Topsis power quality comprehensive evaluation method and system for grid-connected new energy

Abstract

The application discloses a new energy grid-connected TOPSIS power quality comprehensive evaluation method and system, relates to the technical field of power systems, obtains characteristic parameters in new energy grid connection, and establishes a power quality evaluation index system with the characteristic parameters as indexes; the first weight of each index in the power quality evaluation index system is determined based on the fuzzy analytic hierarchy process; the second weight of the index is determined based on the CRITIC method, and the combined weight of each index is determined based on the first weight and the second weight; the improved TOPSIS model is used for comprehensive evaluation of the index weight after the combined weight; the scheme is based on the first weight and the second weight to reduce the dependence on a large number of accurate samples; and the final evaluation weight vector is determined, so that the evaluation result is more objective and accurate; meanwhile, the improved TOPSIS model is used for comprehensive evaluation of the index weight after the combined weight, and the applicability of linear correlation of each index is ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power systems, in particular to a TOPSIS power quality comprehensive evaluation method and system for new energy grid connection. BACKGROUND

[0002] Developing sustainable new energy has become a key to development, and new energy grid connection can convert solar energy and wind energy into electric energy by using advanced technology, thereby meeting the sustainable development demand for electric power. New energy grid connection makes the energy structure more diversified, and a new energy grid connection power generation system can replace a traditional power system. With new energy power generation systems being connected to the power grid in large scale year by year, due to the influence of weather changes, seasonal changes and geographical positions, new energy power generation has intermittency and uncertainty, and has great fluctuation in power generation, so that in actual grid connection, the operation of the entire distribution network will be obviously affected, and the power quality of the power grid is affected to a certain extent. Therefore, a scientific and rigorous evaluation of the power quality and the overall situation of the power quality of the power grid are of great significance to the research on the overall assessment of the power quality in the electric power market environment.

[0003] At present, the methods for evaluating the power quality at home and abroad mainly include the analytic hierarchy process, the fuzzy comprehensive evaluation method, the projection pursuit method, the neural network, the rank-sum ratio method, the probability statistics and vector algebra method, but the above methods have their own limitations: the fuzzy mathematics method improves the fuzziness of the evaluation index through the membership function, but the selection of the membership function is easily affected by subjective factors; the projection pursuit method has high accuracy but is complex and difficult to calculate; the intelligent algorithm modeling process is complex and depends on a large number of accurate samples; and in the rank transformation process, the rank-sum ratio method may lose part of the information, thereby affecting the evaluation result. Therefore, the evaluation of the power quality of the power grid after new energy grid connection needs further research. SUMMARY

[0004] The technical problem to be solved by the application is that the current methods for evaluating the power quality at home and abroad depend on a large number of accurate samples, and the accuracy of the evaluation result has great fluctuation, and the purpose of the application is to provide a TOPSIS power quality comprehensive evaluation method and system for new energy grid connection, the first weight of the index is calculated based on the fuzzy analytic hierarchy process, then the second weight of the index is determined by using the CRITIC method, thereby reducing the dependence on a large number of accurate samples; the first weight and the second weight are combined to determine the final evaluation weight vector, so that the evaluation result is more objective and accurate; and the improved TOPSIS model is used for comprehensive evaluation of the index weight after combination weighting, thereby ensuring the applicability of the linear correlation of each index.

[0005] The application is implemented by the following technical scheme:

[0006] The scheme provides a TOPSIS power quality comprehensive evaluation method for new energy grid connection, comprising:

[0007] Step one: obtaining characteristic parameters in new energy grid connection, and establishing a power quality evaluation index system with the characteristic parameters as indexes;

[0008] Step two: determining the first weight of each index in the power quality evaluation index system based on a fuzzy analytic hierarchy process;

[0009] Step three: determining the second weight of the index based on a CRITIC method, and combining the first weight and the second weight to weight each index;

[0010] Step four: comprehensively evaluating the index weight after combination weighting by using an improved TOPSIS model.

[0011] The working principle of the scheme is as follows: current domestic and foreign methods for power quality evaluation depend on a large number of accurate samples, and the accuracy of the evaluation results is volatile, the purpose of the present application is to provide a TOPSIS power quality comprehensive evaluation method for new energy grid connection, the first weight of the index is calculated based on a fuzzy analytic hierarchy process, then the second weight of the index is determined by using a CRITIC method, thereby reducing the dependence on a large number of accurate samples; the first weight and the second weight are combined to determine the final evaluation weight vector, so that the evaluation result is more objective and accurate; meanwhile, the index weight after combination weighting is comprehensively evaluated by using an improved TOPSIS model, thereby ensuring the applicability of linear correlation of each index.

[0012] Further optimization scheme is that the characteristic parameters comprise: three-phase voltage unbalance degree X εu , voltage total harmonic distortion rate X THDu , absolute value of voltage deviation X eu , short-time voltage flicker X Pst , absolute value of frequency deviation X f , and voltage fluctuation X du .

[0013] Further optimization scheme is that the establishment method of the power quality evaluation index system comprises:

[0014] three-phase voltage unbalance degree X εu and absolute value of voltage deviation X eu are taken as indexes of the amplitude quality evaluation system;

[0015] voltage total harmonic distortion rate X THDu , voltage fluctuation X du , and voltage fluctuation X du are taken as indexes of the waveform quality evaluation system;

[0016] The absolute value X of the frequency deviation f as an index of the frequency quality evaluation system.

[0017] Further optimization scheme is that the first weight determination method of each index comprises:

[0018] Establishing an evaluation matrix R of each index in the power quality evaluation index system: comparing each index in the power quality evaluation index system with each other, measuring the importance between two indexes based on the scale method to obtain the evaluation matrix;

[0019] Determine the type of the evaluation matrix R: if 0≤r ij ≤1, then R is a fuzzy matrix;

[0020] If r ij +r ji =1, then R is a fuzzy complementary matrix;

[0021] If i,j,k∈n, r ij =r ik -r jk +0.5, then R is a fuzzy consistent matrix;

[0022] Where n×n represents that the evaluation matrix R is an n×n matrix, r ij represents the element of the i-th row and the j-th column in the evaluation matrix R; r ji represents the element of the j-th row and the i-th column in the evaluation matrix R;

[0023] Based on the fuzzy complementary matrix, the first weight of each index is calculated.

[0024] Further optimization scheme is that the first weight calculation method of each index comprises:

[0025] The fuzzy complementary matrix R l of the index l is scored p times by threshold, that is:

[0026]

[0027] Wherein, represents the fuzzy relationship of the i-th index relative to the j-th index in the index layer l, and is the average value of p times threshold scoring; n represents the number of indexes in the index layer;

[0028] The fuzzy complementary matrix is aggregated R ij(n×n) , which is:

[0029]

[0030]

[0031] Wherein, r iSumming up the matrix R by row; r ij denotes the element in the fuzzy consistent matrix;

[0032] The subjective weight of the index layer l is wherein

[0033]

[0034]

[0035] γ represents the margin.

[0036] The further optimization scheme is that the determination method of the second weight comprises:

[0037] Taking the positive index and the reverse index as two variables, and on the basis of the deviation of the two variables from the respective average values, a linear single correlation coefficient is obtained by multiplying the two deviations to reflect the correlation degree between the two variables;

[0038]

[0039] wherein: d ij denotes the mutual relationship between the ith index and the jth index, and i=1, 2, …m, n; j=1, 2, …, n; the obtained correlation coefficient matrix is n rows and n columns; z ij denotes the positive index; z ik denotes the reverse index; and is the average value of the positive index data and the average value of the reverse index data;

[0040] Based on the evaluation index correlation coefficient matrix, the comparison intensity S between indexes is obtained j , the conflictiveness R of the index j and the information amount C j :

[0041]

[0042]

[0043] The information amount C j is:

[0044] C j =S j ×R j

[0045] wherein the larger C j is, the larger the information amount contained by the index is;

[0046] Based on the information amount C j , the objective weight of each index is obtained:

[0047]

[0048] The second weight of the evaluation index is

[0049] The acquisition method of the positive index and the negative index includes the following processes:

[0050] Form the evaluation sample matrix X based on the evaluation object and the index, and the matrix element is x ij Based on the min-max normalization method, the positive index and the negative index are respectively normalized by different dimensionless methods, i.e.:

[0051] Positive index:

[0052]

[0053] Negative index:

[0054]

[0055] Wherein, i=1, 2, …, m; j=1, 2, …, n.

[0056] The further optimization scheme is that the method of combination weighting includes:

[0057] The combination weight vector is calculated as w=(w1, w2, L, wn), n

[0058] That is:

[0059] The further optimization scheme is that step four includes the following sub-steps:

[0060] Step 4.1: Obtain the evaluation sample matrix and normalize the evaluation sample matrix;

[0061] Step 4.2: Determine the positive ideal solution A+ and the negative ideal solution A-, and use the cosine of the angle to solve the distance from the jth evaluation scheme to the ideal solution, i.e.

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Wherein: i=1, 2, …, m; j=1, 2, … n; x c ​Indicates the mean value of positive and negative ideal solutions. Indicates the optimal value of each index. i - Indicates the worst value of each index.

[0068] Step 4.3: Calculate the relative closeness G of each object to be evaluated i That is

[0069]

[0070] Generally, the TOPSIS comprehensive evaluation method adopts the Euclidean distance, and when processing linearly correlated indexes, the correlation closeness has certain defects, therefore, the scheme proposes a cosine similarity method to solve the linear correlation problem, which can enhance the applicability of the TOPSIS comprehensive evaluation method when facing the linear correlation problem of indexes. Since there is usually a certain linear correlation between evaluation indexes, the TOPSIS method usually adopts the Euclidean distance to measure the distance between each evaluation scheme and the ideal scheme, which will cause the Euclidean distance to be unreliable. Therefore, the present application adopts the cosine similarity to solve the problem of linear correlation between evaluation indexes. The cosine similarity is a distance measurement method based on the cosine of the angle to calculate the distance between the evaluation scheme and the ideal scheme. The smaller the angle between two vectors, the greater the similarity, that is, the shorter the distance between the two vectors.

[0071] The scheme also provides a TOPSIS power quality comprehensive evaluation system for new energy grid connection, which is used to realize the power quality evaluation method based on fuzzy AHP and combined weighting described in the above scheme, and includes:

[0072] A preprocessing module is configured to acquire characteristic parameters in new energy grid connection, and establish a power quality evaluation index system taking the characteristic parameters as indexes.

[0073] A first calculation module is configured to determine the first weight of each index in the power quality evaluation index system based on the fuzzy AHP.

[0074] A second calculation module is configured to determine the second weight of the index based on the CRITIC method, and combine the first weight and the second weight to weight each index.

[0075] An evaluation module is configured to comprehensively evaluate the index weight after combined weighting by using the improved TOPSIS model.

[0076] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0077] The application provides a new energy grid-connected TOPSIS power quality comprehensive evaluation method and system, the first weight of indexes is calculated based on a fuzzy analytic hierarchy process, then the second weight of indexes is determined by using a CRITIC method, the dependence on a large number of accurate samples is reduced, the first weight and the second weight are combined to determine the final evaluation weight vector, so that the evaluation result is more objective and accurate, and the improved TOPSIS model is used for comprehensive evaluation on the index weight after combination weighting, and the applicability of linear correlation of indexes is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical scheme of the exemplary embodiments of the application, the drawings needed to be used in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope, and other related drawings can also be obtained by those skilled in the art without creative labor on the premise of the drawings.

[0079] In the drawings:

[0080] Figure 1 A flowchart of a new energy grid-connected TOPSIS power quality comprehensive evaluation method;

[0081] Figure 2 A schematic diagram of a power quality evaluation index system. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical scheme and advantages of the application more clear and apparent, the application will be further described in detail below in combination with embodiments and drawings, the exemplary embodiments of the application and the description thereof are only used to explain the application, and should not be regarded as a limitation on the application.

[0083] Embodiment 1

[0084] The embodiment provides a new energy grid-connected TOPSIS power quality comprehensive evaluation method, as shown in Figure 1 , which comprises:

[0085] Step 1: Obtain the characteristic parameters in the new energy grid connection, and establish a power quality evaluation index system with the characteristic parameters as indexes;

[0086] The characteristic parameters include: three-phase voltage unbalance degree X εu , voltage total harmonic distortion rate X THDu , absolute value of voltage deviation X eu , short-time voltage flicker X Pst , absolute value of frequency deviation X f and voltage fluctuation X du .

[0087] As shown in Figure 2 The method for establishing the power quality evaluation index system comprises the following steps:

[0088] Taking the three-phase voltage unbalance degree X εu and the absolute value of voltage deviation X eu as the indicators of the amplitude quality evaluation system;

[0089] Taking the voltage total harmonic distortion rate X THDu , voltage fluctuation X du and voltage fluctuation X du as the indicators of the waveform quality evaluation system;

[0090] Taking the absolute value of frequency deviation X f as the indicator of the frequency quality evaluation system.

[0091] Step 2: determining the first weight of each indicator in the power quality evaluation index system based on the fuzzy analytic hierarchy process;

[0092] The method for determining the first weight of each indicator comprises the following steps:

[0093] Establishing the evaluation matrix R of each indicator in the power quality evaluation index system: comparing each indicator in the power quality evaluation index system two by two, and measuring the importance between the two indicators based on the scale method to obtain the evaluation matrix;

[0094] The (0.1-0.9) scale method is used to measure the importance between the two indicators, as shown in Table 1.

[0095] Table 1 meaning of (0.1-0.9) scale method

[0096]

[0097] Determining the type of the evaluation matrix R: if 0≤r ij ≤1, then R is a fuzzy matrix;

[0098] If r ij +r ji =1, then R is a fuzzy complementary matrix;

[0099] If i, j, k∈n, r ij =r ik -r jk +0.5, then R is a fuzzy consistent matrix; when R is a fuzzy consistent matrix, the following formula is satisfied:

[0100]

[0101]

[0102] wherein n x n represents that the evaluation matrix R is an n x n matrix, r ij represents the element in the i-th row and j-th column of the evaluation matrix R; r ji represents the element in the j-th row and i-th column of the evaluation matrix R;

[0103] The first weight of each index is calculated based on the fuzzy complementary matrix.

[0104] The first weight calculation method of each index comprises:

[0105] The fuzzy complementary matrix R l of the index l is subjected to p times threshold scoring, i.e.:

[0106]

[0107] wherein, represents the fuzzy relationship of the i-th index of the index layer l with respect to the j-th index, and is the average value of the p times threshold scoring; n represents the number of indexes of the index layer;

[0108] The fuzzy complementary matrix is aggregated R ij(n×n) , and is:

[0109]

[0110]

[0111] wherein r i is the sum of the matrix R by row; r ij represents the element in the fuzzy consistent matrix;

[0112] The subjective weight of the index layer l is wherein

[0113]

[0114]

[0115] γ represents a margin.

[0116] Step three: determining the second weight of the index based on the CRITIC method, and combining the weights of each index based on the first weight and the second weight;

[0117] The determination method of the second weight comprises:

[0118] Taking the positive index and the inverse index as two variables, and based on the deviation of the two variables from the respective average values, a linear single correlation coefficient is obtained by multiplying the two deviations to reflect the correlation degree between the two variables;

[0119]

[0120] Wherein: d ij represents the correlation between the ith index and the jth index, and i = 1, 2, … m, n; j = 1, 2, …, n; the obtained correlation coefficient matrix is n rows and n columns; z ij represents a positive index; z ik represents a negative index; and are the average of the positive index data and the average of the negative index data;

[0121] Based on the evaluation index correlation coefficient matrix, the contrast intensity S j between the indexes is obtained. j The conflict R j of the index and the information amount C j :

[0122]

[0123]

[0124] The information amount C j is:

[0125] C j = S j × R

[0126] Wherein, the larger C j , the greater the information amount contained in the index;

[0127] Based on the information amount C j , the objective weight of each index is obtained:

[0128]

[0129] The second weight of the evaluation index is

[0130] The acquisition method of the positive index and the negative index includes the following process:

[0131] Suppose that the present application has m evaluation objects and n evaluation indexes. Form an evaluation sample matrix X, and use the min-max normalization method. According to the difference between the positive and negative indexes, different dimensionless methods are used,

[0132] Based on the evaluation object and the index, an evaluation sample matrix X is formed, and the matrix element is x ij . Based on the min-max normalization method, the positive and negative indexes are respectively used by different dimensionless methods, that is:

[0133] Positive index:

[0134]

[0135] Contrarian indicator:

[0136]

[0137] Where i = 1, 2, ..., m; j = 1, 2, ..., n.

[0138] Methods of combined weighting include:

[0139] The combined weight vector is calculated as w = (w1, w2, ..., w n ),Right now:

[0140] Step 4: Use the improved TOPSIS model to comprehensively evaluate the weights of the combined weighted indicators.

[0141] Step four includes the following sub-steps:

[0142] Step 4.1: Obtain the evaluation sample matrix and normalize it;

[0143] Step 4.2: Determine the positive ideal solution A+ and the negative ideal solution A-, and use the cosine of the included angle to calculate the distance from the j-th solution to the ideal solution, i.e.:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Where: i = 1, 2, ..., m; j = 1, 2, ..., n; x c This represents the mean of the positive and negative ideal solutions; This represents the optimal value for each indicator; This indicates the worst value for each indicator.

[0150] Step 4.3: Calculate the relative proximity G of each object to be evaluated. i ,Right now

[0151]

[0152] Example 2

[0153] The TOPSIS power quality comprehensive assessment system for new energy grid connection in this embodiment is used to implement the power quality assessment method based on fuzzy hierarchical analysis and combined weighting described in the previous embodiment, including:

[0154] a preprocessing module configured to acquire characteristic parameters in grid connection of new energy and establish an electric energy quality evaluation index system with the characteristic parameters as indexes;

[0155] a first calculation module configured to determine first weights of indexes in the electric energy quality evaluation index system based on a fuzzy analytic hierarchy process;

[0156] a second calculation module configured to determine second weights of the indexes based on a CRITIC method and to combine the first weights and the second weights to weight the indexes;

[0157] an evaluation module configured to comprehensively evaluate the index weights after the combination weighting by using an improved TOPSIS model.

[0158] The above detailed description further explains the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific implementation of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A TOPSIS comprehensive power quality assessment method for grid-connected new energy sources, characterized in that, include: Step 1: Obtain the characteristic parameters of new energy grid connection, and establish a power quality assessment index system based on the characteristic parameters; Step 2: Determine the first weight of each indicator in the power quality assessment index system based on fuzzy hierarchical analysis; The methods for determining the first weight of each indicator include: Establish the evaluation matrix R for each indicator in the power quality evaluation index system: compare each indicator in the power quality evaluation index system pairwise, and measure the importance between the two indicators based on the scaling method to obtain the evaluation matrix; Determine the type of the evaluation matrix R: if 0 ≤ r ij If ≤1, then R is a fuzzy matrix; If r is satisfied ij +r ji =1; then R is a fuzzy complementary matrix; If ∀ i, j, k ∈ n, r ij =r ik -r jk +0.5; then R is the fuzzy consistency matrix; Where n×n represents that the evaluation matrix R is an n×n dimensional matrix, r ij This represents the element in the i-th row and j-th column of the evaluation matrix R; r ji This represents the element in the j-th row and i-th column of the evaluation matrix R; The first weight of each indicator is calculated based on the fuzzy complementary matrix. The calculation methods for the first weight of each indicator include: For indicators Fuzzy complementary matrix Performing p threshold scoring operations results in: ； in, Indicator layer The fuzzy relationship between the i-th indicator and the j-th indicator is the average of the p threshold scores; n represents the number of indicators in the indicator layer. Aggregation of fuzzy complementary matrices R ij(n×n) ,for: ； ； in, Sum the rows of matrix R; Represents the elements in the fuzzy consistency matrix; Indicator layer Subjective weight is ;in ； ； Indicates margin; Step 3: Determine the second weight of the indicators based on the CRITIC method, and assign combined weights to each indicator based on the first and second weights; The methods for determining the second weight include: Using positive and negative indicators as two variables, and the deviations of the two variables from their respective averages as a basis, the correlation coefficient between the two variables is obtained by multiplying the two deviations. ； Where: d ij This represents the relationship between the i-th indicator and the j-th indicator, where i = 1, 2, ..., m, n; j = 1, 2, ..., n; the obtained correlation coefficient matrix is ​​n rows and n columns. Indicates a positive indicator; Indicates a contrarian indicator; and This represents the average of the positive and negative indicator data. The comparison strength S between indicators is obtained based on the correlation coefficient matrix of the evaluation indicators. j Conflict of indicators R j and information content : ； ； Information content for: ； in The larger the value, the more information the indicator contains; Based on information content Obtain the objective weights of each indicator: ； The second weight of the evaluation index is then... ; Step 4: Use the improved TOPSIS model to comprehensively evaluate the weights of the combined weighted indicators.

2. The TOPSIS power quality comprehensive assessment method for new energy grid connection according to claim 1, characterized in that, The characteristic parameters include: three-phase voltage imbalance X εu Total harmonic distortion of voltage X THDu The absolute value of voltage deviation X eu Short-time voltage flicker X Pst The absolute value of frequency deviation X f and voltage fluctuation X du .

3. The TOPSIS power quality comprehensive assessment method for new energy grid connection according to claim 2, characterized in that, The methods for establishing a power quality assessment index system include: The three-phase voltage imbalance X εu The absolute value of the voltage deviation X eu As an indicator in the amplitude quality assessment system; The total harmonic distortion of voltage X THDu Voltage fluctuation X du and voltage fluctuation X du As an indicator in the waveform quality assessment system; The absolute value of the frequency deviation X f As an indicator in the frequency quality assessment system.

4. The TOPSIS power quality comprehensive assessment method for new energy grid connection according to claim 2, characterized in that, The methods for obtaining positive and negative indicators include the following process: An evaluation sample matrix X is formed based on the evaluation objects and indicators, with matrix elements as follows: Based on the min-max normalization method, the positive and negative indices are represented by different dimensionless methods, namely: Positive indicators: ； Contrarian indicator: ； Where i = 1, 2, ..., m; j = 1, 2, ..., n.

5. The TOPSIS power quality comprehensive assessment method for new energy grid connection according to claim 2, characterized in that, Methods of combined weighting include: Calculate the combined weight vector as follows: ,Right now: .

6. The TOPSIS power quality comprehensive assessment method for new energy grid connection according to claim 1, characterized in that, Step four includes the following sub-steps: Step 4.1: Obtain the evaluation sample matrix and normalize it; Step 4.2: Determine the positive ideal solution A+ and the negative ideal solution A-, and use the cosine of the included angle to calculate the distance from the j-th solution to the ideal solution, i.e.: ； ； ； Where: i = 1, 2, ..., m; j = 1, 2, ..., n; This represents the mean of the positive and negative ideal solutions; This represents the optimal value for each indicator; This indicates the worst-case value for each indicator; Step 4.3: Calculate the relative proximity G of each evaluation object. i ,Right now 。 7. A TOPSIS power quality comprehensive assessment system for grid-connected new energy sources, characterized in that, The power quality assessment method based on fuzzy hierarchical analysis and combined weighting as described in any one of claims 1-6 includes: The preprocessing module is used to obtain characteristic parameters of new energy grid connection and to establish a power quality assessment index system based on the characteristic parameters. The first calculation module is used to determine the first weight of each indicator in the power quality assessment index system based on the fuzzy hierarchical analysis method. The second calculation module determines the second weight of the indicators based on the CRITIC method, and assigns weights to each indicator based on the first weight and the second weight. The evaluation module is used to comprehensively evaluate the weights of the combined weighted indicators using an improved TOPSIS model.

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