Distribution transformer tap adjustment method based on comprehensive analysis and evaluation

Abstract

The distribution transformer tap adjustment method based on comprehensive analysis and evaluation includes: acquiring distribution substation operation data and related data of the distribution transformer; establishing a comprehensive evaluation system index; establishing an optimization model for distribution transformer tap adjustment; and using the NSGA-II algorithm based on a subjective-objective coupled weighting method to optimize the distribution transformer taps and obtain the optimal adjustment command. This method not only considers traditional voltage quality and equipment loss issues but also introduces comprehensive indicators such as distribution transformer utilization rate. Valuable information is extracted from a large amount of operational data. Through the analysis and processing of historical data, relevant models are established, and multi-objective optimization algorithms are used to enable the distribution transformer to provide the optimal tap adjustment command based on real-time data and multi-dimensional evaluation indicators.

Description

Technical Field

[0001] This invention relates to the field of distribution transformer tap adjustment strategy technology, and specifically to a distribution transformer tap adjustment method based on comprehensive analysis and evaluation. Background Technology

[0002] In recent years, with the continuous development of society and the economy, problems such as complex grid structures, transformer overload operation, and unreasonable transformer tap adjustments in distribution substations have become increasingly prominent, leading to voltage deviations in distribution substations and directly affecting the power comfort of users. Distribution transformers are indispensable and crucial equipment in the power system, primarily converting high-voltage electrical energy into low-voltage electrical energy suitable for user use. Their stable and efficient operation plays a key role in the overall reliability of the power system and the quality of power supply for users. To ensure the efficient and stable operation of distribution transformers, tap adjustments are typically required based on load conditions and voltage levels. However, traditional tap adjustment methods mainly rely on periodic manual inspections and experience-based judgment, which suffers from poor real-time performance and low accuracy. Due to the limited frequency of manual inspections, the actual operating status of the transformer cannot be reflected in a timely manner, resulting in delayed adjustments. Furthermore, experience-based judgment is subjective and makes it difficult to guarantee the scientific accuracy of adjustments.

[0003] With the rapid development of big data and intelligent technologies, the automation and intelligence levels of power distribution systems have been significantly improved. Traditional distribution transformer tap-level regulation methods typically rely on preset rules and static parameters, which cannot effectively cope with the dynamic power grid operating environment and constantly changing load demands. This often leads to problems such as low regulation accuracy, unstable voltage quality, and low energy utilization efficiency. To address these challenges, recent research has gradually introduced advanced technologies such as big data analysis, machine learning, and optimization algorithms to achieve more intelligent tap-level regulation strategies. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for adjusting the tap position of a distribution transformer based on comprehensive analysis and evaluation. This method not only considers traditional voltage quality and equipment loss issues but also incorporates comprehensive indicators such as transformer utilization rate. Valuable information is extracted from a large amount of operational data. Through analysis and processing of historical data, relevant models are established, and multi-objective optimization algorithms are used to enable the distribution transformer to provide optimal tap position adjustments based on real-time data and multi-dimensional evaluation indicators.

[0005] The technical solution adopted in this invention is as follows:

[0006] The distribution transformer tap adjustment method based on comprehensive analysis and evaluation includes the following steps:

[0007] Step 1: Obtain the operating data of the distribution substation and related data of the distribution transformer;

[0008] Step 2: Establish a comprehensive evaluation system of indicators;

[0009] Step 3: Establish an optimization model for distribution transformer tap adjustment;

[0010] Step 4: Use the NSGA-II algorithm based on the subjective-objective coupling weighting method to optimize the tap position of the distribution transformer and obtain the optimal adjustment command.

[0011] In step 2, the comprehensive evaluation system indicators include three primary indicators: voltage quality pass rate, key equipment loss in the distribution substation, and distribution transformer utilization rate. Among them, voltage quality pass rate and key equipment loss in the distribution substation are further divided into two secondary indicators. Voltage quality pass rate includes voltage deviation and voltage over-limit time. Key equipment loss in the distribution substation includes transformer loss and line loss. Distribution transformer utilization rate is the average utilization rate of the distribution transformers in the distribution substation.

[0012] In step 3, the establishment of the distribution transformer tap adjustment optimization model is as follows:

[0013] With the objective functions of maximizing voltage quality compliance rate, minimizing losses of key equipment in distribution substations, and maximizing transformer utilization, the following model is constructed:

[0014] ;

[0015] In the formula: The objective function for the optimization model of tap position adjustment of distribution transformers; , , These are the objective function values ​​for voltage quality qualification rate, key equipment loss in distribution substation area, and distribution transformer utilization rate, respectively. , , They are respectively , , The weighting coefficients.

[0016] Voltage quality pass rate is measured using voltage deviation and voltage over-limit duration, as shown below:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula: , The objective functions are voltage deviation and voltage over-limit duration, respectively; T is the set of time periods. This refers to the set of all nodes in the distribution radio area; , They are nodes The voltage amplitude and the rated voltage amplitude at time t; , They are nodes The duration of voltage over-limit and the standard value of voltage over-limit at time t.

[0021] The losses of key equipment in a distribution substation are measured by considering transformer losses and line losses in the distribution substation area, as shown below:

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula: For the loss of key equipment in the distribution radio area; , These are the objective functions for transformer losses and network line losses in the distribution substation, respectively. , , These are the transformer's no-load loss, capacity, and comprehensive power rated load loss, respectively. , These represent the actual active power and reactive power output of the transformer, respectively. To collect all branch lines in the distribution area; branch at time t The current, This is a branch road. The resistance.

[0026] The utilization rate of distribution transformers is expressed as follows:

[0027] ;

[0028] In the formula: For transformer utilization rate.

[0029] Constraints include power flow constraints, transformer-related constraints, and network operation safety constraints:

[0030] 1) Power flow constraints are expressed as follows:

[0031] ;

[0032] in, Representing nodes respectively , Voltage amplitude at time t; , Representing branch roads Active power and reactive power at time t; Indicates a branch The line reactance;

[0033] 2) Constraints on the optimal economic operating range of the transformer:

[0034] ;

[0035] 3) Transformer capacity constraints:

[0036] ;

[0037] in, This represents the actual capacity of the transformer.

[0038] 4) Limitations on the number of transformer tap changes:

[0039] ;

[0040] In the formula, , These represent the actual number of adjustments made to the distribution transformer and the maximum allowed number of adjustments, respectively.

[0041] 5) Time constraints for transformer tap changing:

[0042] ;

[0043] In the formula, , These represent the times of the two most recent transformer tap changes. This indicates the minimum value of the transformer's tap change time.

[0044] 6) Network operation security constraints:

[0045] ;

[0046] In the formula: For nodes The voltage; , They are nodes The upper and lower limits of the voltage; branch road The current, Then it is its maximum value; For nodes power, , These represent the upper and lower limits of the node power, respectively.

[0047] In step 4, the weights of each objective function are selected by the subjective-objective coupling weighting method, and the transformer tap position is optimized by the NSGA-II algorithm. The most satisfactory solution that is most conducive to the optimal regulation of the distribution transformer is selected from the final Pareto solution set and used as the optimal regulation command.

[0048] By coupling subjective and objective weights using the multiplier normalization method, the drawbacks of a single method are avoided, ultimately yielding a comprehensive weight:

[0049] ;

[0050] In the formula: This is the overall weighting coefficient; This refers to the subjective weighting coefficient. This is the objective weighting coefficient.

[0051] The selection process utilizes a combined subjective and objective weighting method. The subjective weighting coefficients are set by the decision-maker, while the objective weighting method employs entropy weighting. Entropy weighting is a weighting method based on information entropy. It determines the degree of influence of each indicator on the evaluation system by calculating its entropy value. The lower the entropy value, the greater the information content of the indicator, and the higher its weight. The calculation process of entropy weighting is shown below:

[0052] Data standardization: The original data of each evaluation indicator are normalized to obtain a standardized matrix.

[0053] ;

[0054] In the formula: It is the first The sample at the th Standard values ​​for each indicator; It is the first The sample at the th Values ​​under each indicator; and They are the first The minimum and maximum values ​​of each indicator.

[0055] Calculate the weighting: Calculate the weight of each indicator in the standardized matrix. .

[0056] ;

[0057] In the formula: That is the number of samples.

[0058] Calculate information entropy: based on proportion Calculate the entropy value of each indicator. .

[0059] ;

[0060] In the formula: It is a constant.

[0061] Calculate entropy weights: Calculate weights based on information entropy. .

[0062] ;

[0063] In the formula: It refers to the number of indicators.

[0064] This invention provides a method for adjusting the tap position of a distribution transformer based on comprehensive analysis and evaluation, with the following technical advantages:

[0065] 1) This invention proposes an optimization strategy for distribution transformer tap adjustment based on comprehensive analysis and evaluation. It establishes multi-level indicators to evaluate the final result, which can simultaneously consider three key factors: voltage quality, equipment loss and distribution transformer utilization rate, thereby improving the comprehensiveness and effectiveness of decision-making.

[0066] 2) This invention utilizes the NSGA-II algorithm based on a subjective-objective coupled weighting method to find the optimal gear position, ultimately determining the optimal solution through continuous iteration. Furthermore, decision-makers can dynamically adjust the weights according to actual needs and combine objective requirements to determine the emphasizing effect of different adjustment schemes to determine the effectiveness of gear position adjustment, achieving intelligent and humanized evaluation of adjustment effects.

[0067] 3) This invention comprehensively analyzes the influencing factors of distribution transformer tap adjustment, and establishes an optimization model for distribution transformer tap adjustment based on five aspects: voltage deviation, voltage over-limit time, transformer loss, line loss and distribution transformer utilization rate. Using a set of weights as representatives, and combining a multi-objective model, the final result is whether the distribution transformer tap adjustment strategy is reasonable and effective.

[0068] 4) The analysis results show that the distribution transformer tap adjustment model based on comprehensive analysis and evaluation can fully integrate many factors and accurately reflect the operating status of the distribution transformer. Distribution transformer operation decision-makers can take timely and effective targeted tap adjustment schemes according to different emphase effects, and can adapt to the actual operating conditions of distribution transformers in different areas.

[0069] 5) The transformer tap adjustment strategy based on comprehensive analysis and evaluation in this invention can not only significantly improve the operating efficiency and reliability of distribution transformers and ensure the stability and high quality of power supply, but also reduce manual intervention and transformer losses, improve the level of automation, and greatly improve the real-time performance and accuracy of adjustment. Attached Figure Description

[0070] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0071] Figure 1 This invention provides a framework for a distribution transformer tap adjustment strategy based on comprehensive analysis and evaluation.

[0072] Figure 2 This is a schematic diagram of the comprehensive evaluation system for distribution transformation and adjustment.

[0073] Figure 3 The flowchart shows the NSGA-II algorithm based on the subjective-objective coupling weighting method for tap optimization in distribution transformers.

[0074] Figure 4 This is a diagram illustrating the distance of localized congestion.

[0075] Figure 5 A simplified diagram of the distribution area.

[0076] Figure 6 This diagram illustrates the number of individuals corresponding to the Pareto optimal solution. Detailed Implementation

[0077] A distribution transformer tap adjustment method based on comprehensive analysis and evaluation, applied to low-voltage distribution networks, includes the following steps:

[0078] (1) Establish a distribution transformer tap adjustment strategy framework based on comprehensive analysis and evaluation:

[0079] like Figure 1 As shown, the distribution transformer tap adjustment method mainly consists of four steps: data acquisition and processing module, distribution transformer tap adjustment strategy optimization model, optimization algorithm solution, and issuing the optimal adjustment command. Relying on the existing distribution substation operation and maintenance platform, one year's operating data and related data of the distribution transformer for this distribution substation are obtained, as shown in Table 1.

[0080]

[0081] (2) Establish a distribution transformer tap optimization model based on comprehensive analysis and evaluation:

[0082] A comprehensive evaluation system should be able to reflect the key indicators of the distribution transformer optimization and regulation strategy. By sorting out and analyzing the distribution transformer optimization and regulation strategy, four key components of the comprehensive evaluation system are identified. Taking the optimization of distribution transformers to solve the problem of poor voltage quality in the distribution area as the governance objective, a multi-objective optimization governance model is established to maximize the voltage quality qualification rate, minimize the loss of key equipment in the distribution area, and maximize the utilization rate of distribution transformers.

[0083] The comprehensive evaluation system indicators established in this invention are as follows: Figure 2As shown, the voltage quality pass rate consists of three primary indicators: the critical equipment loss in the distribution substation area and the transformer utilization rate. The voltage quality pass rate and the critical equipment loss in the distribution substation area are further divided into two secondary indicators. The voltage quality pass rate is mainly composed of voltage deviation and voltage over-limit duration. For the critical equipment loss in the distribution substation area, this invention only considers transformer losses and line losses. The transformer utilization rate is the average utilization rate of the transformers in the distribution substation area.

[0084] (3) NSGA-II algorithm based on subjective-objective coupling weighting method

[0085] Since the objective function of this invention is a multi-objective function, the final function result is related to the value of the weights. As can be seen from Table 2, different weight coefficients for the three objective functions will result in different optimal solutions.

[0086]

[0087] Therefore, choosing appropriate weighting coefficients has a crucial impact on finding the optimal solution. This invention utilizes a subjective-objective coupled weighting method to optimize the weighting coefficients of each objective function. Subjective weighting methods are suitable for scenarios with incomplete data or difficult-to-quantify indicators, but they rely on expert experience and judgment; while objective weighting methods are suitable for situations with sufficient data and where it is desirable to avoid human bias, but they may overlook practical experience. Therefore, this invention uses the multiplier normalization method to couple subjective and objective weights, avoiding the drawbacks of a single method, and ultimately obtaining a comprehensive weight:

[0088] ;

[0089] In the formula: This is the overall weighting coefficient; This refers to the subjective weighting coefficient. This is the objective weighting coefficient.

[0090] The weights of each objective function are selected using a subjective-objective coupled weighting method. Then, the NSGA-II algorithm is used to optimize the transformer tap position. From the final Pareto solution set, the most satisfactory solution, which is most beneficial to the optimal regulation of the distribution transformer, is selected and used as the optimal regulation command. The algorithm is implemented using the GAMULTIOBJ solver in MATLAB.

[0091] Working principle

[0092] (1) Objective function for optimizing the tap adjustment of distribution transformers:

[0093] Based on the existing distribution substation operation and maintenance platform, real-time operation data of the distribution substation and related data of the distribution transformer are obtained. With the objective functions of maximizing voltage quality qualification rate, minimizing the loss of key equipment in the distribution substation and maximizing the utilization rate of distribution transformers, the following model is constructed.

[0094] ;

[0095] In the formula: , , These are the objective function values ​​for voltage quality qualification rate, key equipment loss in distribution substation area, and distribution transformer utilization rate, respectively. , , They are respectively , , The weighting coefficients.

[0096] Voltage quality is a crucial indicator describing the reliability of a distribution substation and the user's electricity experience. Voltage deviation and voltage over-limit duration are the most direct indicators of voltage quality, visually reflecting the success rate of transformer tap adjustment optimization. Therefore, this invention uses voltage deviation and voltage over-limit duration to measure the voltage quality pass rate, as shown below:

[0097] ;

[0098] ;

[0099] ;

[0100] In the formula: , The objective functions are voltage deviation and voltage over-limit duration, respectively; T is the set of time periods. This refers to the set of all nodes in the distribution radio area; , They are nodes The voltage amplitude and the rated voltage amplitude at time t; , They are nodes The duration of voltage over-limit and the standard value of voltage over-limit at time t.

[0101] Many countries and regions have clear standards and requirements for distribution network losses. Analyzing the losses of a distribution substation can not only reflect its economic and environmental benefits, but its magnitude can also reflect the rationality and feasibility of the proposed technical solution to a certain extent. Therefore, this invention analyzes the losses of key equipment in a distribution substation. As one of the important pieces of equipment in a distribution substation, the power loss of the transformer is a significant component of the total distribution network losses, accounting for approximately 40% to 70%. Therefore, this invention considers transformer losses and line losses in the distribution substation to measure the losses of key equipment in the distribution substation, as shown below:

[0102] ;

[0103] ;

[0104] ;

[0105] In the formula: , These are the objective functions for transformer losses and network line losses in the distribution substation, respectively. , , These are the transformer's no-load loss, capacity, and overall rated load loss. , These represent the actual active power and reactive power output of the transformer, respectively. To collect all branch lines in the distribution area; branch at time t The current, This is a branch road. The resistance.

[0106] Since this invention mainly focuses on the optimization and adjustment strategy of transformer taps in distribution substations, it uses the utilization rate of distribution transformers as one of the indicators. This not only reflects the current development status of the distribution substation but also provides timely reference for staff. The indicator is as follows:

[0107] ;

[0108] (2) Constraints:

[0109] The constraints included in this invention are mainly divided into power flow constraints, transformer-related constraints, and network operation safety constraints.

[0110] Power flow constraints are represented as follows:

[0111] ;

[0112] in, Representing nodes respectively , Voltage amplitude at time t; , Representing branch roads Active power and reactive power at time t; Indicates a branch The line reactance.

[0113] Optimal economic operating range constraints for transformers:

[0114] ;

[0115] Transformer capacity constraints:

[0116] ;

[0117] Transformer tap change limit:

[0118] ;

[0119] In the formula , These represent the actual number of adjustments made to the distribution transformer and the maximum allowed number of adjustments, respectively.

[0120] Time constraints for transformer tap adjustment:

[0121] ;

[0122] In the formula , These represent the times of the two most recent transformer tap changes. This indicates the minimum value of the transformer's tap change time.

[0123] Network operation security constraints

[0124] ;

[0125] In the formula: For nodes voltage, , They are nodes The upper and lower limits of voltage; branch road The current, Then it is its maximum value; For nodes power, , These represent the upper and lower limits of the node power, respectively.

[0126] (3) NSGA-II algorithm based on subjective-objective coupling weighting method:

[0127] Regarding the weights mentioned in the above model, this invention employs a subjective-objective coupled weighting method for selection. The subjective weighting coefficients are set by the decision-maker, while the objective weighting method uses entropy weighting. Entropy weighting is a weighting method based on information entropy. It determines the degree of influence of each indicator on the evaluation system by calculating its entropy value. The lower the entropy value, the greater the information content of the indicator, and the higher its weight. The calculation process of entropy weighting is as follows:

[0128] Data standardization: The original data of each evaluation indicator are normalized to obtain a standardized matrix.

[0129] ;

[0130] In the formula: It is the first The sample at the th Standard values ​​for each indicator; It is the first The sample at the th Values ​​under each indicator; and They are the first The minimum and maximum values ​​of each indicator.

[0131] Calculate the weighting: Calculate the weight of each indicator in the standardized matrix. .

[0132] ;

[0133] In the formula: That is the number of samples.

[0134] Calculate information entropy: based on proportion Calculate the entropy value of each indicator. .

[0135] ;

[0136] In the formula: It is a constant.

[0137] Calculate entropy weights: Calculate weights based on information entropy. .

[0138] ;

[0139] In the formula: It refers to the number of indicators.

[0140] This invention uses a subjective-objective coupled weighting method to determine the weights of each objective function, and then uses the NSGA-II algorithm to find the optimal solution for the transformer tap position. The flowchart of the NSGA-II algorithm for tap position optimization in distribution transformers is as follows: Figure 3 As shown. To solve practical problems using the NSGA-II algorithm, encoding processing is required. Currently, transformer tap positions in distribution substations are mainly divided into five positions: 1 (10kV+5%), 2 (10kV+2.5%), 3 (10kV), 4 (10kV-2.5%), and 5 (10kV-5%). Since the transformer tap positions are discrete integers, integer encoding is used, resulting in 1, 2, 3, 4, and 5. Here, each transformer tap position can be considered an individual in the population. The n transformer tap position selection schemes are arranged sequentially as genes, forming a population, representing the corresponding tap position K = [ , ,… For example, [1, 2, 3, 4, 5] represent the operating gears of the transformer in the distribution substation as gear 1, gear 2, gear 3, gear 4, and gear 5, respectively.

[0141] Initial solutions are randomly generated in the population to traverse more possible tap configurations in the solution space. The performance of each individual (i.e., each tap configuration scheme) is measured by the objective function of the defined distribution transformer tap adjustment optimization model. The values ​​of each individual on the three objectives of voltage quality compliance rate, key equipment loss, and distribution transformer utilization rate are calculated and stored for subsequent non-dominated ranking and congestion calculation.

[0142] A non-dominated ordination is performed on all individuals in the population, resulting in two ranks (P1, P2), where individuals within each rank are non-dominated. The main steps of the non-dominated ordination are: the first rank (P1) contains all individuals that are not dominated by any other individual; the second rank (P2) contains individuals that are not dominated after removing P1, and so on. Within each rank, the crowding distance for each individual is calculated, such as... Figure 4 The diagram shows the local crowding distance. Individuals with lower crowding levels are typically located at the boundaries of the target space, which helps maintain population diversity.

[0143] A new population is generated based on non-dominated sorting and crowding. During selection, lower-ranking non-dominated individuals are prioritized, while individuals with higher crowding within the same rank are prioritized to maintain diversity. When generating the new population, crossover and mutation operations of a genetic algorithm are applied. Since the distribution transformer only has five taps, the mutated genes are randomly changed within the range of 1 to 5. Two individuals are selected from the parents, and a portion of their genes are exchanged to generate new individuals; a small-probability random change is made to the genes of some individuals to increase population diversity. New tap configuration schemes are generated through the above crossover and mutation operations.

[0144] The elite strategy aims to retain superior individuals and eliminate inferior ones. The strategy involves merging the current population with its offspring to form a new population. Then, the merged population is sorted using non-dominated ranking, and the top N individuals (ranked by non-dominance level and crowding) are selected to form the next generation. Finally, this process is repeated until a preset maximum number of generations is reached or the optimal solution for the population remains unchanged over several generations. After the termination condition is met, the algorithm obtains a Pareto front solution set, which contains all configuration schemes with optimal gear selection characteristics.

[0145] From the obtained Pareto front solution set, the most suitable gear configuration can be selected from these schemes according to the priority of the actual application and the actual needs. For example, if the voltage quality compliance rate is given priority, the solution that is optimal under this objective is selected; if multiple objectives are considered comprehensively, a solution that performs relatively evenly across the objectives can be selected.

[0146] (4) Case Analysis:

[0147] To further verify the effectiveness of this invention, a simplified diagram of a distribution transformer substation is shown below. Figure 5 As shown in Table 1, the data used was determined using a subjective-objective coupled weighting method. The final weight values ​​are as follows: W1=0.46, W2=0.34, W3=0.2. The operating gears of the distribution transformers for the four quarters are K0=[3,2,4,2]. The parameters of NSGA-II are set as follows: population size is 100, number of iterations is 100, crossover rate is 0.75, and mutation rate is 0.02.

[0148] To assess the effectiveness and feasibility of the proposed distribution transformer tap-level optimization adjustment strategy based on comprehensive analysis and evaluation, this invention utilizes data from a specific transformer substation in a certain region and set parameters for calculation and verification. Specifically, Figure 6 This demonstrates the change in the number of individuals corresponding to the Pareto optimal solution obtained by the proposed NSGA-II algorithm during the population evolution process. Figure 6As can be seen, since the maximum number of evolutions is set to 100, the number of Pareto optimal solutions increases with the number of generations, gradually converging to a single value. Around generation 50, the number of Pareto optimal solutions stabilizes at around 30; and even after 100 generations, the number of Pareto optimal solutions remains at 30, indicating that convergence has been achieved.

[0149] The optimal distribution transformer tap position obtained by the method proposed in this invention is K1=[2,1,3,2]. Table 3 shows a comparison of the average voltage on the user side before and after the tap change. The standard value of the voltage on the user side is 220V. The table shows that the deviation of the average voltage on the user side for the first three quarters decreased from the original 5.35%, 8.55%, and 4.15% to 0.2%, 0.57%, and 0.1%. This demonstrates that the distribution transformer tap position adjustment method based on comprehensive analysis and evaluation proposed in this paper can effectively solve the distribution transformer tap position adjustment problem.

[0150]

[0151] This invention provides a method for adjusting the tap position of a distribution transformer based on comprehensive analysis and evaluation, which has the following characteristics:

[0152] (1) The model of this invention uses a comprehensive analysis and evaluation method to establish a two-level indicator comprehensive evaluation system for distribution transformer adjustment. Through the analysis of various factors, the voltage quality qualification rate, the loss of key equipment in the distribution area and the utilization rate of distribution transformers are finally selected as the first-level indicators. The corresponding second-level indicators are further selected, and a multi-objective model is established by using the correlation relationship.

[0153] (2) The present invention uses a comprehensive analysis and evaluation method to establish an evaluation model for the optimal adjustment strategy of distribution transformer taps. It uses real-time operation data of distribution substations for analysis and optimization, effectively combines actual data with theoretical models, and selects the weights of three objective functions—voltage quality qualification rate, key equipment loss in distribution substations, and distribution transformer utilization rate—using the subjective-objective coupling weighting method. Then, it uses the NSGA-II algorithm to analyze and optimize the transformer taps, and finally calculates the most suitable optimization result for the actual situation, providing guidance for the design, tap adjustment, operation and maintenance of distribution transformers in the distribution network.

[0154] (3) This invention uses the subjective-objective coupled weighting method to optimize the weight coefficients of each objective function. It combines the advantages of subjective experience and objective data, and can effectively balance subjectivity and objectivity in the weighting process, thereby improving the scientificity and rationality of weight allocation.

[0155] (4) This invention optimizes the model to establish a distribution transformer tap optimization model based on comprehensive analysis and evaluation, and applies the NSGA-II algorithm based on the subjective-objective coupling weighting method to solve it. Tests in specific cases show that the non-dominated solutions obtained by using the NSGA-II algorithm are uniformly distributed in the target space and have good convergence and robustness.

Claims

1. A method for regulating the tap of a distribution transformer based on an integrated analysis evaluation, characterized by Includes the following steps: Step 1: Obtain the operating data of the distribution substation and related data of the distribution transformer; Step 2: Establish a comprehensive evaluation system of indicators; Step 3: Establish an optimization model for distribution transformer tap adjustment; Step 4: Optimize the tap position of the distribution transformer using the NSGA-II algorithm based on the subjective-objective coupling weighting method to obtain the optimal adjustment command; In step 3, the establishment of the distribution transformer tap adjustment optimization model is as follows: With the objective functions of maximizing voltage quality compliance rate, minimizing losses of key equipment in distribution substations, and maximizing transformer utilization, the following model is constructed: ； In the formula: The objective function for the optimization model of tap position adjustment of distribution transformers; , , These are the objective function values ​​for voltage quality qualification rate, key equipment loss in distribution substation area, and distribution transformer utilization rate, respectively. , , They are respectively , , Weighting coefficients; Voltage quality pass rate is measured using voltage deviation and voltage over-limit duration, as shown below: ； ； ； In the formula: , The objective functions are voltage deviation and voltage over-limit duration, respectively; T is the set of time periods. This refers to the set of all nodes in the distribution radio area; , They are nodes The voltage amplitude and the rated voltage amplitude at time t; , They are nodes The duration of voltage over-limit and the standard value of voltage over-limit at time t; The losses of key equipment in a distribution substation are measured by considering transformer losses and line losses in the distribution substation area, as shown below: ； ； ； In the formula: For the loss of key equipment in the distribution radio area; , These are the objective functions for transformer losses and network line losses in the distribution substation, respectively. , , These are the transformer's no-load loss, capacity, and comprehensive power rated load loss, respectively. , These represent the actual active power and reactive power output of the transformer, respectively. To collect all branch lines in the distribution area; branch at time t The current, This is a branch road. The resistance; The utilization rate of a distribution transformer is expressed as follows: ； In the formula: For transformer utilization rate.

2. The distribution transformer tap adjustment method based on comprehensive analysis and evaluation according to claim 1, characterized in that: In step 2, the comprehensive evaluation system indicators include three primary indicators: voltage quality pass rate, key equipment loss in the distribution substation, and distribution transformer utilization rate. Among them, voltage quality pass rate and key equipment loss in the distribution substation are further divided into two secondary indicators. Voltage quality pass rate includes voltage deviation and voltage over-limit time. Key equipment loss in the distribution substation includes transformer loss and line loss. Distribution transformer utilization rate is the average utilization rate of the distribution transformers in the distribution substation.

3. The distribution transformer tap adjustment method based on comprehensive analysis and evaluation according to claim 1, characterized in that: Constraints include power flow constraints, transformer-related constraints, and network operation safety constraints: 1) Power flow constraints are expressed as follows: ； in, Representing nodes respectively , Voltage amplitude at time t; , Representing branch roads Active power and reactive power at time t; Indicates a branch The line reactance; 2) Constraints on the optimal economic operating range of the transformer: ； 3) Transformer capacity constraints: ； in, This represents the actual capacity of the transformer. 4) Limitations on the number of transformer tap changes: ； In the formula, , These represent the actual number of adjustments made by the distribution transformer and the maximum allowed number of adjustments, respectively. 5) Time constraints for transformer tap changing: ； In the formula, , These represent the times of the two most recent transformer tap changes. This indicates the minimum value of the transformer tap-changing time; 6) Network operation security constraints: ； In the formula: For nodes The voltage; , They are nodes The upper and lower limits of the voltage; branch road The current, Then it is its maximum value; For nodes power, , These represent the upper and lower limits of the node power, respectively.

4. The distribution transformer tap adjustment method based on comprehensive analysis and evaluation according to claim 1, characterized in that: In step 4, the weights of each objective function are selected by the subjective-objective coupling weighting method, and the transformer tap position is optimized by the NSGA-II algorithm. The most satisfactory solution that is most conducive to the optimal regulation of the distribution transformer is selected from the final Pareto solution set and used as the optimal regulation command.

5. The distribution transformer tap adjustment method based on comprehensive analysis and evaluation according to claim 4, characterized in that: In step 4, the subjective and objective weights are coupled using the multiplier normalization method to avoid the drawbacks of a single method, ultimately obtaining a comprehensive weight: ； In the formula: This is the overall weighting coefficient; Subjective weighting coefficient; This is the objective weighting coefficient.

6. The distribution transformer tap adjustment method based on comprehensive analysis and evaluation according to claim 5, characterized in that: The selection process utilizes a subjective-objective coupled weighting method. The subjective weighting coefficients are set by the decision-maker, while the objective weighting method employs entropy weighting. Entropy weighting is a weighting method based on information entropy. It calculates the entropy value of each indicator to determine its influence on the evaluation system; the lower the entropy value, the greater the information content of the indicator, and the higher its weight. The calculation process of entropy weighting is shown below: Data standardization: The raw data of each evaluation indicator are normalized to obtain a standardized matrix; ； In the formula: It is the first The sample at the th Standard values ​​for each indicator; It is the first The sample at the th Values ​​under each indicator; and They are the first The minimum and maximum values ​​of each indicator; Calculate the weighting: Calculate the weight of each indicator in the standardized matrix. ; ； In the formula: It is the number of samples; Calculate information entropy: based on proportion Calculate the entropy value of each indicator. ; ； In the formula: It is a constant; Calculate entropy weights: Calculate weights based on information entropy. ; ； In the formula: It refers to the number of indicators.

Images