Electric energy quality comprehensive evaluation method and system based on improved CRITIC weight

Abstract

The invention provides an electric energy quality comprehensive evaluation method and system based on an improved CRITIC weight, and the method comprises the following steps: building electric energy quality monitoring points, and selecting the same number of evaluation indexes for each electric energy quality monitoring point; in combination with the quartile and the improved distance correlation coefficient, improving a CRITIC weight calculation method; calculating the weight of each evaluation index of each power quality monitoring point based on an improved CRITIC weight calculation method; based on the weight of each evaluation index, constructing an electric energy quality comprehensive evaluation model; and based on the electric energy quality comprehensive evaluation model, sorting and rating the electric energy quality monitoring points to obtain an evaluation result, and completing the comprehensive evaluation of the electric energy quality. The electric energy quality comprehensive evaluation model constructed by the method has relatively high feasibility and robustness.

Description

Technical Field

[0001] The present invention belongs to the technical field of power quality evaluation, and in particular relates to a comprehensive power quality evaluation method and system based on improved CRITIC weights. Background Art

[0002] In recent years, problems and accidents caused by power quality have been on the rise, and economic losses caused by unqualified power quality are also increasing. The management of power quality and the control of power pollution are imperative. Comprehensive evaluation of power quality is one of the key measures to ensure the stable operation of the power system and improve the reliability of power supply. At present, many scholars have conducted research on comprehensive evaluation of power quality and risk assessment of distribution network equipment, mainly focusing on weight selection and model characteristics. When conducting comprehensive evaluation of power quality, different weight selections will affect the final evaluation results, thereby reducing the credibility of the evaluation results. The weighting of each evaluation index is often involved in the complex multi-criteria decision-making evaluation process. The weight of the index reflects the decision maker's preference for different decision criteria. Determining the correct weight can better reflect the decision maker's intention and goals. The reasonable allocation of weights is the key to scientific evaluation. Different index weighting methods directly affect the evaluation results. If the weight allocation is unreasonable or inaccurate, it may cause the decision result to deviate from the actual situation and affect the effectiveness of the decision. Therefore, how to determine the index weight is an important issue.

[0003] The traditional CRITIC weight calculation method uses the dimensionless standard deviation to reflect the variability between indicators. Combining the coefficient of variation and the distance correlation coefficient, a natural idea is to use the coefficient of variation and the distance correlation coefficient to measure the variability and conflict of indicators respectively. However, the coefficient of variation and the distance correlation coefficient are more sensitive to outliers, resulting in unstable weight calculation results and low robustness. In view of this, the present invention proposes a CRITIC weight calculation method based on quartiles and improved distance correlation coefficients. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a comprehensive power quality evaluation method and system based on improved CRITIC weights, and constructs a comprehensive power quality evaluation model with high feasibility and robustness.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The comprehensive evaluation method of power quality based on improved CRITIC weight includes the following steps:

[0007] Establish power quality monitoring points and select the same number of evaluation indicators for each power quality monitoring point;

[0008] The CRITIC weight calculation method is improved by combining quartiles and improved distance correlation coefficient;

[0009] Based on the improved CRITIC weight calculation method, the weight of each evaluation index of each power quality monitoring point is calculated;

[0010] Based on the weights of the various evaluation indicators, a comprehensive power quality evaluation model is constructed;

[0011] Based on the comprehensive power quality evaluation model, the power quality monitoring points are ranked and rated to obtain evaluation results, thereby completing the comprehensive evaluation of power quality.

[0012] Preferably, the evaluation indicators include: voltage deviation I1, voltage fluctuation I2, voltage flicker I3, voltage harmonics I4, three-phase imbalance I5, frequency deviation I6, voltage sag I7, power supply reliability I8 and serviceability index I9.

[0013] Preferably, the method for obtaining the weight of each evaluation index of each power quality monitoring point is:

[0014] Based on the quartiles, the variability of each evaluation index was calculated;

[0015] Based on the distance correlation coefficient between the evaluation indicators, the conflict of each evaluation indicator is obtained;

[0016] Based on the variability and conflict of each evaluation indicator, calculating the information carrying capacity between the evaluation indicators;

[0017] Based on the information carrying capacity, the weight of each evaluation index of each power quality monitoring point is obtained.

[0018] Preferably, based on the quartiles, the method for calculating the variability of each evaluation index is:

[0019] The interquartile range and median are used to replace the standard deviation and mean in the coefficient of variation. The formula is as follows:

[0020]

[0021] in They represent the third quartile and the first quartile of the j-th evaluation index respectively. is the second quartile, representing the median, IQR j is the interquartile range of the jth evaluation index.

[0022] Preferably, the method for obtaining the conflict of each evaluation index is:

[0023] Based on the distance between the observed values ​​of each evaluation index, the dual center distance matrix is ​​calculated:

[0024] Based on the dual-center distance matrix, calculating the distance covariance between the evaluation indicators; based on the distance covariance, calculating the distance correlation coefficient between the evaluation indicators;

[0025] Based on the distance correlation coefficient, obtaining a distance correlation coefficient matrix;

[0026] Based on the distance correlation coefficient matrix, the conflict of each evaluation index is obtained.

[0027] Preferably, based on the weights of the various evaluation indicators, a method for constructing a comprehensive power quality evaluation model is:

[0028] Constructing a decision matrix based on the power quality monitoring points and the observed values ​​of the evaluation indicators;

[0029] Convert all evaluation indicators into positive indicators and make the positive indicators dimensionless;

[0030] Based on the decision matrix after dimensionless transformation of all positive indicators and the weight of each evaluation indicator of each power quality monitoring point, a weighted normalized decision matrix is ​​obtained;

[0031] Based on the weighted normalized decision matrix, obtaining positive and negative ideal solutions for each evaluation indicator;

[0032] Calculating the distance between each power quality monitoring point and the positive and negative ideal solutions, and calculating the evaluation score of each power quality monitoring point based on the distance;

[0033] Based on the evaluation scores, the power quality monitoring points are sorted and graded to complete the construction of a comprehensive power quality evaluation model.

[0034] Preferably, the method for obtaining the positive and negative ideal solutions of each evaluation index is: based on the weighted normalized decision matrix, taking the maximum value of each evaluation index as the positive ideal solution, and taking the minimum value of each evaluation index as the negative ideal solution.

[0035] The present invention also provides a comprehensive power quality evaluation system based on improved CRITIC weights, which is used to implement the method, comprising:

[0036] An evaluation index selection module is used to establish power quality monitoring points and select the same number of evaluation indicators for each power quality monitoring point;

[0037] Improved module, used to improve the CRITIC weight calculation method by combining quartiles and improved distance correlation coefficient;

[0038] The weight calculation module is used to calculate the weight of each evaluation index of each power quality monitoring point based on the improved CRITIC weight calculation method;

[0039] An evaluation model building module, used to build a comprehensive power quality evaluation model based on the weights of the various evaluation indicators;

[0040] The comprehensive evaluation module is used to rank and rate the power quality monitoring points based on the power quality comprehensive evaluation model, obtain evaluation results, and complete the comprehensive evaluation of power quality.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses the quotient of the quartile distance and the median to measure the variability between indicators, and then uses the improved distance correlation coefficient to calculate the conflict between indicators, effectively improving the feasibility and robustness of the comprehensive evaluation model of power quality. By quantitatively analyzing and comprehensively evaluating power quality, power quality problems can be discovered and handled in a timely manner, the operation of the power system can be optimized, the power supply quality and reliability can be improved, the equipment damage rate and maintenance cost can be reduced, and the economic and social benefits of the enterprise can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 The present invention is a flowchart of a comprehensive power quality evaluation method based on improved CRITIC weights according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Embodiment 1

[0047] like Figure 1 As shown, the comprehensive evaluation method of power quality based on improved CRITIC weight includes the following steps:

[0048] S1: Establish power quality monitoring points, and select the same number of evaluation indicators for each power quality monitoring point; a further implementation method is that the evaluation indicators include: voltage deviation I1, voltage fluctuation I2, voltage flicker I3, voltage harmonics I4, three-phase imbalance I5, frequency deviation I6, voltage sag I7, power supply reliability I8 and serviceability index I9. According to the national power quality standards, combined with the above power quality index system data, the power quality is roughly divided into five levels: excellent, good, medium, qualified and unqualified, represented by Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ respectively. The value range of each level data is obtained by combining the above selected 3 positive indicators and 6 negative indicators data as shown in Table 1.

[0049] Table 1

[0050]

[0051] Based on the observation values ​​of power quality monitoring points and evaluation indicators, a decision matrix is ​​constructed; specifically, assuming there are m power quality monitoring points A1, A2, ..., A m Each power quality monitoring point has n indicators C1, C2, ..., C n , arrange the n index values ​​of m power quality monitoring points to get the decision matrix:

[0052]

[0053] S2: Improve the CRITIC weight calculation method by combining quartiles and improved distance correlation coefficient;

[0054] S3: Based on the improved CRITIC weight calculation method, the weight of each evaluation index of each power quality monitoring point is calculated;

[0055] A further implementation method is that the method for obtaining the weight of each evaluation index of each power quality monitoring point is:

[0056] The distance correlation coefficient has a simple form of estimator. Assuming there are m power quality monitoring points, for any two evaluation indicators C j ,C j′ , define its dual center distance matrices A and B as:

[0057]

[0058] in

[0059] a ih =||C ij -C hj || p ,i,h=1,2,…,m (1)

[0060] bih =||C ij′ -C hj′ ‖ q ,i,h=1,2,…,m (2)

[0061] Index C j With indicator C j′ The distance covariance between them is calculated as follows:

[0062]

[0063] Therefore, indicator C j With indicator C j′ The distance correlation coefficient can be calculated according to the following formula:

[0064]

[0065] Experiments have found that the coefficient of variation and distance correlation coefficient are more sensitive to outliers. To solve this problem, we use the following method to improve them:

[0066] Based on the quartiles, the variability of each evaluation index was calculated;

[0067] The coefficient of variation is calculated as follows:

[0068]

[0069] Where σ j represents the standard deviation of the jth indicator in the decision matrix, μ j Represents the mean of the jth indicator in the decision matrix.

[0070] In this embodiment, the interquartile range and median are used to replace the standard deviation and mean in the coefficient of variation, and the formula is as follows:

[0071]

[0072] in They represent the third quartile and the first quartile of the j-th evaluation index respectively. is the second quartile, representing the median, IQR j is the interquartile range of the jth evaluation index.

[0073] Let α, β∈R n ,definition

[0074] d(α,β)=log(1+||α-β|| p ),

[0075] where ||α-β|| prepresents a certain distance between α and β, then d(α, β) is also a distance between α and β. The proof is given below:

[0076] (1) Non-negativity: Because ||α-β|| p ≥0, so 1+||α-β|| p ≥1, so

[0077] d(α,β)=log(1+||α-β|| p )≥0.

[0078] (2) Symmetry: Because ||α-β|| p =||β-α|| p , a simple calculation shows that

[0079]

[0080] (3) Triangle inequality: Because

[0081] ||α-β|| p ≤||α-γ|| p +||β-γ|| p

[0082] so

[0083] 1+||α-β|| p ≤1+||α-γ|| p +||β-γ|| p ,

[0084] therefore

[0085]

[0086] Notice

[0087] log(1+||α-γ|| p )+log(1+||β-γ|| p )=log(1+||α-γ|| p +||β-γ|| p +||α-γ|| p ×β-γ|| p )

[0088] log(1+||α-β|| p )≤log(1+||α-γ|| p )+log(1+||β-v|| p ).

[0089] In summary, d(α, β) = log(1+||α-β|| p) satisfies the three properties of distance.

[0090] In order to improve the robustness of the distance correlation coefficient, the formulas (1) and (2) for calculating the distance matrix in the distance correlation coefficient are improved as follows:

[0091] a ih =log(1+||C ij -C hj ‖ p ) (3)

[0092] b ih =log(1+|| Cij ′-C hj′ ‖ q ) (4)

[0093] Because (3) and (4) represent the distances between indicator observations, after replacing (1) and (2) with (3) and (4) respectively, the estimation theorem of distance correlation coefficient also holds. Therefore, the center distance matrix obtained by (3) and (4) can also be used to estimate the distance correlation coefficient.

[0094] Based on the distance correlation coefficient between the evaluation indicators, the conflict of each evaluation indicator is obtained;

[0095] Based on the variability and conflict of each evaluation indicator, the information carrying capacity between the evaluation indicators is calculated;

[0096] Based on the information carrying capacity, the weight of each evaluation index of each power quality monitoring point is obtained.

[0097] A further implementation method is to obtain the conflict of each evaluation index by:

[0098] Based on the distance between the observed values ​​of each evaluation index, the dual center distance matrix is ​​calculated:

[0099] Based on the dual-center distance matrix, calculating the distance covariance between the evaluation indicators; based on the distance covariance, calculating the distance correlation coefficient between the evaluation indicators;

[0100] Based on the distance correlation coefficient, a distance correlation coefficient matrix is ​​obtained;

[0101] Based on the distance correlation coefficient matrix, the conflict of each evaluation index is obtained.

[0102] Specifically, in this embodiment, the improved distance correlation coefficient is used to measure the correlation between indicators. The larger the value, the stronger the correlation. The distance correlation coefficient dr jj′ The calculation formula is as follows:

[0103]

[0104] in

[0105]

[0106] In the above formula, matrix A and matrix B are dual-center distance matrices:

[0107]

[0108]

[0109] in

[0110] a ih =log(1+||C ij -C hj || p ),

[0111] b ih =log(1+‖C ij′ -C hj′ ‖ q ),

[0112] Calculated by dr jj′ After the distance correlation coefficient matrix is ​​formed, the index conflict R is calculated based on the distance correlation coefficient matrix. j , the calculation formula is as follows:

[0113]

[0114] Calculate the information carrying capacity between indicators. Information carrying capacity ICC j The larger the value of , the more information it contains.

[0115]

[0116] Determine the indicator weight. The calculation formula of the indicator weight is as follows:

[0117]

[0118] S4: Based on the weights of various evaluation indicators, a comprehensive evaluation model for power quality is constructed;

[0119] A further implementation method is to construct a comprehensive power quality evaluation model based on the weights of various evaluation indicators:

[0120] Based on the observation values ​​of power quality monitoring points and evaluation indicators, a decision matrix is ​​constructed; specifically, assuming there are m power quality monitoring points A1, A2, ..., A m Each power quality monitoring point has n indicators C1, C2, ..., C n, arrange the n index values ​​of m power quality monitoring points to get the decision matrix:

[0121]

[0122] All evaluation indicators are converted into positive indicators, and the positive indicators are dimensionless. Specifically, for the negative indicators, the following method can be used for positive conversion:

[0123] y ij =Mx ij or

[0124] Where M is x ij For the intermediate indicators to be positive, the following formula can be used for positive conversion:

[0125]

[0126] Where M is |x best -x ij The maximum value of |, x best For x ij When dealing with the positive conversion of interval indicators, the following formula can be applied for conversion:

[0127] M = max{a-min(x ij ),max(x ij )-b}

[0128]

[0129] Where a and b are the lower and upper limits of the interval respectively.

[0130] The data is processed dimensionlessly to avoid the impact caused by the difference in the dimension and order of magnitude of the indicator. The indicator is normalized using the range method, and the processed data is between 0 and 1. The processing method is as follows:

[0131]

[0132] Among them, y jmin and jmax They represent the minimum and maximum values ​​of the evaluation object in the jth indicator respectively.

[0133] Based on the dimensionless decision matrix of all positive indicators and the weights of each evaluation indicator at each power quality monitoring point, a weighted normalized decision matrix is ​​obtained; in this embodiment, the weight vector w=(w1,w2,...w n ), The weighted normalized decision matrix V is calculated according to the following formula:

[0134]

[0135] Based on the weighted normalized decision matrix, the positive and negative ideal solutions of each evaluation index are obtained; a further implementation method is that the method for obtaining the positive and negative ideal solutions of each evaluation index is: based on the weighted normalized decision matrix, the maximum value of each evaluation index is taken as the positive ideal solution, and the minimum value of each evaluation index is taken as the negative ideal solution. Specifically, all indicators are extremely large indicators and have a unified value range, and the maximum value of each indicator is taken as the positive ideal solution:

[0136]

[0137] Similarly, the minimum value of each indicator is taken as the negative ideal solution:

[0138]

[0139] You can also use v max Or PIS means v + , use v min Or NIS means v - .

[0140] The distance from each power quality monitoring point to the positive and negative ideal solutions is calculated, and the evaluation score of each power quality monitoring point is calculated based on the distance. In this embodiment, on the basis of obtaining the positive and negative ideal solutions, the distance from each power quality monitoring point to the positive ideal solution is further calculated:

[0141]

[0142] Distance to negative ideal solution:

[0143]

[0144] When calculating the distance from the power quality monitoring point to the ideal solution, other distance measurement methods can also be used, and the most commonly used one is the Euclidean distance.

[0145] Based on the evaluation scores, the power quality monitoring points are sorted and graded to complete the construction of the comprehensive power quality evaluation model.

[0146] In this embodiment, the evaluation score of each power quality monitoring point is calculated according to the following formula:

[0147]

[0148] According to E i The power quality monitoring points are sorted according to the value of , and the power quality monitoring points closer to 1 are better.

[0149] S5: Based on the comprehensive evaluation model of power quality, the power quality monitoring points are ranked and rated, the evaluation results are obtained, and the comprehensive evaluation of power quality is completed.

[0150] In this embodiment, based on the above-constructed comprehensive power quality evaluation model, an example analysis is performed using power quality data collected from five test points of a 0.38 kV observation station, and the measured data is derived from a published academic paper.

[0151] Table 2

[0152]

[0153] The example data in Table 2 are input into the comprehensive power quality evaluation system to calculate the power quality grade evaluation results of each test point.

[0154] Table 3

[0155]

[0156] The model constructed by the present invention does not need to consider the selection method of the subjective and objective weights of each indicator during the evaluation process, can fully utilize the data characteristics of each indicator, and can also avoid the influence of outliers on the results, so the credibility of the obtained classification results is high.

[0157] Embodiment 2

[0158] The present invention also provides a comprehensive power quality evaluation system based on improved CRITIC weights, which is used to implement a method, including:

[0159] An evaluation index selection module is used to establish power quality monitoring points and select the same number of evaluation indicators for each power quality monitoring point;

[0160] Improved module, used to improve the CRITIC weight calculation method by combining quartiles and improved distance correlation coefficient;

[0161] The weight calculation module is used to calculate the weight of each evaluation index of each power quality monitoring point based on the improved CRITIC weight calculation method;

[0162] An evaluation model building module is used to build a comprehensive power quality evaluation model based on the weights of various evaluation indicators;

[0163] The comprehensive evaluation module is used to rank and rate the power quality monitoring points based on the comprehensive power quality evaluation model, obtain evaluation results, and complete the comprehensive evaluation of power quality.

[0164] In the evaluation index selection module, the evaluation indicators include: voltage deviation I1, voltage fluctuation I2, voltage flicker I3, voltage harmonics I4, three-phase unbalance I5, frequency deviation I6, voltage sag I7, power supply reliability I8 and serviceability index I9. According to the national power quality standards and combined with the above power quality index system data, the power quality is roughly divided into five levels: excellent, good, medium, qualified and unqualified, represented by Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ respectively.

[0165] Based on the observation values ​​of power quality monitoring points and evaluation indicators, a decision matrix is ​​constructed; specifically, assuming there are m power quality monitoring points A1, A2, ..., A m Each power quality monitoring point has n indicators C1, C2, ..., C n , arrange the n index values ​​of m power quality monitoring points to get the decision matrix:

[0166]

[0167] In the weight calculation module, the method for obtaining the weight of each evaluation index of each power quality monitoring point is:

[0168] The distance correlation coefficient has a simple form of estimator. Assuming there are m power quality monitoring points, for any two evaluation indicators C j ,C j′ , define its dual center distance matrices A and B as:

[0169]

[0170]

[0171] in

[0172] a ih =||C ij -C hj || p ,i,h=1,2,…,m (1)

[0173] b ih =||C ij′ -C hj′ || q ,i,h=1,2,…,m (2)

[0174] Index C j With indicator C j′ The distance covariance between them is calculated as follows:

[0175]

[0176] Therefore, indicator C j With indicator Cj′ The distance correlation coefficient can be calculated according to the following formula:

[0177]

[0178] Experiments have found that the coefficient of variation and distance correlation coefficient are more sensitive to outliers. To solve this problem, we use the following method to improve them:

[0179] A variability calculation unit is used to calculate the variability of each evaluation index based on quartiles;

[0180] The coefficient of variation is calculated as follows:

[0181]

[0182] Where σ j represents the standard deviation of the jth indicator in the decision matrix, μ j Represents the mean of the jth indicator in the decision matrix.

[0183] In this embodiment, the interquartile range and median are used to replace the standard deviation and mean in the coefficient of variation, and the formula is as follows:

[0184]

[0185] in They represent the third quartile and the first quartile of the j-th evaluation index respectively. is the second quartile, representing the median, IQR j is the interquartile range of the jth evaluation index.

[0186] Let α,β∈R n ,definition

[0187] d(α,β)=log(1+||α-β|| p ),

[0188] where ||α-β|| p represents a certain distance between α and β, then d(α,β) is also a distance between α and β. The proof is given below:

[0189] (1) Non-negativity: Because ||α-β|| p ≥0, so 1+||α-β|| p ≥1, so

[0190] d(α,β)=log(1+||α-β|| p )≥0.

[0191] (2) Symmetry: Because ||α-β|| p =||β-α||p , a simple calculation shows that

[0192]

[0193] (3) Triangle inequality: Because

[0194] ||α-β|| p ≤||α-γ|| p +||β-γ|| p ,

[0195] so

[0196] 1+||α-β|| p ≤1+||α-γ|| p +||β-γ|| p .

[0197] therefore

[0198]

[0199] Notice

[0200] log(1+||α-γ|| p )+log(1+||β-γ|| p )=log(1+|α-γ|| p +|β-γ|| p +||α-γ|| p ×|β-γ|| p )

[0201] log(1+||α-β|| p )≤log(1+||α-γ|| p )+log(1+||β-γ|| p ).

[0202] In summary, d(α,β)=log(1+||α-β|| p ) satisfies the three properties of distance.

[0203] In order to improve the robustness of the distance correlation coefficient, the formulas (1) and (2) for calculating the distance matrix in the distance correlation coefficient are improved as follows:

[0204] a ih =log(1+||C ij -C hj || p ) (3)

[0205] b ih =log(1+||C ij′ -C hj′|| q ) (4)

[0206] Because (3) and (4) represent the distances between indicator observations, after replacing (1) and (2) with (3) and (4) respectively, the estimation theorem of distance correlation coefficient also holds. Therefore, the center distance matrix obtained by (3) and (4) can also be used to estimate the distance correlation coefficient.

[0207] A conflict obtaining unit, used for obtaining the conflict of each evaluation index based on the distance correlation coefficient between the evaluation indexes;

[0208] An information carrying capacity calculation unit, used to calculate the information carrying capacity between the evaluation indicators based on the variability and conflict of each evaluation indicator;

[0209] The weight calculation unit is used to obtain the weight of each evaluation index of each power quality monitoring point based on the information carrying capacity.

[0210] A further implementation is that the conflict obtaining unit includes:

[0211] The dual-center distance matrix acquisition subunit is used to calculate the dual-center distance matrix based on the distance between the observed values ​​of each evaluation index:

[0212] A distance correlation coefficient calculation subunit, used to calculate the distance covariance between the evaluation indicators based on the dual-center distance matrix; and calculate the distance correlation coefficient between the evaluation indicators based on the distance covariance;

[0213] A coefficient matrix acquisition subunit is used to obtain a distance correlation coefficient matrix based on the distance correlation coefficient;

[0214] The conflict calculation subunit is used to obtain the conflict of each evaluation index based on the distance correlation coefficient matrix.

[0215] Specifically, in this embodiment, the improved distance correlation coefficient is used to measure the correlation between indicators. The larger the value, the stronger the correlation. The distance correlation coefficient dr jj′ The calculation formula is as follows:

[0216]

[0217] in

[0218]

[0219] In the above formula, matrix A and matrix B are dual-center distance matrices:

[0220]

[0221]

[0222] in

[0223] a ih =log(1+||C ij -C hj || p ),

[0224] b ih =log(1+||C ij′ -C hj′ || q ).

[0225] Calculated by dr jj′ After the distance correlation coefficient matrix is ​​formed, the index conflict R is calculated based on the distance correlation coefficient matrix. j , the calculation formula is as follows:

[0226]

[0227] Calculate the information carrying capacity between indicators. Information carrying capacity ICC j The larger the value of , the more information it contains.

[0228]

[0229] Determine the indicator weight. The calculation formula of the indicator weight is as follows:

[0230]

[0231] A further implementation is that the evaluation model building module includes:

[0232] The decision matrix construction unit is used to construct a decision matrix based on the power quality monitoring points and the observed values ​​of the evaluation indicators. Specifically, assuming that there are m power quality monitoring points A1, A2, ..., A m Each power quality monitoring point has n indicators C1, C2, ..., C n , arrange the n index values ​​of m power quality monitoring points to get the decision matrix:

[0233]

[0234] The processing unit is used to convert all evaluation indicators into positive indicators and make the positive indicators dimensionless; specifically, for the negative indicators, the following method can be used for positive conversion:

[0235] y ij =Mx ij or

[0236] Where M is xij For the intermediate indicators to be positive, the following formula can be used for positive conversion:

[0237]

[0238] Where M is |x best -x ij The maximum value of |, x best For x ij When dealing with the positive conversion of interval indicators, the following formula can be applied for conversion:

[0239] M = max{a-min(x ij ),max(x ij )-b}

[0240]

[0241] Where a and b are the lower and upper limits of the interval respectively.

[0242] The data is processed dimensionlessly to avoid the impact caused by the difference in the dimension and order of magnitude of the indicator. The indicator is normalized using the range method, and the processed data is between 0 and 1. The processing method is as follows:

[0243]

[0244] Among them, y jmin and jmax They represent the minimum and maximum values ​​of the evaluation object in the jth indicator respectively.

[0245] The weighted normalized decision matrix acquisition unit is used to obtain the weighted normalized decision matrix based on the dimensionless decision matrix of all positive indicators and the weights of each evaluation indicator of each power quality monitoring point; in this embodiment, the weight vector w=(w1,w2,...w n ), The weighted normalized decision matrix V is calculated according to the following formula:

[0246]

[0247] The positive and negative ideal solution acquisition unit is used to obtain the positive and negative ideal solutions of each evaluation index based on the weighted normalized decision matrix; a further implementation method is that the method of obtaining the positive and negative ideal solutions of each evaluation index is: based on the weighted normalized decision matrix, the maximum value of each evaluation index is taken as the positive ideal solution, and the minimum value of each evaluation index is taken as the negative ideal solution. Specifically, all indicators are extremely large indicators and have a unified value range, and the maximum value of each indicator is taken as the positive ideal solution:

[0248]

[0249] Similarly, the minimum value of each indicator is taken as the negative ideal solution:

[0250]

[0251] You can also use v max Or PIS means v + , use v min Or NIS means v - .

[0252] The distance calculation unit is used to calculate the distance from each power quality monitoring point to the positive and negative ideal solutions, and calculate the evaluation score of each power quality monitoring point based on the distance; in this embodiment, on the basis of obtaining the positive and negative ideal solutions, the distance from each power quality monitoring point to the positive ideal solution is further calculated:

[0253]

[0254] Distance to negative ideal solution:

[0255]

[0256] When calculating the distance from the power quality monitoring point to the ideal solution, other distance measurement methods can also be used, and the most commonly used one is the Euclidean distance.

[0257] The model building unit is used to sort and grade the power quality monitoring points based on the evaluation scores, and complete the construction of the comprehensive power quality evaluation model.

[0258] In this embodiment, the evaluation score of each power quality monitoring point is calculated according to the following formula:

[0259]

[0260] According to E i The power quality monitoring points are sorted according to the value of , and the power quality monitoring points closer to 1 are better.

[0261] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A comprehensive evaluation method for power quality based on improved CRITIC weights, characterized in that: The following steps are involved: Establish power quality monitoring points and select the same number of evaluation indicators for each power quality monitoring point; The CRITIC weight calculation method is improved by combining quartiles and improved distance correlation coefficient; Based on the improved CRITIC weight calculation method, the weight of each evaluation index of each power quality monitoring point is calculated; Based on the weights of the various evaluation indicators, a comprehensive power quality evaluation model is constructed; Based on the comprehensive power quality evaluation model, the power quality monitoring points are ranked and rated to obtain evaluation results, thereby completing the comprehensive evaluation of power quality.

2. The comprehensive evaluation method for power quality based on improved CRITIC weight according to claim 1 is characterized in that: The evaluation indicators include: voltage deviation I1, voltage fluctuation I2, voltage flicker I3, voltage harmonics I4, three-phase imbalance I5, frequency deviation I6, voltage sag I7, power supply reliability I8 and serviceability index I9.

3. The comprehensive evaluation method for power quality based on improved CRITIC weight according to claim 1 is characterized in that: The method for obtaining the weight of each evaluation index of each power quality monitoring point is: Based on the quartiles, the variability of each evaluation index was calculated; Based on the distance correlation coefficient between the evaluation indicators, the conflict of each evaluation indicator is obtained; Based on the variability and conflict of each evaluation indicator, calculating the information carrying capacity between the evaluation indicators; Based on the information carrying capacity, the weight of each evaluation index of each power quality monitoring point is obtained.

4. The comprehensive evaluation method for power quality based on improved CRITIC weight according to claim 3 is characterized in that: Based on the quartiles, the method for calculating the variability of each evaluation index is: The interquartile range and median are used to replace the standard deviation and mean in the coefficient of variation. The formula is as follows: in They represent the third quartile and the first quartile of the j-th evaluation index respectively. is the second quartile, representing the median, IQR j is the interquartile range of the jth evaluation index.

5. The comprehensive power quality evaluation method based on improved CRITIC weight according to claim 3 is characterized in that: The method to obtain the conflict of each evaluation index is: Based on the distance between the observed values ​​of each evaluation index, the dual center distance matrix is ​​calculated: Based on the dual-center distance matrix, calculating the distance covariance between the evaluation indicators; based on the distance covariance, calculating the distance correlation coefficient between the evaluation indicators; Based on the distance correlation coefficient, obtaining a distance correlation coefficient matrix; Based on the distance correlation coefficient matrix, the conflict of each evaluation index is obtained.

6. The comprehensive evaluation method for power quality based on improved CRITIC weight according to claim 3 is characterized in that: Based on the weights of the various evaluation indicators, the method for constructing a comprehensive power quality evaluation model is: Constructing a decision matrix based on the power quality monitoring points and the observed values ​​of the evaluation indicators; Convert all evaluation indicators into positive indicators and make the positive indicators dimensionless; Based on the decision matrix after dimensionless transformation of all positive indicators and the weight of each evaluation indicator of each power quality monitoring point, a weighted normalized decision matrix is ​​obtained; Based on the weighted normalized decision matrix, obtaining positive and negative ideal solutions for each evaluation indicator; Calculating the distance between each power quality monitoring point and the positive and negative ideal solutions, and calculating the evaluation score of each power quality monitoring point based on the distance; Based on the evaluation scores, the power quality monitoring points are sorted and graded to complete the construction of a comprehensive power quality evaluation model.

7. The method for comprehensive evaluation of power quality based on improved CRITIC weight according to claim 6, characterized in that: The method for obtaining the positive and negative ideal solutions of each evaluation index is: based on the weighted normalized decision matrix, taking the maximum value of each evaluation index as the positive ideal solution, and taking the minimum value of each evaluation index as the negative ideal solution.

8. A comprehensive power quality evaluation system based on improved CRITIC weights, used to implement the method according to any one of claims 1 to 7, characterized in that: include: An evaluation index selection module is used to establish power quality monitoring points and select the same number of evaluation indicators for each power quality monitoring point; Improved module, used to improve the CRITIC weight calculation method by combining quartiles and improved distance correlation coefficient; The weight calculation module is used to calculate the weight of each evaluation index of each power quality monitoring point based on the improved CRITIC weight calculation method; An evaluation model building module, used to build a comprehensive power quality evaluation model based on the weights of the various evaluation indicators; The comprehensive evaluation module is used to rank and rate the power quality monitoring points based on the power quality comprehensive evaluation model, obtain evaluation results, and complete the comprehensive evaluation of power quality.

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