluation.

[0076] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0078] Figure 1 It is a flowchart of a power quality evaluation method based on dynamic weight assignment provided by an embodiment of the present application;

[0079] Figure 2 It is a schematic structural diagram of a power quality evaluation device based on dynamic weight assignment provided by an embodiment of the present application;

[0080] Figure 3 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0082] It should be understood that, as used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups.

[0083] It should also be understood that the term "and / or" as used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0084] As used in the specification of this application and the appended claims, the term "if" may be construed, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrases "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0085] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are used only for differential description and should not be construed as indicating or implying relative importance.

[0086] Reference to "one embodiment" or "some embodiments" or the like described in the specification of this application means that a particular feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0087] In view of the problem of low accuracy in the existing power quality assessment, the embodiments of the present application provide a power quality assessment method based on dynamic weighting. This power quality assessment method obtains the values of multiple power quality indicators of the target power system, then determines multiple target power quality indicators from all the power quality indicators, and then calculates the subjective weights of each target power quality indicator by using the analytic hierarchy process combined with the fuzzy theory. Then, the objective weights of each target power quality indicator are calculated by using the entropy weight method. Finally, the power quality assessment value of the target power system is calculated based on all the subjective weights, all the objective weights, and the values of all the target power quality indicators, and the power quality assessment result of the target power system is obtained according to the power quality assessment value. Among them, obtaining the values of multiple power quality indicators expands the data source and improves the comprehensiveness of the data. Determining the target power quality indicators can improve the scientificity and effectiveness of the target power quality indicators. Calculating the subjective weights and objective weights can accurately and comprehensively express the weights of the target power quality indicators. Conducting power quality assessment based on the subjective weights and objective weights can effectively improve the accuracy of power quality assessment.

[0088] Next, an exemplary description is given of the power quality assessment method based on dynamic weighting provided by the present application.

[0089] As Figure 1 shown, the power quality assessment method based on dynamic weighting provided by the present application includes the following steps:

[0090] Step 11, obtain the values of multiple power quality indicators of the target power system.

[0091] The above-mentioned multiple power quality indicators are indicators related to power quality, such as voltage, current, frequency, power factor, etc.

[0092] In some embodiments of the present application, professional power quality detection equipment can be used to measure and record the values of multiple power quality indicators in real time. Ensure the comprehensiveness and timeliness of the data, that is, cover different parts and different time periods of the target power system, and ensure that the collected data can reflect the latest situation of power quality.

[0093] It should be noted that after obtaining the data, it is necessary to identify and remove invalid data such as outliers and missing values, and at the same time use appropriate mathematical methods to smooth the data to reduce the interference of noise. In addition, in order to ensure the consistency and comparability of data from different sources, it is also necessary to standardize the data. Through these preprocessing operations, the accuracy and availability of the data can be significantly improved, thus providing strong support for the accurate assessment of power quality.

[0094] Step 12, determine multiple target power quality indicators from all the power quality indicators.

[0095] The influence degree of each of the above target power quality indicators on power quality is greater than that of all other power quality indicators.

[0096] Specifically, the effectiveness values of all power quality indicators are evaluated based on all power quality indicators; Next, a sensitivity analysis method can be used to calculate the influence degree of a single power quality indicator on the effectiveness value; Finally, multiple power quality indicators with small influence degrees are removed.

[0097] Exemplarily, the calculation process of the above sensitivity analysis method is as follows:

[0098] (1) Determine the objective function, constraint function of interest, and the power quality indicators involved in the analysis.

[0099] (2) Select a suitable two-level orthogonal table according to the number of power quality indicators (number of factors) involved in the analysis. For example, if the number of power quality indicators is n, the selected orthogonal table La(2c) should satisfy c≥n. From the construction rule of the two-dimensional level table:

[0100]

[0101] We get c = 4*i - 1, i≥(n + 1) / 4 (i is a positive integer). For example, when the number of power quality indicators n is 14, from the above formula, i = 4, that is, the orthogonal table to be selected is L 16 (2 15 )

[0102] (3) Determine the levels of each variable (power quality indicator) according to the variation design scheme of the selected orthogonal table (similar to arranging the test scheme in the orthogonal experiment).

[0103] (4) Call the optimization model (similar to conducting an experiment) to calculate the objective function and / or constraint function values corresponding to different design schemes respectively.

[0104] (5) Calculate the average values of the objective function and constraint function corresponding to each level of each variable

[0105] (6) From the formula Calculate the range R of the objective function and constraint function j .

[0106] Analyze the obtained results by the range analysis method. According to Judge the optimal levels of each variable for the objective function and constraint function, and then judge their respective monotonicities according to Table 1; According to R j Calculate the sensitivity |S ij | of the objective function and constraint function to each power quality indicator. This sensitivity is the influence degree of the power quality indicator.

[0107]

[0108] Table 1

[0109] It is worth mentioning that obtaining the values of multiple power quality indicators expands the data sources, improves the comprehensiveness of the data, and determining the target power quality indicators can improve the scientificity and effectiveness of the target power quality indicators.

[0110] Step 13: Calculate the subjective weight of each target power quality indicator by using the analytic hierarchy process combined with the fuzzy theory.

[0111] The above-mentioned subjective weight is used to describe the importance degree of the target power quality indicator in the power quality assessment.

[0112] In some embodiments of itself, the step of calculating the subjective weight of each target power quality indicator by using the analytic hierarchy process combined with the fuzzy theory includes:

[0113] The first step: Define multiple expert models, and calculate the evaluation value of each expert model for each target power quality indicator by using Bayes' theorem.

[0114] The above-mentioned expert model can be a knowledge base with knowledge related to power quality, for example, including knowledge related to the influence of the target power quality indicator on power quality.

[0115] Specifically, this step includes:

[0116] (1) Define variables and prior probabilities

[0117] Suppose there are k expert models. For a certain evaluation problem, each expert model E k (where k = 1, 2,..., K) has given its own opinion or data D k (such as: for the power quality level, D k = "excellent" or "unqualified"), define the set of events H (i.e., the set of target power quality indicators) as all possible evaluation results or hypotheses, that is, H = {H1, H2,..., H n}}. Assign a prior probability P(H|D k ) to the opinion D k of each expert model, which represents the probability of event H occurring under the condition that the expert model E k gives the opinion D k .

[0118] (2) Calculate the likelihood function

[0119] The likelihood function P(D k |H j ) represents that under the condition that the event occurs, the expert model E k gives the opinion D kThe probability. Estimate the likelihood function from historical data.

[0120] (3) Construct the evaluation matrix

[0121] Create a K×n evaluation matrix M, where M kj represents the expert model E k for the event H j evaluation value. The evaluation value M kj is calculated through the likelihood function P(D k |H j ).

[0122] (4) Apply Bayes' theorem

[0123] Use Bayes' theorem to update the probability of event H j , that is, calculate the posterior probability P(H j |D1,D2,...,D K ).

[0124] The posterior probability can be calculated by the formula:

[0125]

[0126] where P(D1,D2,...,D K |H j ) is the joint likelihood function, which can be decomposed into the product of individual likelihood functions:

[0127]

[0128] where, for new opinions, that is, opinions not belonging to the D1,D2,...,D K sequence, an incremental learning mechanism is introduced to update the posterior probability in real time to enhance accuracy, flexibility, and applicability. Denote the original opinion sequence as D old , and the new opinion sequence as D new . For the new opinion sequence D new and each event H j , calculate the likelihood function P(D new |H j ).

[0129] Use the recursive form of Bayes' theorem to update the prior probability P(H j |D old ) that is P(H j |D1,D2,...,D K ) to the posterior probability P(H j |D old ∪D new ):

[0130]

[0131] Among them, P(D new |D old ) can be calculated by the law of total probability:

[0132] P(D new |D old ) = ∑ j P(D new |H j ) · P(H j |D old )

[0133] (5) Fill the judgment matrix

[0134] Take the calculated posterior probability P(H j |D1, D2,..., D K ) as the value of the corresponding element in the judgment matrix M, that is, M ij = P(H j |D1, D2,..., D K ). The posterior probability between the expert model opinion and the target power index is the judgment value of the expert model for each target power index.

[0135] In the second step, for each expert model, use the expert model to perform a secondary judgment on each judgment value to obtain the fuzzy number of the expert model for each judgment value.

[0136] Exemplarily, triangular fuzzy numbers can be used to perform a secondary judgment on the judgment values to obtain the fuzzy numbers of the expert model for each judgment value. The triangular fuzzy number can be expressed as Among them a is the lower limit value of the triangular fuzzy set set according to the judgment value, a is the possible value (i.e., the fuzzy number), is the upper limit value of the triangular fuzzy set set according to the judgment value.

[0137] In the third step, aggregate all the fuzzy numbers to obtain the fuzzy judgment matrix.

[0138] The elements in the fuzzy judgment matrix are the final fuzzy numbers of each expert model for each target power index.

[0139] Specifically, through the formula:

[0140]

[0141] Calculate the fuzzy judgment matrix A;

[0142] Among them, K represents the number of expert models, represents the fuzzy number of the secondary judgment of the k-th expert model for q ij , q ijIt represents the evaluation value of the i-th expert model for the j-th target power energy index, and n represents the number of target power energy indexes.

[0143] In the fourth step, the fuzzy judgment matrix is standardized and transformed to obtain a fuzzy consistent matrix.

[0144] Specifically, through the formula:

[0145]

[0146] Calculate the element r' in the fuzzy consistent matrix ij ;

[0147] Among them, r' ij represents the final fuzzy number of the i-th expert model for the j-th target power energy index, r ij represents the standard fuzzy number of the i-th expert model for the j-th target power energy index, a ij is the element in the fuzzy judgment matrix, representing the initial fuzzy number of the i-th expert model for the j-th target power energy index, a zj represents the initial fuzzy number of the z-th expert model for the j-th target power energy index, r jK represents the standard fuzzy number of the K-th expert model for the j-th target power energy index.

[0148] In the fifth step, calculate the initial subjective weight of each target power energy index according to the fuzzy consistent matrix, and dynamically adjust each initial subjective weight to obtain the subjective weight of each target power energy index.

[0149] Specifically, through the formula:

[0150]

[0151] Calculate the initial subjective weight of the j-th target power energy index

[0152] Through the formula:

[0153]

[0154] Calculate the subjective weight w of the j-th target power energy index sub,j .

[0155] Among them, represents the upper triangular fuzzy limit value corresponding to the j-th target power energy index, a j represents the lower triangular fuzzy limit value corresponding to the j-th target power energy index, x represents the value within the interval inside, represents the membership function of, Denote the dynamic subjective weight of the j-th target power quality index. Denote the initial subjective weight after normalization of the j-th target power quality index. c1 represents the credibility of the first expert model, c2 represents the credibility of the second expert model, and c K represents the credibility of the K-th expert model.

[0156] It can be seen from the above calculation formula that the dynamic subjective weight is calculated based on the credibility of the expert model and the initial subjective weight. According to different actually set expert models, the adaptive adjustment of the dynamic subjective weight can be realized through the above calculation formula.

[0157] Exemplarily, the credibility of the expert model can be set according to the scale of the expert model and the richness of the information it contains. The larger the scale or the higher the richness of the information, the higher the credibility of the expert model.

[0158] Step 14: Calculate the objective weight of each target power quality index using the entropy weight method.

[0159] The above objective weight is used to describe the amount of information of the target power quality index in the power quality assessment.

[0160] It should be noted that the entropy weight method is a method of assigning weights. According to the influence of the numerical changes of each target power quality index on the whole, the entropy value of the target power quality index is calculated, and then the weight is determined. In information theory, entropy refers to the degree of chaos or disorder of the system (entropy is a measure of uncertainty). The larger the entropy value of the index, the smaller the weight (the larger the entropy value of the index -> the higher the degree of information chaos of the index -> the greater the uncertainty -> the smaller the amount of information -> the smaller the variation index -> the weaker the comprehensive evaluation ability of the index information -> the smaller the index weight). In this application, the entropy value of the target power quality index is calculated by the entropy weight method, and the objective weight of the target power quality index is determined based on the entropy value. According to the above description, the larger the entropy value, the smaller the objective weight of the target power quality index.

[0161] Step 15: Calculate the power quality evaluation value of the target power system based on all subjective weights, all objective weights, and the values of all target power quality indexes, and obtain the power quality evaluation result of the target power system according to the power quality evaluation value.

[0162] The above power quality evaluation result is used to describe the power quality status of the target power system. For example, if the power quality evaluation result is the power quality grade, the higher the numerical value of the power quality evaluation value, the higher the power quality grade, and the better the power quality status.

[0163] In some embodiments of this application, the step of calculating the power quality evaluation value of the target power system based on all subjective weights, all objective weights, and the values of all target power quality indexes, and obtaining the power quality evaluation result of the target power system according to the power quality evaluation value includes:

[0164] Step 1: Calculate the comprehensive weight of each target power index according to the subjective weight and objective weight corresponding to each target power index.

[0165] First, introduce the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introduce the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight.

[0166] Then, according to the adjusted subjective weight and adjusted objective weight corresponding to each target power index, introduce a fusion coefficient to calculate the candidate comprehensive weight of each target power index.

[0167] Then, calculate the difference value according to all the adjusted objective weights and all the adjusted subjective weights.

[0168] Then, determine whether the difference value is less than or equal to the preset difference value.

[0169] If so, take each candidate comprehensive weight as the comprehensive weight of the corresponding target power index.

[0170] Otherwise, adjust the values of the subjective factor weight coefficient, data characteristic influence coefficient and fusion coefficient, and return to the step of introducing the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introducing the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight.

[0171] It should be noted that the step of introducing the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight includes:

[0172] Through the formula:

[0173] w' obj,j =α j ·w obj,j

[0174] Calculate the adjusted objective weight w' obj,j ;

[0175] where, w obj,j represents the objective weight of the jth target power index, α j represents the subjective factor weight coefficient corresponding to the jth target power index, j = 1, 2,..., n, and n represents the number of target power indices;

[0176] The step of introducing the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight includes:

[0177] Through the formula:

[0178] w' sub,j = w sub,j + β j ·(w obj,j - w sub,j )

[0179] Calculate the adjusted subjective weight w' sub,j ;

[0180] where w sub,j represents the subjective weight of the j-th target power index, and β j represents the influence coefficient of the data characteristics corresponding to the j-th target power index.

[0181] The steps of introducing a fusion coefficient to calculate the candidate comprehensive weight of each target power index according to the adjusted subjective weight and adjusted objective weight corresponding to each target power index are as follows:

[0182] Through the formula:

[0183] w com,j = γ·w' sub,j +(1 - γ)·w' obj,j

[0184] Calculate the candidate comprehensive weight w of the j-th target power index com,j ;

[0185] where γ represents the fusion coefficient, w' sub,j represents the adjusted subjective weight of the j-th target power index, and w' obj,j represents the adjusted objective weight of the j-th target power index.

[0186] The steps of calculating the difference value according to all the adjusted objective weights and all the adjusted subjective weights are as follows:

[0187] Through the formula:

[0188]

[0189] Calculate the difference value D;

[0190] where n represents the number of target power indexes.

[0191] It should be noted that the expression of the above difference preset value is ε:

[0192]

[0193] where V represents the standard deviation, coefficient of variation or other indicators measuring the variability of data.

[0194] In the second step, based on all the comprehensive weights, the values of all the target power quality indicators are weighted and summed to obtain the power quality evaluation value of the target power system.

[0195] In the third step, according to the power quality evaluation value, the power quality evaluation result of the target power system is obtained.

[0196] Exemplarily, the power quality evaluation result is the power quality level, such as "Level 1", "Level 2", "Level 3", "Level 4", etc. The calculated power quality evaluation value is compared with the standard or threshold set according to the requirements of the power system operation and the characteristics of the power quality indicators to determine the belonging power quality level. This is to intuitively represent the power quality situation and provide a basis for subsequent power quality management and improvement, etc.

[0197] It is worth mentioning that obtaining the values of multiple power quality indicators expands the data source, improves the comprehensiveness of the data, determining the target power quality indicators can improve the scientificity and effectiveness of the target power quality indicators, calculating the subjective weights and objective weights can accurately and comprehensively express the weights of the target power quality indicators, and performing power quality evaluation based on the subjective weights and objective weights can effectively improve the accuracy of the power quality evaluation.

[0198] In addition, the beneficial effects of this application are also reflected in the following aspects:

[0199] 1. Realize dynamic and flexible selection of evaluation indicators: This application dynamically selects the indicators that have a significant impact on power quality through sensitivity analysis, making the evaluation more targeted and effective, adapting to the evaluation needs of different power systems and different time periods, providing accurate and useful evaluation results, and providing the possibility for further optimization of the power quality evaluation method.

[0200] 2. Improve the accuracy and pertinence of the evaluation: This application systematically collects and preprocesses the relevant data of power quality, combines the dynamically selected key indicators and comprehensive weight calculation, can more comprehensively and accurately evaluate the power quality, and helps the power system operator to more accurately understand the actual situation of the power quality.

[0201] Next, an exemplary description is given of the power quality evaluation device based on dynamic weighting provided by this application.

[0202] As Figure 2 shown, an embodiment of this application provides a power quality evaluation device based on dynamic weighting. The power quality evaluation device 200 based on dynamic weighting includes:

[0203] An acquisition module 201, which acquires the values of multiple power quality indicators of the target power system;

[0204] Determination module 202 determines a plurality of target power indices from all power indices; the influence degree of each target power index on power quality is greater than that of all other power indices on power quality;

[0205] Subjective weight calculation module 203 calculates the subjective weight of each target power index by using the analytic hierarchy process combined with fuzzy theory; the subjective weight is used to describe the importance degree of the target power index in power quality assessment;

[0206] Objective weight calculation module 204 calculates the objective weight of each target power index by using the entropy weight method; the objective weight is used to describe the information content of the target power index in power quality assessment;

[0207] Calculation module 205 calculates the power quality assessment value of the target power system based on all subjective weights, all objective weights and the values of all target power indices, and obtains the power quality assessment result of the target power system according to the power quality assessment value; the power quality assessment result is used to describe the power quality condition of the target power system.

[0208] It should be noted that, regarding the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part specifically, and will not be elaborated here.

[0209] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0210] As Figure 3 shown, an embodiment of the present application provides a terminal device. The terminal device D10 in this embodiment includes: at least one processor D100 ( Figure 3Only one processor is shown (), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100. When the processor D100 executes the computer program D102, the steps in any of the above method embodiments are implemented.

[0211] Specifically, when the processor D100 executes the computer program D102, it obtains the values of multiple power energy metrics of the target power energy system, then determines multiple target power energy metrics from all the power energy metrics, calculates the subjective weights of each target power energy metric using the analytic hierarchy process combined with the fuzzy theory, then calculates the objective weights of each target power energy metric using the entropy weight method, and finally calculates the power quality evaluation value of the target power energy system based on all the subjective weights, all the objective weights, and the values of all the target power energy metrics, and obtains the power quality evaluation result of the target power energy system according to the power quality evaluation value. Among them, obtaining the values of multiple power energy metrics expands the data source and improves the comprehensiveness of the data. Determining the target power energy metrics can improve the scientificity and effectiveness of the target power energy metrics. Calculating the subjective weights and objective weights can accurately and comprehensively express the weights of the target power energy metrics. Conducting power quality evaluation based on the subjective weights and objective weights can effectively improve the accuracy of the power quality evaluation.

[0212] The so-called processor D100 may be a central processing unit (CPU, Central Processing Unit), and this processor D100 may also be other general-purpose processors, digital signal processors (DSP, Digital Signal Processor), application specific integrated circuits (ASIC, Application Specific Integrated Circuit), field-programmable gate arrays (FPGA, Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0213] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as the hard disk or memory of the terminal device D10. In some other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk equipped on the terminal device D10, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory D101 may also include both the internal storage unit and the external storage device of the terminal device D10. The memory D101 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0214] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, which when executed by a processor, can implement the steps in the above-mentioned method embodiments.

[0215] An embodiment of the present application provides a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above-mentioned method embodiments.

[0216] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the power quality assessment method device / terminal device based on dynamic empowerment, a recording medium, a computer memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0217] In the above embodiments, the descriptions of the various embodiments each have their own emphasis. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0218] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0219] The above is the preferred implementation manner of this application. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle described in this application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. A power quality assessment method based on dynamic weighting, characterized in that: include: Obtaining values ​​of multiple power indicators of a target power system; Determine a plurality of target power indicators from all power indicators; The influence of each target power index on power quality is greater than the influence of all other power indexes on power quality; The subjective weight of each target power indicator is calculated by using the hierarchical analysis method combined with fuzzy theory; the subjective weight is used to describe the importance of the target power indicator in power quality assessment; Calculating the objective weight of each target electric energy indicator by using entropy weight method; The objective weight is used to describe the information content of the target power indicator in the power quality assessment; Based on the values ​​of all subjective weights, all objective weights and all target power indicators, a power quality assessment value of the target power system is calculated, and a power quality assessment result of the target power system is obtained according to the power quality assessment value; the power quality assessment result is used to describe the power quality status of the target power system.

2. The power quality assessment method according to claim 1, characterized in that: The method of calculating the subjective weight of each target electric energy index by using the hierarchical analysis method combined with fuzzy theory includes: Define multiple expert models, and use Bayesian theorem to calculate the evaluation value of each expert model for each target electric energy indicator; For each of the expert models, use the expert model to perform a secondary evaluation on each of the evaluation values ​​to obtain a fuzzy number for each evaluation value by the expert model; Aggregate all fuzzy numbers to obtain a fuzzy judgment matrix; the elements in the fuzzy judgment matrix are the final fuzzy numbers of each expert model for each target power index; Standardizing and transforming the fuzzy judgment matrix to obtain a fuzzy consistency matrix; The initial subjective weight of each target electric energy indicator is calculated according to the fuzzy consistency matrix, and each of the initial subjective weights is dynamically adjusted to obtain the subjective weight of each target electric energy indicator.

3. The power quality assessment method according to claim 2, characterized in that: The fuzzy judgment matrix is ​​obtained by aggregating all fuzzy numbers, including: By formula: Calculate the fuzzy judgment matrix A; Among them, K represents the number of expert models, represents the kth expert model for q ij The fuzzy number of the second evaluation, q ij represents the judgment value of the i-th expert model on the j-th target power index, and n represents the number of target power indexes.

4. The power quality assessment method according to claim 3, characterized in that: The fuzzy judgment matrix is ​​standardized and converted to obtain a fuzzy consistency matrix, including: By formula: Calculate the element r' in the fuzzy consistent matrix ij ; Among them, r' ij represents the final fuzzy number of the i-th expert model for the j-th target power index, r ij represents the standard fuzzy number of the i-th expert model for the j-th target power index, a ij is an element in the fuzzy judgment matrix, representing the initial fuzzy number of the i-th expert model for the j-th target power index, a zj represents the initial fuzzy number of the zth expert model for the jth target power index, r jK represents the standard fuzzy number of the K-th expert model for the j-th target electric energy index; The calculating the initial subjective weight of each target electric energy indicator according to the fuzzy consistency matrix includes: By formula: Calculate the initial subjective weight of the jth target power index The dynamically adjusting each of the initial subjective weights to obtain the subjective weight of each target electric energy indicator includes: By formula: Calculate the subjective weight w of the jth target power index sub,j ; in, represents the triangular fuzzy upper limit value corresponding to the j-th target power index, a j represents the triangular fuzzy lower limit value corresponding to the jth target power index, and x represents the interval The value inside, express The membership function of represents the dynamic subjective weight of the j-th target electric energy indicator, represents the normalized initial subjective weight of the jth target power index, c1 represents the credibility of the first expert model, c2 represents the credibility of the second expert model, and c K Represents the credibility of the Kth expert model.

5. The power quality assessment method according to claim 1, characterized in that: The calculating the power quality assessment value of the target power system based on the values ​​of all subjective weights, all objective weights and all target power indicators includes: Calculate the comprehensive weight of each target electric energy indicator according to the subjective weight and the objective weight corresponding to each target electric energy indicator; The values ​​of all target electric energy indicators are weighted and summed based on all comprehensive weights to obtain the electric energy quality assessment value of the target electric energy system.

6. The power quality assessment method according to claim 5, characterized in that: The step of calculating the comprehensive weight of the target electric energy index according to the subjective weight and the objective weight corresponding to each target electric energy index comprises: Introduce the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introduce the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight; According to the adjusted subjective weight and the adjusted objective weight corresponding to each of the target electric energy indicators, a fusion coefficient is introduced to calculate the candidate comprehensive weight of each of the target electric energy indicators; Calculate the difference value based on all adjusted objective weights and all adjusted subjective weights; Determine whether the difference value is less than or equal to a preset difference value; If so, each candidate comprehensive weight is used as the comprehensive weight of the corresponding target power index; Otherwise, adjust the values ​​of the subjective factor weight coefficient, the data characteristic influence coefficient and the fusion coefficient, and return to the step of introducing the subjective factor weight coefficient to adjust each objective weight to obtain the adjusted objective weight, and introduce the data characteristic influence coefficient to adjust each subjective weight to obtain the adjusted subjective weight.

7. The power quality assessment method according to claim 6, characterized in that: The subjective factor weight coefficient is introduced to adjust each objective weight to obtain the adjusted objective weight, including: By formula: w′ obj,j =a j ·w obj,j Calculate the adjusted objective weight w' obj,j ; Among them, w obj,j represents the objective weight of the j-th target electric energy index, α j represents the subjective factor weight coefficient corresponding to the j-th target power index, j = 1, 2, ..., n, and n represents the number of target power indexes; The data characteristic influence coefficient is introduced to adjust each subjective weight to obtain the adjusted subjective weight, including: By formula: w′ sub,j =w sub,j +β j ·(w obj,j -w sub,j ) Calculate the adjusted subjective weight w' sub,j ; Among them, w sub,j represents the subjective weight of the j-th target electric energy index, β j represents the data characteristic influence coefficient corresponding to the j-th target electric energy index; The step of introducing a fusion coefficient to calculate the candidate comprehensive weight of each target electric energy indicator according to the adjusted subjective weight and the adjusted objective weight corresponding to each target electric energy indicator comprises: By formula: w com,j =γ·w' sub,j +(1-γ)·w' obj,j Calculate the candidate comprehensive weight w of the jth target power index com,j ; Among them, γ represents the fusion coefficient, w' sub,j represents the adjusted subjective weight of the j-th target electric energy index, w' obj,j represents the adjusted objective weight of the j-th target electric energy indicator.

8. The power quality assessment method according to claim 7, characterized in that: The difference value is calculated based on all adjusted objective weights and all adjusted subjective weights, including: By formula: Calculate the difference value D; Wherein, n represents the number of target power indicators.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the power quality assessment method based on dynamic weighting according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the power quality assessment method based on dynamic weighting according to any one of claims 1 to 8 is implemented.

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