Power quality comprehensive evaluation method for distribution network based on G1 and CRITIC

Abstract

The application discloses a power distribution network power quality comprehensive evaluation method based on G1 and CRITIC. The method comprises the following steps: obtaining power grid data monitored at a monitoring point of a power distribution network, wherein the power grid data at least comprises all initial data under different evaluation indexes; for different evaluation indexes, using a sequence relation analysis method to calculate a subjective weight corresponding to each evaluation index, and using an objective weighting method to calculate an objective weight corresponding to each evaluation index; calculating a comprehensive weight corresponding to each evaluation index based on the subjective weight and the objective weight; determining initial data corresponding to each evaluation index from all initial data, and using a fuzzy evaluation algorithm to evaluate power quality of the power distribution network based on the comprehensive weight. The application solves the technical problem that the evaluation result of the power distribution network power quality evaluation does not conform to the actual situation of the power distribution network and the evaluation accuracy is low.

Description

Technical Field

[0001] This invention relates to the field of power quality assessment technology for distribution networks, and more specifically, to a comprehensive power quality assessment method for distribution networks based on G1 and CRITIC. Background Technology

[0002] Currently, with the development of new power systems, distribution networks are increasingly intertwined with distributed power sources, loads, and energy storage, forming a distributed renewable energy grid. Consequently, the factors affecting power quality in distribution networks are becoming increasingly complex. On the other hand, the increase in power quality-sensitive appliances is leading to higher demands for power quality from users. Distribution network power quality issues have attracted widespread attention from both power suppliers and consumers. Power quality assessment helps to address these issues in a targeted manner; therefore, distribution network power quality evaluation has become an important research area in the modern power sector.

[0003] In recent years, certain research achievements have been made in the field of distribution networks and their power quality. Regarding the management and mitigation of power quality issues in distribution networks, researchers have proposed monitoring and mitigation methods to address the impact of distributed generation and power electronic device integration on power quality. In power quality assessment, methods based on the Analytic Hierarchy Process (AHP) exist, but the AHP relies heavily on expert opinions, easily leading to assessment results that deviate from reality. Other researchers have proposed a data-driven power quality assessment method, which can objectively assess the overall power quality of the distribution network system; however, this method depends on large amounts of data, which are often difficult to obtain in real time.

[0004] In addition, there are other power quality evaluation methods such as fuzzy pattern recognition, set pair analysis and variable fuzzy sets, and grey comprehensive evaluation based on optimal combination weights. However, the power quality indicators used in these methods have not been optimized for distribution networks. Therefore, when applied to power quality evaluation of distribution networks, they may result in large computational loads and evaluation results that do not conform to the actual situation of the distribution network.

[0005] It is evident that the relevant technologies suffer from technical problems such as the assessment results of power quality evaluation in distribution networks not conforming to the actual situation of the distribution network, resulting in low assessment accuracy. Summary of the Invention

[0006] This invention provides a comprehensive evaluation method for power quality in distribution networks based on G1 and CRITIC, which at least solves the technical problem in related technologies that the evaluation results of power quality assessment in distribution networks do not conform to the actual situation of the distribution network and the evaluation accuracy is low.

[0007] According to one aspect of the present invention, a method for comprehensive evaluation of power quality in distribution networks based on G1 and CRITIC is provided, which may include: acquiring power grid data obtained from monitoring points in the distribution network, wherein the power grid data includes at least all initial data under different evaluation indicators, and the different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics; for the different evaluation indicators, calculating the subjective weight corresponding to each evaluation indicator using the order relation analysis method, and calculating the objective weight corresponding to each evaluation indicator using the objective weighting method; calculating the comprehensive weight corresponding to each evaluation indicator based on the subjective weight and the objective weight; determining the initial data corresponding to each evaluation indicator from all the initial data, and using a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network.

[0008] In an exemplary embodiment, the subjective weight corresponding to each evaluation indicator is calculated using the order relation analysis method, including: ranking the different evaluation indicators according to their importance to obtain a ranking set, wherein each evaluation indicator in the ranking set has a ranking number, and the ranking number corresponds to the importance of the evaluation indicator; and determining two adjacent evaluation indicators from the ranking set. and in, This represents the (k-1)th evaluation index. Represents the k-th evaluation index; determine and The ratio of importance between them r k ; in, for The weight coefficient represents the subjective weight of the (k-1)th evaluation indicator. for The weight coefficient represents the subjective weight of the k-th evaluation indicator; based on the importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index

[0009] In one exemplary embodiment, based on the importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index This includes: calculating the weight of the k-th evaluation indicator using the following formula. Where i, n, and k are positive integers; for the (k-1)th evaluation indicator, the weight of the (k-1)th evaluation indicator is calculated using the following formula:

[0010] In an exemplary embodiment, calculating the objective weight corresponding to each evaluation index using an objective weighting method includes: normalizing the initial data matrix X corresponding to all the initial data to obtain a standardized data matrix Y, where X = (x ij ) m×n , Y = (y ij ) m×n , x ij Let represent the index data of the j-th evaluation indicator at the i-th monitoring point, yij represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, n represent the number of evaluation indicators, m represent the number of monitoring points, and max(xij) = 1. j ) represents the maximum index data at different monitoring points under the j-th evaluation index, min(x j Let represent the minimum indicator data at different monitoring points under the j-th evaluation indicator; calculate the information content based on the indicator variation coefficient and indicator conflict coefficient of the standardized data matrix, wherein the indicator variation coefficient is used to quantify the comparative strength of the evaluation indicator, and the indicator conflict coefficient is used to represent the degree of difference between the maximum and minimum indicator data; normalize each column of the information content, and obtain the set of objective weights corresponding to the evaluation indicator based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators.

[0011] In one exemplary embodiment, before calculating the information content based on the index variation coefficient and index conflict coefficient of the standardized data matrix, the method further includes: calculating the data average of the j-th column of the standardized data matrix. And calculate the standard deviation s of the data in the j-th column. j The coefficient of variation v of the index for the data in the j-th column is calculated using the following formula. j : in,

[0012] In an exemplary embodiment, before calculating the information content based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix, the method further includes: calculating the correlation coefficient r between different evaluation indicators. ij ,in, s iLet s represent the standard deviation corresponding to the i-th evaluation indicator. j This represents the standard deviation corresponding to the j-th evaluation indicator. The covariance between the i-th and j-th columns of the standardized data matrix is ​​represented by the following formula; the index conflict coefficient A of the j-th column data is calculated using the following formula. j :

[0013] In an exemplary embodiment, calculating the information content based on the index variation coefficient and the index conflict coefficient of the standardized data matrix includes: calculating the information content based on the index variation coefficient v of the j-th column data. j The coefficient of conflict A between the index and the data in column j j Calculation of information content E j E j =v j ×A j Normalize each column of data in the information quantity, including: normalizing E using the following formula. j Normalize: Where, θ j This represents the objective weight corresponding to the j-th evaluation indicator.

[0014] In one exemplary embodiment, a fuzzy algorithm combined with the comprehensive weights is used to quantitatively assess the power quality of the distribution network, including:

[0015] The selected evaluation indicators are denoted as U1, U2, U3, U4, and U5, respectively, and a factor set U is constructed as {U1, U2, U3, U4, U5}. The power quality is divided into 5 intervals from best to worst: “Excellent”, “Good”, “Medium”, “Poor”, and “Extremely Poor”, respectively, and denoted as V1, V2, V3, V4, and V5, respectively, and an evaluation set V is constructed as {V1, V2, V3, V4, V5}.

[0016] Power quality is classified into good and bad levels, resulting in the following evaluation set V:

[0017] V = [Good, Fair, Average, Poor]

[0018] =[95 80 70 55 40],

[0019] The membership degree of different comments of the index is calculated using a Gaussian membership function f(y).

[0020]

[0021] Where y is the monitoring data of the distribution network evaluation index, σ and c are set parameters. σ is set to 0.3, c is initially 1, and gradually decreases to 0 with an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding c value. The membership function corresponding to each evaluation set is calculated.

[0022] The evaluation value y of the indicator ij Combining with Gaussian membership functions, we obtain the evaluation matrix F of the index:

[0023]

[0024] in, It is the indicator y ij For rating level V k The degree of membership;

[0025] use The operator performs a fuzzy product operation on the weights and the evaluation matrix to obtain the overall evaluation score of the power quality of the distribution network. The formula for the fuzzy product operation is expressed as follows:

[0026] B i =[b i (V1) b i (V2) b i (V3) b i (V4) b i (V5)],

[0027] in: b i (V k () is a rating symbol V that indicates the degree of membership between indicators. k ;

[0028] Use formula Calculate the overall assessment score of the power quality of the distribution network, and determine the quality of the power quality of the distribution network based on the corresponding power quality assessment results and the quantitative grading intervals of the power quality of the distribution network.

[0029] According to another aspect of the present invention, a comprehensive power quality evaluation device for distribution networks based on G1 and CRITIC is also provided. This device may further include: an acquisition module, configured to acquire power grid data obtained from monitoring points in the distribution network, wherein the power grid data includes at least all initial data under different evaluation indicators, and the different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics; a first calculation module, configured to calculate the subjective weight corresponding to each evaluation indicator using an ordinal relation analysis method and the objective weight corresponding to each evaluation indicator using an objective weighting method; a second calculation module, configured to calculate the comprehensive weight corresponding to each evaluation indicator based on the subjective weight and the objective weight; and an evaluation module, configured to determine the initial data corresponding to each evaluation indicator from all the initial data and to quantitatively evaluate the power quality of the distribution network using a fuzzy algorithm combined with the comprehensive weight.

[0030] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is running, it controls the device where the computer-readable storage medium is located to execute the power quality comprehensive evaluation method for distribution networks based on G1 and CRITIC according to the present invention.

[0031] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program is executed by the processor to perform the distribution network power quality comprehensive evaluation method based on G1 and CRITIC according to the present invention.

[0032] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the distribution network power quality comprehensive evaluation method based on G1 and CRITIC according to the present invention.

[0033] In this embodiment of the invention, firstly, power grid data obtained from monitoring points in the distribution network is acquired. This power grid data includes at least all initial data under different evaluation indicators, which include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. Then, for each evaluation indicator, a sequence relation analysis method is used to calculate the subjective weight, and an objective weighting method is used to calculate the objective weight. Next, a comprehensive weight is calculated based on the subjective and objective weights. Finally, the initial data corresponding to each evaluation indicator is determined from all the initial data, and a fuzzy algorithm combined with the comprehensive weight is used to quantitatively evaluate the power quality of the distribution network. This invention analyzes the factors affecting power quality in distribution networks and the power quality problems caused by these factors, and proposes different evaluation indicators for power quality in distribution networks accordingly. Then, it uses the G1 method and the CRITIC method to determine subjective and objective weights, calculates the comprehensive weight, and uses the fuzzy comprehensive evaluation method to quantitatively evaluate the power quality of the distribution network. This solves the technical problem that the evaluation results of power quality assessment in distribution networks do not conform to the actual situation of the distribution network and have low accuracy, thus improving the accuracy of the comprehensive evaluation results of power quality in distribution networks.

[0034] The beneficial effects of this invention are as follows: the G1 method and the CRITIC method are used to calculate the subjective and objective weights respectively, and the comprehensive weights are calculated by combining them to reduce information loss in the weighting process. Different evaluation indicators are proposed, and fuzzy comprehensive evaluation method is used to evaluate these evaluation indicators to strengthen their correlation and effectively improve the accuracy of the comprehensive evaluation results of power quality of distribution networks. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0036] Figure 1 This is a flowchart of a comprehensive power quality evaluation method for distribution networks based on G1 and CRITIC according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of a comprehensive power quality evaluation process for a distribution network based on G1 and CRITIC according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of a fuzzy comprehensive evaluation of power quality in a distribution network based on G1 and CRITIC according to an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of a power quality comprehensive evaluation device for distribution networks based on G1 and CRITIC according to an embodiment of the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] Example 1

[0043] According to an embodiment of the present invention, an embodiment of a comprehensive power quality assessment method for distribution networks based on G1 and CRITIC is provided. The steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] Figure 1 This is a flowchart of a comprehensive power quality evaluation method for distribution networks based on G1 and CRITIC, according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0045] Step S102: Obtain power grid data from monitoring points in the distribution network. The power grid data includes at least all initial data under different evaluation indicators. The different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics.

[0046] Regarding step S102 above, the different evaluation indicators mentioned above can be understood as typical indicators for evaluating the power quality of the distribution network. It is understood that the values ​​of these indicators are negatively correlated with the power quality of the power system; that is, the lower the indicator value, the higher the power quality.

[0047] Voltage deviation is caused by an imbalance of reactive power in the power distribution network. For example, in summer, household appliances such as air conditioners connected to the distribution network absorb a large amount of reactive power, leading to an imbalance between active and reactive power in the network and resulting in voltage deviation. Voltage deviation can cause instability in the power system and potentially lead to the collapse of the distribution system.

[0048] Voltage fluctuations refer to voltage flicker phenomena that occur during large-scale grid connection. Frequent voltage fluctuations can lead to equipment overload, damage to electrical components, or premature aging.

[0049] Voltage sag is a short-term disturbance phenomenon in the distribution network caused by a sudden large current due to short-circuit faults in lines and busbars, no-load excitation of large transformers, or large load switching. Alternatively, electricity theft can sometimes lead to voltage sag and short-term interruption problems in the distribution network.

[0050] In power systems, nonlinear loads sometimes inject large amounts of harmonic currents into the distribution network, leading to three-phase imbalance. Three-phase imbalance increases losses in lines and distribution transformers, causing severe transformer overheating. For users, three-phase imbalance results in uneven energy distribution within the power system, with some lines potentially underloaded while others are underloaded.

[0051] The numerous power electronic devices connected to the power distribution network can cause harmonic problems during operation. Harmonics can lead to additional energy loss in the power system, resulting in reduced system efficiency and increased operating costs.

[0052] Step S104: For the different evaluation indicators, the subjective weight corresponding to each evaluation indicator is calculated using the ordinal relation analysis method, and the objective weight corresponding to each evaluation indicator is calculated using the objective weighting method.

[0053] G1 (Order Relationship Analysis) is a method for calculating subjective weights, primarily used in Multi-Criterion Decision Analysis (MCDA) and Multi-Attribute Decision Analysis (MADA). The purpose of G1 is to determine the relative importance of a decision-maker to multiple criteria (or attributes). This method gathers information about the decision-maker's preferences through a series of pairwise comparisons and then uses this information to calculate the weight of each criterion. Therefore, G1 can be used to calculate subjective weights.

[0054] The advantages of the G1 method are its simplicity, intuitiveness, and ease of understanding and application. However, when dealing with a large number of criteria, pairwise comparisons can become cumbersome. Furthermore, the G1 method relies on the decision-maker's personal preferences and may be influenced by subjective biases.

[0055] The following are the steps for calculating subjective weights using the G1 method:

[0056] 1. Define the decision problem: First, clarify the decision problem and the criteria (or attributes) involved.

[0057] 2. Pairwise comparison: For each pair of criteria (i and j), ask the decision-maker about the importance of criterion i relative to criterion j. The decision-maker needs to score each pair of criteria according to their preferences. Odd-numbered scales (such as 1, 3, 5, 7, 9, etc.) are typically used to represent relative importance.

[0058] 3. Construct the comparison matrix: Based on the results of pairwise comparisons, construct an n×n matrix A, where n is the number of criteria. The element a_ij in the matrix represents the relative importance of criterion i with respect to criterion j.

[0059] 4. Normalization Matrix: Normalize each row of the comparison matrix A so that the sum of the elements in each row equals 1. Thus, each column of the normalized matrix represents the relative weight of each criterion.

[0060] 5. Calculate the weights: Sum the columns of the normalized matrix and then divide by the number of criteria n to obtain the subjective weight of each criterion.

[0061] 6. Check Consistency: To ensure the reasonableness of subjective weights, the consistency of the comparison matrix can be checked. If the consistency index (CI) is less than a certain threshold (usually 0.1), the weights can be accepted; otherwise, pairwise comparisons need to be performed again.

[0062] The CRITIC method (objective weighting) is a decision analysis approach used to determine the importance of different criteria in the decision-making process and to consider the interrelationships between these criteria. This method helps decision-makers better understand the impact and interrelationships between various criteria, thereby making more accurate decisions. It plays a crucial role in decision-making processes such as project selection, risk assessment, and resource allocation. Therefore, CRITIC (Criteria Importance Through Intercrieria Correlation) can be used to calculate objective weights.

[0063] In this invention, for example, the G1 method and the CRITIC method can be used to calculate the subjective weight and the objective weight respectively. In this way, when calculating the comprehensive weight based on the combination of subjective weight and objective weight in the subsequent process, the information loss during the weighting process can be reduced.

[0064] Step S106: Calculate the comprehensive weight corresponding to each evaluation indicator based on the subjective weight and the objective weight;

[0065] Step S108: Determine the initial data corresponding to each evaluation index from all the initial data, and use a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network.

[0066] It should be noted that fuzzy algorithms are mathematical methods used in various fields, such as artificial intelligence, control systems, and decision analysis, to process fuzzy information. They can map fuzzy inputs to a fuzzy output. Fuzzy algorithms can better handle uncertain and fuzzy information, improving the accuracy and robustness of decision-making.

[0067] Common fuzzy algorithms include fuzzy logic, fuzzy sets, and fuzzy inference.

[0068] Fuzzy logic is essentially a logic system that can handle fuzzy concepts and fuzzy propositions. It can be used for fuzzy reasoning and decision-making.

[0069] Fuzzy sets are a mathematical tool based on membership degree, which can describe the degree of membership of fuzzy concepts and the intersection, union, and complement operations of fuzzy sets.

[0070] Fuzzy reasoning is a reasoning method based on fuzzy logic and fuzzy sets, which can perform reasoning and decision-making based on fuzzy rules and fuzzy facts.

[0071] Through the above steps, subjective and objective weights can be calculated using the G1 and CRITIC methods, and combined to calculate the comprehensive weight. The fuzzy comprehensive evaluation method is then used in conjunction with the comprehensive weight, thereby improving the accuracy of the comprehensive evaluation results of power quality in the distribution network.

[0072] Optionally, using a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network can be understood as using a fuzzy algorithm to calculate the evaluation matrix corresponding to the initial data and the comprehensive weight to obtain the evaluation score corresponding to the initial data.

[0073] Through the steps described above in this invention, firstly, power grid data obtained from monitoring points in the distribution network is acquired. This power grid data includes at least all initial data under different evaluation indicators, which include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. Then, for each evaluation indicator, the subjective weight is calculated using order relation analysis, and the objective weight is calculated using objective weighting. Next, the comprehensive weight corresponding to each evaluation indicator is calculated based on the subjective and objective weights. Finally, the initial data corresponding to each evaluation indicator is determined from all the initial data, and a fuzzy algorithm combined with the comprehensive weights is used to quantitatively evaluate the power quality of the distribution network. This invention analyzes the factors affecting power quality in distribution networks and the power quality problems caused by these factors, and proposes different evaluation indicators for power quality in distribution networks accordingly. Then, it uses the G1 method and the CRITIC method to determine subjective and objective weights, calculates the comprehensive weight, and uses the fuzzy comprehensive evaluation method to quantitatively evaluate the power quality of the distribution network. This solves the technical problem that the evaluation results of power quality assessment in distribution networks do not conform to the actual situation of the distribution network and have low accuracy, thus improving the accuracy of the comprehensive evaluation results of power quality in distribution networks.

[0074] The method described in this embodiment will be further described below.

[0075] As an optional implementation, the subjective weight corresponding to each evaluation indicator is calculated using the order relation analysis method, including: ranking the different evaluation indicators according to their importance to obtain a ranking set, wherein each evaluation indicator in the ranking set has a ranking number, and the ranking number corresponds to the importance of the evaluation indicator; and determining two adjacent evaluation indicators from the ranking set. and in, This represents the (k-1)th evaluation index. Represents the k-th evaluation index; determine and The ratio of importance between them r k ; in, for The weight coefficient represents the subjective weight of the (k-1)th evaluation indicator. for The weight coefficient represents the subjective weight of the k-th evaluation indicator; based on the importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index

[0076] Furthermore, based on the aforementioned importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index This can include: calculating the weight of the k-th evaluation indicator using the following formula. Where i, n, and k are positive integers;

[0077] For the (k-1)th evaluation indicator, the weight of the (k-1)th evaluation indicator is calculated using the following formula:

[0078]

[0079] Optionally, the objective weighting method is used to calculate the objective weight corresponding to each evaluation indicator, including: normalizing the initial data matrix X corresponding to all the initial data to obtain a standardized data matrix Y, where X = (x ij ) m×n , Y = (y ij ) m×n , x ij Let y represent the indicator data of the j-th evaluation indicator at the i-th monitoring point. ij Let represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, n represent the number of evaluation indicators, m represent the number of monitoring points, and max(x) = 1. j ) represents the maximum index data at different monitoring points under the j-th evaluation index, min(x j Let represent the minimum indicator data at different monitoring points under the j-th evaluation indicator; calculate the information content based on the indicator variation coefficient and indicator conflict coefficient of the standardized data matrix, wherein the indicator variation coefficient is used to quantify the comparative strength of the evaluation indicator, and the indicator conflict coefficient is used to represent the degree of difference between the maximum and minimum indicator data; normalize each column of the information content, and obtain the set of objective weights corresponding to the evaluation indicator based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators.

[0080] Optionally, before calculating the information content based on the coefficient of variation and the coefficient of conflict of the indicators in the standardized data matrix, the method further includes: calculating the data average of the j-th column of the standardized data matrix. And calculate the standard deviation s of the data in the j-th column. j The coefficient of variation v of the index for the data in the j-th column is calculated using the following formula.j : in,

[0081] Optionally, before calculating the information content based on the coefficient of variation and the coefficient of conflict of the indicators in the standardized data matrix, the method further includes: calculating the correlation coefficient r between different evaluation indicators. ij ,in, s i Let s represent the standard deviation corresponding to the i-th evaluation indicator. j This represents the standard deviation corresponding to the j-th evaluation indicator. The covariance between the i-th and j-th columns of the standardized data matrix is ​​represented by the following formula; the index conflict coefficient A of the j-th column data is calculated using the following formula. j :

[0082] Optionally, the information content is calculated based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix, including: calculating the information content based on the coefficient of variation v of the indicators in the j-th column of the data. j The coefficient of conflict A between the index and the data in column j j Calculation of information content E j E j =v j ×A j Normalize each column of data in the information quantity, including: normalizing E using the following formula. j Normalize: Where, θ j This represents the objective weight corresponding to the j-th evaluation indicator.

[0083] Optionally, a fuzzy algorithm combined with the comprehensive weights is used to quantitatively assess the power quality of the distribution network, including:

[0084] The five selected indicators are denoted as U1, U2, U3, U4, and U5, respectively, and a factor set U is constructed as {U1, U2, U3, U4, U5}. The power quality is divided into five intervals from best to worst: “Excellent”, “Good”, “Medium”, “Poor”, and “Very Poor”, respectively, and denoted as V1, V2, V3, V4, and V5, respectively, and an evaluation set V is constructed as {V1, V2, V3, V4, V5}.

[0085] Power quality is classified into good and bad levels, resulting in the following evaluation set V:

[0086] V = [Good, Fair, Average, Poor]

[0087] =[95 80 70 55 40],

[0088] The membership degree of different comments of the index is calculated using a Gaussian membership function f(y).

[0089]

[0090] Where y is the monitoring data of the distribution network evaluation index, σ and c are set parameters. σ is set to 0.3, c is initially 1, and gradually decreases to 0 with an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding c value. The membership function corresponding to each evaluation set is calculated.

[0091] The evaluation value y of the indicator ij Combining with Gaussian membership functions, we obtain the evaluation matrix F of the index:

[0092]

[0093] in, It is the indicator y ij For rating level V k The degree of membership;

[0094] use The operator performs a fuzzy product operation on the weights and the evaluation matrix to obtain the overall evaluation score of the power quality of the distribution network. The formula for the fuzzy product operation is expressed as follows:

[0095] B i =[b i (V1) b i (V2) b i (V3) b i (V4) b i (V5)],

[0096] in: b i (V k () is a rating symbol V that indicates the degree of membership between indicators. k ;

[0097] Use formula Calculate the overall assessment score of the power quality of the distribution network, and determine the quality of the power quality of the distribution network based on the corresponding power quality assessment results and the quantitative grading intervals of the power quality of the distribution network.

[0098] Through the above embodiments, multiple indicators for comprehensive evaluation of power quality in distribution networks can be proposed based on the factors affecting power quality in the distribution network. Then, the subjective and objective weights are calculated using the G1 and CRITIC algorithms respectively, and the comprehensive weight is calculated by combining them. The fuzzy comprehensive evaluation method is then used in conjunction with the comprehensive weight to comprehensively evaluate the power quality of the distribution network under different evaluation indicators. This improves the accuracy of the comprehensive evaluation results of power quality in distribution networks, which has certain guiding significance and reference value for improving the utilization rate of power efficiency in distribution networks.

[0099] Example 2

[0100] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments. For example, the technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments; the sizes of the numbers in the embodiments are only illustrative and are not specifically limited here. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention and are not specifically limited here.

[0101] Figure 2 This is a schematic diagram of a comprehensive power quality assessment process for a distribution network based on G1 and CRITIC, according to an embodiment of the present invention. Figure 2 As shown, the power quality of the distribution network can be comprehensively evaluated based on G1 and CRITIC methods: First, the factors affecting power quality in the distribution network are analyzed, and five indicators for comprehensive evaluation of power quality in the distribution network are proposed; second, subjective and objective weights are calculated using the G1 and CRITIC methods, and the comprehensive weight is calculated by combining them; finally, the fuzzy comprehensive evaluation method is used in combination with the comprehensive weight to conduct quantitative evaluation and fuzzy comprehensive evaluation of the power quality of the distribution network.

[0102] This process is implemented through the following steps:

[0103] Step S201: Analyze the factors affecting power quality in the distribution network and propose five comprehensive evaluation criteria for power quality in the distribution network;

[0104] Step S202: Calculate the subjective weights and objective weights using the G1 method and the CRITIC method, and combine them to calculate the comprehensive weight;

[0105] Step S203: Use the fuzzy comprehensive evaluation method combined with comprehensive weights to conduct quantitative evaluation and fuzzy comprehensive evaluation of power quality in the distribution network.

[0106] It should be noted that power quality refers to the quality of AC power supplied by the power grid to the user side. Ideally, the power should present a perfectly symmetrical sine wave with minimal differences between each other.

[0107] Power quality standards are basic power standards formulated from the perspective of ensuring power grid safety and normal user operation. Based on nationally promulgated power indicators and indicators developed during application, there are dozens of power quality evaluation indicators, and the importance of these indicators varies across different systems. To ensure greater accuracy of the selected indicators, appropriate power quality evaluation indicators should be chosen based on the operating characteristics of different systems, so that the evaluation results more accurately reflect reality.

[0108] Considering the increasing complexity of power distribution networks with the development of power systems, the grid connection of new energy sources, the application of power electronic equipment, and the increase in nonlinear loads all bring about power quality problems. However, in actual power systems, the factors causing power quality problems due to the increase in nonlinear loads, the widespread use of power electronic equipment, and electricity theft are particularly prominent. For example, nonlinear loads such as transformers, generators, and air conditioners inject a large amount of harmonic current into the distribution network, leading to harmonic problems and three-phase imbalance. At the same time, the reactive power imbalance caused by nonlinear loads can also cause voltage deviation problems. Furthermore, the output of new energy sources such as wind and solar power, due to their inherent randomness, volatility, and intermittency, often brings voltage fluctuation and voltage flicker problems to the distribution network. Therefore, this invention selects five evaluation indicators (i.e., the above evaluation indicators) for voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics.

[0109] Furthermore, step S202 includes: S2021, subjective weight calculation. For example, the G1 method can be used for calculation. The G1 method determines the weight of each indicator based on its importance, allowing experts to rank and quantify the importance of indicators according to the characteristics of the distribution network and their knowledge and experience.

[0110] The specific implementation process of step S2021 is as follows:

[0111] 1) Determine the importance of the evaluation indicators: Gather the n indicators into a set X = {x1, x2, ..., xn}. n Determine the importance of each pair of indicators, and follow the indicators. Arrange the importance from highest to lowest

[0112] 2) Determine the relative importance ratio between evaluation indicators: Determine the two indicators of adjacent indicators according to Table 1. and Importance ratio:

[0113]

[0114] in, and Representing indicators and The weighting coefficients.

[0115] Furthermore, relative importance is higher than r. k The value selection method is shown in Table 1:

[0116] Table 1. Ratio of relative importance (r) k Value table

[0117]

[0118]

[0119] 3) Calculate the subjective weights of the evaluation indicators: based on the determined r k The weight of the nth evaluation indicator is calculated using the following formula:

[0120]

[0121] Therefore, the weight of the (k-1)th indicator is:

[0122]

[0123] Step S202 above further includes: S2022, objective weight calculation. For example, the CRITIC method can be used for calculation. Based on the CRITIC method, the importance of each indicator in the decision-making process can be determined by analyzing the correlation and comparative strength between evaluation indicators, thereby assigning weights more accurately.

[0124] The specific implementation steps of step S2022 are as follows:

[0125] 1) Data standardization:

[0126] The initial data from power quality monitoring of the distribution network are used to construct a data matrix X = (x ij ) m×n The initial data is then normalized to ensure it falls within a uniform numerical range.

[0127] Apply the minimum-maximum normalization method to x ij After unified processing, we obtain y ij y ij The processed, standardized data value of the j-th power quality evaluation index for the i-th evaluation object in the distribution network;

[0128]

[0129] The normalized data are combined to form a standardized data matrix Y = (y ij ) m×n The details are as follows:

[0130]

[0131] Where: x ij Let x be the measured value of the j-th power quality evaluation index of the distribution network for the i-th evaluation object, i = 1, ..., m, j = 1, ..., n, where n is the number of power quality evaluation indicators (evaluation indicators) of the distribution network, m is the number of evaluation objects (monitoring points), and max(x j ), min(x j () represents the maximum and minimum values ​​of different evaluation objects under the same evaluation index.

[0132] 2) Calculate the coefficient of variation of the indicators: The coefficient of variation is a quantitative display of the comparative strength of the indicators. j The calculation formula is:

[0133]

[0134]

[0135]

[0136] Where: s j Let be the standard deviation of the j-th evaluation index. is the average value of the j-th evaluation index.

[0137] 3) Calculation of indicator conflict: Forming a standardized data matrix Y = (y ij ) m×n Calculate the correlation coefficient r of different indicators ij Next, the degree of difference A between the maximum and minimum evaluation values ​​of different evaluation objects is calculated. j :

[0138]

[0139]

[0140] Where: s i Let s be the standard deviation of the i-th evaluation indicator (equivalent to the standard deviation of the data mentioned above). j Let be the standard deviation of the j-th evaluation index. Let be the covariance between the i-th and j-th columns of the standardized matrix.

[0141] 4) Information content calculation: The information content E is calculated using the following formula. j :

[0142] E j =v j ×A j .

[0143] 5) Calculation of objective weights for evaluation indicators: Normalize the information content to obtain the weight θ of the j-th indicator. j for:

[0144]

[0145] It's important to note that the strength of a comparison is typically quantified by calculating the coefficient of variation (COP). A higher COP indicates greater volatility in the indicator, thus warranting a higher weight, as highly volatile indicators have a greater influence on decision-making. The conflict between indicators is represented by their correlation coefficient. A higher correlation coefficient indicates less conflict between indicators, thus requiring a lower weight. Low correlation between indicators means they are unlikely to conflict in decision-making, and therefore do not need to be assigned significant weight.

[0146] In this step, the CRITIC method identifies conflicts and correlations between indicators, allowing for adjustments to the weight of each indicator to reduce the impact of redundant information. This method better reflects the actual influence relationships between indicators, making the evaluation results more accurate and reliable. Therefore, when dealing with evaluation problems involving multiple indicators and multiple objects, the CRITIC method provides an effective approach for more scientific evaluation and decision-making.

[0147] Step S202 also includes step S2023, comprehensive weight calculation. Since subjective weighting based on the G1 method relies excessively on expert opinions, and objective weighting based on the CRITIC method relies too heavily on quantitative analysis of sample data, neglecting the subjective qualitative analysis of evaluation indicators, using a single weighting method results in information loss, leading to indicator weights that do not conform to the actual situation of the distribution network. Therefore, to minimize information loss while accurately reflecting the actual system situation, the comprehensive weight λ is calculated by combining the subjective weight ω and the objective weight θ, as shown in the following formula:

[0148]

[0149] In step S203, specifically: the five selected indicators are denoted as U1, U2, U3, U4, and U5, respectively, and a factor set U is constructed: {U1, U2, U3, U4, U5}; the power quality is divided into five intervals from best to worst: “excellent”, “good”, “medium”, “poor”, and “extremely poor”, respectively, denoted as V1, V2, V3, V4, and V5, and an evaluation set V is constructed: {V1, V2, V3, V4, V5}.

[0150] The power quality rating table is shown in Table 2 below:

[0151] Table 2 Power Quality Classification Table for Distribution Networks

[0152] V1 good (85,100] 95 V2 better (75,85] 80 V3 generally (60,75] 70 V4 Poor (50,60] 55 V5 Difference (0,50] 40

[0153] Based on Table 2 above, the evaluation set (equivalent to the aforementioned preset evaluation set) V can be denoted as:

[0154] V = [Good, Fair, Average, Poor]

[0155] =[95 80 70 55 40].

[0156] The membership degree of different comments of the index is calculated using the Gaussian membership function f(y), as shown in the following formula:

[0157]

[0158] In the formula: y is the monitoring data of the power distribution network evaluation index; σ and c are set parameters. In this paper, σ is set to 0.3 and c is initially set to 1. It is gradually reduced to 0 with an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding c value. The membership function corresponding to each evaluation set is calculated.

[0159] Next, the evaluation value y of the indicator will be... ij Combining this with the membership function obtained above, we obtain the evaluation matrix F of the index:

[0160]

[0161] in: It is the indicator y ij For rating level V k The degree of subordination.

[0162] Then, adopt The operator (weighted average fuzzy comprehensive operator) performs fuzzy product operation on the weights and evaluation matrix to obtain the overall evaluation of the power quality assessment system of the distribution network, as shown in the following formula:

[0163] B i =[b i (V1) b i (V2) b i (V3) b i (V4) b i (V5)].

[0164] in: b i (V k ) indicates the rating level V k The degree of membership between them (i.e., the evaluation score mentioned above).

[0165] Next, calculate the mass fraction using the following formula:

[0166]

[0167] Finally, based on the power quality assessment results and the corresponding relationship between the power quality grading intervals of the distribution network, the power quality of the distribution network is determined.

[0168] Furthermore, it can be combined with Figure 3 The following embodiments illustrate the comprehensive power quality evaluation process for distribution networks based on G1 and CRITIC in this invention. Figure 3 As shown, this paper first analyzes the factors affecting power quality in the distribution network and the power quality problems caused by these factors, and proposes five power quality evaluation indicators for the distribution network. Then, it uses the subjective weights determined by the G1 method and the objective weights determined by the CRITIC method to calculate the comprehensive weights, and performs fuzzy evaluation on the power quality scores of the distribution network calculated based on the comprehensive weights to obtain accurate power quality assessment results for the distribution network.

[0169] Next, we will use a specific example to illustrate the above embodiments.

[0170] In this embodiment, real-time data of the power distribution network is collected, and five selected monitoring points are analyzed as examples.

[0171] Based on the power quality assessment indicators for the distribution network selected in this invention, the initial data of the monitoring points used for assessment are shown in Table 3 below:

[0172] Table 3 Power Quality Data Table

[0173] 1 2.53 0.96 53.12 0.88 1.12 2 1.66 1.05 65.23 1.07 1.26 3 3.85 1.41 76.25 0.83 1.18 4 2.01 0.85 23.54 0.58 0.82 5 3.18 1.27 69.72 1.23 1.35

[0174] Step 1: Calculate subjective weights based on the G1 method. Specific steps include:

[0175] Step S11, determine the order of evaluation indicators: Based on expert opinions, rank the power quality evaluation indicators of the distribution network in descending order of importance. The ranking results are as follows:

[0176] Voltage deviation > harmonics > voltage fluctuation > three-phase imbalance > voltage sag.

[0177] Step S12, determine the relative importance ratios among evaluation indicators: Based on expert opinions, determine the importance ratios of adjacent indicators as follows: r2 = 1.3, r3 = 1.6, r4 = 1.2, r5 = 1.2. Finally, calculate the subjective weights of the evaluation indicators. The calculation results are as follows:

[0178] ω=[0.3351 0.1611 0.1119 0.1342 0.2577].

[0179] Step 2, calculate the objective weights based on the CRITIC method, specifically:

[0180] Step S21, obtain the initial data matrix X from Table 3:

[0181]

[0182] Step S22, Data Standardization: The initial data is standardized to obtain the evaluation index matrix Y of the power quality evaluation index of the distribution network.

[0183]

[0184] Step S23, Calculate the coefficient of variation of the indicators: Calculate the standardized mean of the data for each evaluation indicator. And the standard deviation s:

[0185]

[0186] s=[0.40360.40850.39490.37990.3806].

[0187] The coefficients of variation for each indicator were calculated:

[0188] v=[0.73410.75751.11440.79140.9888].

[0189] Step S24, Calculate the conflict of indices: Calculate the correlation coefficient r between different indices. ij The conflict quantification value A is calculated as follows:

[0190] A=[2.06541.12380.90441.70131.1263].

[0191] Step S25, Calculation of Information Content and Objective Weights: Calculate the information content E of the indicator and perform normalization to obtain the objective weight θ of the indicator:

[0192] E=[1.5163 0.8513 1.0079 1.3465 1.1137].

[0193] θ=[0.2598 0.1459 0.1727 0.2307 0.1909].

[0194] Step 3, comprehensive weight calculation, specifically includes:

[0195] Step S31, obtain the comprehensive weight λ:

[0196] λ=[0.2995 0.1556 0.1411 0.1786 0.2252].

[0197] Step S32: Based on the data in Table 3, a fuzzy comprehensive evaluation was performed on the conditions of the five monitoring points. This will be illustrated using monitoring point 1 as an example.

[0198] First, based on the data in Table 3, the relevant data of monitoring point 1 were substituted into the calculation to obtain the evaluation matrix F1 of monitoring point 1.

[0199]

[0200] Then, a weighted average fuzzy synthesis operator is used to perform an overall evaluation of each monitoring point:

[0201] B1=λ·F1=[0.1319 0.2720 0.3183 0.2060 0.0717].

[0202] Finally, the fuzzy evaluation score Z1 for monitoring point 1 is calculated.

[0203] Z1=B1·V T =70.7775.

[0204] Based on the data in Table 3, the fuzzy evaluation scores for the other four monitoring points were calculated using the same method. These scores were then summarized to obtain the comprehensive power quality score for the five monitoring points, as shown in Table 4.

[0205] Table 4 Power Quality Assessment Scores at Monitoring Points

[0206]

[0207] The data in Table 4 shows that the power quality of the five monitoring points, ranked from best to worst, is as follows: monitoring point 4, monitoring point 1, monitoring point 2, monitoring point 3, and monitoring point 5.

[0208] The calculated subjective weights indicate that experts are most concerned about voltage deviation. The objective weights show significant differences in voltage deviation indicators across different monitoring points. Therefore, addressing voltage deviation should be the first priority in improving power quality in distribution networks. Voltage sag, however, has the lowest weight in the overall weighting. As a short-term disturbance with a low frequency, voltage sag is less harmful than other issues, and its low weighting aligns with reality.

[0209] As shown in Table 4, the power quality assessment scores of each monitoring point indicate that the power quality of monitoring point 4 is relatively good, the power quality of monitoring points 1 and 2 is average, and the power quality of monitoring points 3 and 5 is poor. This assessment result has certain guiding significance for the pricing of electricity products based on quality. For example, the distribution price of monitoring point 4 can be set higher than that of other monitoring points.

[0210] Example 3

[0211] According to an embodiment of the present invention, a power quality comprehensive evaluation device for distribution networks based on G1 and CRITIC is also provided. It should be noted that the power quality comprehensive evaluation device for distribution networks based on G1 and CRITIC in this embodiment can be used to execute the power quality comprehensive evaluation method for distribution networks based on G1 and CRITIC in Embodiment 1 of the present invention.

[0212] Figure 4 This is a schematic diagram of a power quality comprehensive evaluation device for distribution networks based on G1 and CRITIC according to an embodiment of the present invention. Figure 4 As shown, the distribution network power quality comprehensive evaluation device 40 based on G1 and CRITIC may include:

[0213] The acquisition module 402 is used to acquire power grid data obtained by monitoring at the monitoring points of the distribution network. The power grid data includes at least all initial data under different evaluation indicators. The different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics.

[0214] The first calculation module 404 is used to calculate the subjective weight of each evaluation indicator using the ordinal relation analysis method and to calculate the objective weight of each evaluation indicator using the objective weighting method.

[0215] The second calculation module 406 is used to calculate the comprehensive weight corresponding to each evaluation index based on the subjective weight and the objective weight;

[0216] The evaluation module 408 is used to determine the initial data corresponding to each evaluation index from all the initial data, and to use a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network.

[0217] Using the above device, firstly, power grid data monitored at monitoring points in the distribution network is acquired. This power grid data includes at least all initial data under different evaluation indicators, which include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. Then, for each evaluation indicator, the subjective weight is calculated using order relation analysis, and the objective weight is calculated using objective weighting. Next, the comprehensive weight for each evaluation indicator is calculated based on the subjective and objective weights. Finally, the initial data corresponding to each evaluation indicator is determined from all the initial data, and a fuzzy algorithm combined with the comprehensive weights is used to quantitatively evaluate the power quality of the distribution network. This invention analyzes the factors affecting power quality in distribution networks and the power quality problems caused by these factors, and proposes different evaluation indicators for power quality in distribution networks accordingly. Then, it uses the G1 method and the CRITIC method to determine subjective and objective weights, calculates the comprehensive weight, and uses the fuzzy comprehensive evaluation method to quantitatively evaluate the power quality of the distribution network. This solves the technical problem that the evaluation results of power quality assessment in distribution networks do not conform to the actual situation of the distribution network and have low accuracy, thus improving the accuracy of the comprehensive evaluation results of power quality in distribution networks.

[0218] In an exemplary embodiment, the first calculation module is further configured to: sort the different evaluation indicators according to their importance to obtain a sorted set, wherein each evaluation indicator in the sorted set has a sorting number, and the sorting number corresponds to the importance of the evaluation indicator; and determine two adjacent evaluation indicators from the sorted set. and in, This represents the (k-1)th evaluation index. Represents the k-th evaluation index; determine and The ratio of importance between them r k ; in, for The weight coefficient represents the subjective weight of the (k-1)th evaluation indicator. for The weight coefficient represents the subjective weight of the k-th evaluation indicator; based on the importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index

[0219] In one exemplary embodiment, the first calculation module is further configured to: calculate the weight of the k-th evaluation index using the following formula. Where i, n, and k are positive integers; for the (k-1)th evaluation indicator, the weight of the (k-1)th evaluation indicator is calculated using the following formula:

[0220] In an exemplary embodiment, the first calculation module is further configured to: normalize the initial data matrix X corresponding to all the initial data to obtain a standardized data matrix Y, wherein X = (x ij ) m×n , Y = (y ij ) m×n , x ij Let y represent the indicator data of the j-th evaluation indicator at the i-th monitoring point. ij Let represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, n represent the number of evaluation indicators, m represent the number of monitoring points, and max(x) = 1. j ) represents the maximum index data at different monitoring points under the j-th evaluation index, min(x j Let represent the minimum indicator data at different monitoring points under the j-th evaluation indicator; calculate the information content based on the indicator variation coefficient and indicator conflict coefficient of the standardized data matrix, wherein the indicator variation coefficient is used to quantify the comparative strength of the evaluation indicator, and the indicator conflict coefficient is used to represent the degree of difference between the maximum and minimum indicator data; normalize each column of the information content, and obtain the set of objective weights corresponding to the evaluation indicator based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators.

[0221] In one exemplary embodiment, the first calculation module is further configured to: calculate the average value of the data in the j-th column of the standardized data matrix. And calculate the standard deviation s of the data in the j-th column. j The coefficient of variation v of the index for the data in the j-th column is calculated using the following formula. j : in,

[0222] In one exemplary embodiment, the first calculation module is further configured to: calculate the correlation coefficient r between different evaluation indicators. ij ,in, s i Let s represent the standard deviation corresponding to the i-th evaluation indicator. jThis represents the standard deviation corresponding to the j-th evaluation indicator. The covariance between the i-th and j-th columns of the standardized data matrix is ​​represented by the following formula; the index conflict coefficient A of the j-th column data is calculated using the following formula. j :

[0223] In one exemplary embodiment, the first calculation module is further configured to: calculate based on the coefficient of variation v of the index of the j-th column data. j The coefficient of conflict A between the index and the data in column j j Calculation of information content E j E j =v j ×A j Normalize each column of data in the information quantity, including: normalizing E using the following formula. j Normalize: Where, θ j This represents the objective weight corresponding to the j-th evaluation indicator.

[0224] In an exemplary embodiment, the evaluation module is further configured to: denot the selected different evaluation indicators as U1, U2, U3, U4, and U5 respectively, and construct a factor set U: {U1, U2, U3, U4, U5}; divide the power quality into 5 intervals, from best to worst, namely “excellent”, “good”, “medium”, “poor”, and “extremely poor”, denoted as V1, V2, V3, V4, and V5 respectively, and construct an evaluation set V: {V1, V2, V3, V4, V5}.

[0225] Power quality is classified into good and bad levels, resulting in the following evaluation set V:

[0226] V = [Good, Fair, Average, Poor]

[0227] =[95 80 70 55 40],

[0228] The membership degree of different comments of the index is calculated using a Gaussian membership function f(y).

[0229]

[0230] Where y is the monitoring data of the distribution network evaluation index, σ and c are set parameters. σ is set to 0.3, c is initially 1, and gradually decreases to 0 with an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding c value. The membership function corresponding to each evaluation set is calculated.

[0231] The evaluation value y of the indicator ij Combining with Gaussian membership functions, we obtain the evaluation matrix F of the index:

[0232]

[0233] in, It is the indicator y ij For rating level V k The degree of membership;

[0234] use The operator performs a fuzzy product operation on the weights and the evaluation matrix to obtain the overall evaluation score of the power quality of the distribution network. The formula for the fuzzy product operation is expressed as follows:

[0235] B i =[b i (V1)b i (V2)b i (V3)b i (V4)b i (V5)]

[0236] in: b i (V k () is a rating symbol V that indicates the degree of membership between indicators. k ;

[0237] Use formula Calculate the overall assessment score of the power quality of the distribution network, and determine the quality of the power quality of the distribution network based on the corresponding power quality assessment results and the quantitative grading intervals of the power quality of the distribution network.

[0238] Example 4

[0239] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes the distribution network power quality comprehensive evaluation method based on G1 and CRITIC in Embodiment 1.

[0240] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0241] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: acquiring power grid data obtained from monitoring points in the distribution network, wherein the power grid data includes at least all initial data under different evaluation indicators, and the different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics; for the different evaluation indicators, calculating the subjective weight corresponding to each evaluation indicator using the ordinal relation analysis method, and calculating the objective weight corresponding to each evaluation indicator using the objective weighting method; calculating the comprehensive weight corresponding to each evaluation indicator based on the subjective weight and the objective weight; determining the initial data corresponding to each evaluation indicator from all the initial data, and using a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network.

[0242] First, power grid data is acquired from monitoring points in the distribution network. This data includes at least all initial data under different evaluation indicators, which include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. Then, for each evaluation indicator, a sequence relation analysis method is used to calculate the subjective weight, and an objective weighting method is used to calculate the objective weight. Next, a comprehensive weight is calculated based on the subjective and objective weights. Finally, the initial data corresponding to each evaluation indicator is determined from all the initial data, and a fuzzy algorithm combined with the comprehensive weight is used to quantitatively evaluate the power quality of the distribution network. This invention analyzes the factors affecting power quality in distribution networks and the power quality problems caused by these factors, and proposes different evaluation indicators for power quality in distribution networks accordingly. Then, it uses the G1 method and the CRITIC method to determine subjective and objective weights, calculates the comprehensive weight, and uses the fuzzy comprehensive evaluation method to quantitatively evaluate the power quality of the distribution network. This solves the technical problem that the evaluation results of power quality assessment in distribution networks do not conform to the actual situation of the distribution network and have low accuracy, thus improving the accuracy of the comprehensive evaluation results of power quality in distribution networks.

[0243] Optionally, the aforementioned computer-readable storage medium may also execute program code that performs the following steps: sorting the different evaluation indicators according to their importance to obtain a sorted set, wherein each evaluation indicator in the sorted set has a sorting number, and the sorting number corresponds to the importance of the evaluation indicator; determining two adjacent evaluation indicators from the sorted set. and in, This represents the (k-1)th evaluation index. Represents the k-th evaluation index; determine and The ratio of importance between them r k ; in, for The weight coefficient represents the subjective weight of the (k-1)th evaluation indicator. for The weight coefficient represents the subjective weight of the k-th evaluation indicator; based on the importance ratio r k Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index

[0244] Optionally, the aforementioned computer-readable storage medium may also execute program code that performs the following steps: For the k-th evaluation indicator, calculate the weight of the k-th evaluation indicator using the following formula. Where i, n, and k are positive integers; for the (k-1)th evaluation indicator, the weight of the (k-1)th evaluation indicator is calculated using the following formula:

[0245] Optionally, the computer-readable storage medium described above may also execute program code that performs the following steps: normalizes the initial data matrix X corresponding to all the initial data to obtain a standardized data matrix Y, where X = (x ij ) m×n , Y = (y ij ) m×n , x ij Let y represent the indicator data of the j-th evaluation indicator at the i-th monitoring point. ij Let represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, n represent the number of evaluation indicators, m represent the number of monitoring points, and max(x) = 1. j ) represents the maximum index data at different monitoring points under the j-th evaluation index, min(x j Let represent the minimum indicator data at different monitoring points under the j-th evaluation indicator; calculate the information content based on the indicator variation coefficient and indicator conflict coefficient of the standardized data matrix, wherein the indicator variation coefficient is used to quantify the comparative strength of the evaluation indicator, and the indicator conflict coefficient is used to represent the degree of difference between the maximum and minimum indicator data; normalize each column of the information content, and obtain the set of objective weights corresponding to the evaluation indicator based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators.

[0246] Optionally, the computer-readable storage medium described above may also execute program code that performs the following steps: calculating the average value of the data in the j-th column of the normalized data matrix. And calculate the standard deviation s of the data in the j-th column. j The coefficient of variation v of the index for the data in the j-th column is calculated using the following formula. j : in,

[0247] Optionally, the aforementioned computer-readable storage medium may also execute program code that performs the following steps: calculating the correlation coefficient r between different evaluation indicators. ij ,in, s i Let s represent the standard deviation corresponding to the i-th evaluation indicator. j This represents the standard deviation corresponding to the j-th evaluation indicator. The covariance between the i-th and j-th columns of the standardized data matrix is ​​represented by the following formula; the index conflict coefficient A of the j-th column data is calculated using the following formula. j :

[0248] Optionally, the aforementioned computer-readable storage medium may also execute program code that performs the following steps: based on the coefficient of variation v of the index of the j-th column data. j The coefficient of conflict A between the index and the data in column j j Calculation of information content E j E j =v j ×A j Normalize each column of data in the information quantity, including: normalizing E using the following formula. j Normalize: Where, θ j This represents the objective weight corresponding to the j-th evaluation indicator.

[0249] Optionally, the computer-readable storage medium described above may also include program code that executes the following steps:

[0250] The selected evaluation indicators are denoted as U1, U2, U3, U4, and U5, respectively, and a factor set U is constructed as {U1, U2, U3, U4, U5}. The power quality is divided into 5 intervals from best to worst: “Excellent”, “Good”, “Medium”, “Poor”, and “Extremely Poor”, respectively, and denoted as V1, V2, V3, V4, and V5, respectively, and an evaluation set V is constructed as {V1, V2, V3, V4, V5}.

[0251] Power quality is classified into good and bad levels, resulting in the following evaluation set V:

[0252] V = [Good, Fair, Average, Poor]

[0253] =[95 80 70 55 40],

[0254] The membership degree of different comments of the index is calculated using a Gaussian membership function f(y).

[0255]

[0256] Where y is the monitoring data of the distribution network evaluation index, σ and c are set parameters. σ is set to 0.3, c is initially 1, and gradually decreases to 0 with an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding c value. The membership function corresponding to each evaluation set is calculated.

[0257] The evaluation value y of the indicator ij Combining with Gaussian membership functions, we obtain the evaluation matrix F of the index:

[0258]

[0259] in, It is the indicator y ij For rating level V k The degree of membership;

[0260] use The operator performs a fuzzy product operation on the weights and the evaluation matrix to obtain the overall evaluation score of the power quality of the distribution network. The formula for the fuzzy product operation is expressed as follows:

[0261] B i =[b i (V1) b i (V2) b i (V3) b i (V4) b i (V5)],

[0262] in: b i (V k () is a rating symbol V that indicates the degree of membership between indicators. k ;

[0263] Use formula Calculate the overall assessment score of the power quality of the distribution network, and determine the quality of the power quality of the distribution network based on the corresponding power quality assessment results and the quantitative grading intervals of the power quality of the distribution network.

[0264] Example 5

[0265] According to an embodiment of the present invention, a computer program product is also provided, the computer program product including computer instructions, wherein when the computer instructions are executed by a processor, the method for comprehensive evaluation of power quality of distribution networks based on G1 and CRITIC in Embodiment 1 is implemented.

[0266] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0267] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0268] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0269] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0270] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0271] If the integrated unit is implemented as 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, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0272] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive evaluation method for power quality in distribution networks based on G1 and CRITIC, characterized in that, include: The power grid data obtained from monitoring points in the distribution network includes at least all initial data under different evaluation indicators, and the different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. For the different evaluation indicators, the subjective weight corresponding to each evaluation indicator is calculated using the ordinal relation analysis method, and the objective weight corresponding to each evaluation indicator is calculated using the objective weighting method. The comprehensive weight corresponding to each evaluation indicator is calculated based on the subjective weight and the objective weight. The initial data corresponding to each evaluation index is determined from all the initial data, and the power quality of the distribution network is quantitatively evaluated by using a fuzzy algorithm combined with the comprehensive weight. The objective weighting method is used to calculate the objective weight corresponding to each evaluation indicator, including: The initial data matrix X corresponding to all the initial data is normalized to obtain the standardized data matrix Y. in, , , , , This represents the indicator data of the j-th evaluation indicator at the i-th monitoring point. Let represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, where n represents the number of evaluation indicators and m represents the number of monitoring points. This represents the maximum index data at different monitoring points under the j-th evaluation index. This represents the minimum index data for different monitoring points under the j-th evaluation index; The information content is calculated based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix, wherein the coefficient of variation of the indicators is used to quantify the comparative strength of the evaluation indicators, and the coefficient of conflict of the indicators is used to represent the degree of difference between the maximum indicator data and the minimum indicator data. Normalize each column of data in the information quantity, and obtain the set of objective weights corresponding to the evaluation index based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators; The method further includes, before calculating the information content based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix: Calculate the average value of the data in the j-th column of the standardized data matrix. And calculate the standard deviation of the data in the j-th column. ; The coefficient of variation of the index for the data in column j is calculated using the following formula. : ， in, , ; The method further includes, before calculating the information content based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix: Calculate the correlation coefficient between different evaluation indicators ,in, , This represents the standard deviation corresponding to the i-th evaluation indicator. This represents the standard deviation corresponding to the j-th evaluation indicator. This represents the covariance between the data in the i-th column and the data in the j-th column of the standardized data matrix; The index conflict coefficient of the data in column j is calculated using the following formula. : ； The calculation of information content based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix includes: Based on the coefficient of variation of the index in column j Conflict coefficient with the index of the j-th column data Calculate information content : ， Normalize each column of data in the information volume, including: using the following formula... Normalize: ,in, This represents the objective weight corresponding to the j-th evaluation indicator.

2. The method according to claim 1, characterized in that, The subjective weight corresponding to each evaluation indicator is calculated using the ordinal relation analysis method, including: The different evaluation indicators are ranked according to their importance to obtain a ranking set, wherein each evaluation indicator in the ranking set has a ranking number, and there is a corresponding relationship between the ranking number and the importance of the evaluation indicator. Determine two adjacent evaluation indicators from the sorted set. and ,in, This represents the (k-1)th evaluation index. This represents the k-th evaluation indicator; Sure and The ratio of importance between ; ； in, for The weight coefficient represents the subjective weight of the (k-1)th evaluation indicator. for The weight coefficient represents the subjective weight of the k-th evaluation indicator. ; Based on the aforementioned importance ratio Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index .

3. The method according to claim 2, characterized in that, Based on the aforementioned importance ratio Calculate the subjective weight of the k-th evaluation index respectively. and the subjective weight of the (k-1)th evaluation index ,include: For the k-th evaluation indicator, the subjective weight of the k-th evaluation indicator is calculated using the following formula. : ， Where i, n, and k are positive integers. This represents the ratio of the importance of the (i-1)th evaluation indicator to the ith evaluation indicator. For the (k-1)th evaluation indicator, the subjective weight of the (k-1)th evaluation indicator is calculated using the following formula: 。 4. The method according to claim 1, characterized in that, The power quality of the distribution network is quantitatively assessed using a fuzzy algorithm combined with the aforementioned comprehensive weights, including: The different selected evaluation indicators are denoted as U. 1、 U 2、 U 3、 U 4、 U 5， Construct a factor set U: {U1, U2, U3, U4, U5}; divide the power quality into 5 intervals from best to worst: "excellent", "good", "medium", "poor", and "extremely poor", denoted as V1, V2, V3, V4, and V5 respectively, and construct an evaluation set V: {V1, V2, V3, V4, V5}; Power quality is classified into good and bad levels, resulting in the following evaluation set V: ， Through Gaussian membership functions The membership degree of different comments for the indicators is calculated. ; in, For monitoring data of power distribution network evaluation indicators, and These are the set parameters, settings. The initial value of c is 1, and it is gradually reduced to 0 at an arithmetic decrease rate of 0.25 to ensure that each evaluation membership degree has its own corresponding value. The membership function corresponding to each evaluation set is calculated based on the value. The evaluation value of the indicator By combining it with Gaussian membership functions, the evaluation matrix of the index is obtained. : ； in, It is an indicator For the rating level The degree of membership; use The operator performs a fuzzy product operation on the weights and the evaluation matrix to obtain the overall evaluation score of the power quality of the distribution network. The formula for the fuzzy product operation is expressed as follows: ， in: , It is a comment indicating the degree of membership between indicators. , The comprehensive weight corresponding to the i-th evaluation indicator; Use formula Calculate the overall assessment score of the power quality of the distribution network, and determine the quality of the power quality of the distribution network based on the corresponding power quality assessment results and the quantitative grading intervals of the power quality of the distribution network.

5. A comprehensive power quality evaluation device for distribution networks based on G1 and CRITIC, characterized in that, include: The acquisition module is used to acquire power grid data obtained by monitoring at monitoring points in the distribution network. The power grid data includes at least all initial data under different evaluation indicators, and the different evaluation indicators include at least one of the following: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, and harmonics. The first calculation module is used to calculate the subjective weight of each evaluation indicator using the ordinal relation analysis method and to calculate the objective weight of each evaluation indicator using the objective weighting method. The second calculation module is used to calculate the comprehensive weight corresponding to each evaluation indicator based on the subjective weight and the objective weight; The evaluation module is used to determine the initial data corresponding to each evaluation indicator from all the initial data, and to use a fuzzy algorithm combined with the comprehensive weight to quantitatively evaluate the power quality of the distribution network. The first calculation module is further configured to normalize the initial data matrix X corresponding to all the initial data to obtain a standardized data matrix Y. in, , , , , This represents the indicator data of the j-th evaluation indicator at the i-th monitoring point. Let represent the standardized data of the j-th evaluation indicator at the i-th monitoring point, where n represents the number of evaluation indicators and m represents the number of monitoring points. This represents the maximum index data at different monitoring points under the j-th evaluation index. This represents the minimum index data for different monitoring points under the j-th evaluation index; The information content is calculated based on the coefficient of variation of the indicators in the standardized data matrix and the coefficient of conflict of the indicators in the standardized data matrix, wherein the coefficient of variation of the indicators is used to quantify the comparative strength of the evaluation indicators, and the coefficient of conflict of the indicators is used to represent the degree of difference between the maximum indicator data and the minimum indicator data. Normalize each column of data in the information quantity, and obtain the set of objective weights corresponding to the evaluation index based on the normalization result, wherein the set of objective weights includes the objective weights corresponding to the different evaluation indicators; The first calculation module is further configured to calculate the average value of the j-th column of the standardized data matrix. And calculate the standard deviation of the data in the j-th column. ; The coefficient of variation of the index for the data in column j is calculated using the following formula. : ， in, , ; The first calculation module is further configured to calculate the correlation coefficients between different evaluation indicators. ,in, , This represents the standard deviation corresponding to the i-th evaluation indicator. This represents the standard deviation corresponding to the j-th evaluation indicator. This represents the covariance between the data in the i-th column and the data in the j-th column of the standardized data matrix; The index conflict coefficient of the data in column j is calculated using the following formula. : ； The first calculation module is further configured to calculate the coefficient of variation of the index based on the data in the j-th column. Conflict coefficient with the index of the j-th column data Calculate information content : ， Normalize each column of data in the information volume, including: using the following formula... Normalize: ,in, This represents the objective weight corresponding to the j-th evaluation indicator.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 4.

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