Power distribution network voltage sag evaluation method based on power supply parameter identification

Abstract

The invention provides a power distribution network voltage sag evaluation method based on power supply parameter identification, and aims to overcome the defects in the aspect of power distribution network voltage sag evaluation in the prior art. Constructing a power supply parameter identification model, deploying an intelligent electric meter and a sensor at key nodes of the power distribution network to collect voltage, current and power data, and selecting an equivalent circuit model to be combined with a least square method for solving to obtain a power supply parameter estimation value. Secondly, voltage sag characteristic analysis is carried out, key characteristics such as voltage sag amplitude and duration are extracted, and a statistical method is used for analyzing the relation between the characteristics and power supply parameters; and then constructing a voltage sag influence evaluation model, and adopting an FMEA method to comprehensively consider the fault occurrence probability, the influence severity and the detection difficulty to quantify the risk. And finally, making a treatment strategy by using a multi-objective optimization algorithm according to an evaluation result, and balancing cost and benefits. The method can effectively identify a voltage sag mode rule, reasonably configure and treat resources, and realize improvement of power supply reliability and electric energy quality of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network, and in particular to the monitoring and management of distribution network voltage quality, and specifically to a distribution network voltage sag assessment method based on power supply parameter identification. Background Art

[0002] In modern power systems, the voltage sag problem of distribution networks is becoming increasingly prominent, causing many adverse effects on industrial production, commercial operations and residents' lives. Traditional voltage sag assessment methods have many defects:

[0003] Limitations of data collection and analysis: Previous technologies were not comprehensive and accurate enough in data collection. They only relied on limited monitoring points to collect simple voltage and current data, lacked effective use of power data, and had a low collection frequency, making it difficult to capture the rapid changes in voltage sag details. For example, in some industrial areas, due to the complexity and diversity of electrical equipment, voltage sag characteristics are difficult to accurately obtain, resulting in a lack of a reliable data basis for subsequent analysis and governance. In terms of analysis methods, simple statistical descriptions are often used, which cannot deeply explore the intrinsic connection between voltage sag events and power supply parameters, and it is difficult to accurately identify their patterns and laws.

[0004] Inaccurate risk assessment: Existing risk assessment methods usually do not fully consider the differentiated sensitivity of different types of loads to voltage sags, as well as the actual detection capabilities of the monitoring system. The classification of fault modes is relatively rough, and there is no detailed division based on multiple factors such as amplitude and duration, so that the risk assessment results cannot accurately reflect the actual impact of voltage sags on the distribution network. This may lead to unreasonable resource allocation in governance decisions, high-risk areas cannot be governed in a timely and effective manner, and some low-risk areas are over-invested.

[0005] Lack of optimization of governance strategies: Traditional governance strategy formulation often focuses on a single goal, such as simply pursuing the reduction of voltage sag incidence or reducing governance costs, without comprehensively considering the balance between cost and benefit. When selecting governance measures, there is a lack of systematic optimization algorithms, making it difficult to select the most suitable strategy combination for the actual situation of the distribution network from many feasible options. For example, in the transformation of distribution networks in some old communities, due to the lack of scientific optimization methods, expensive but suboptimal governance equipment may be selected, resulting in a waste of resources and an inability to effectively solve the problem of voltage sag. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a distribution network voltage sag assessment method based on power supply parameter identification, aiming to achieve effective assessment and management of distribution network voltage sag and improve power supply reliability and power quality by constructing a power supply parameter identification model, accurately analyzing characteristics and risks, and formulating strategies using a multi-objective optimization algorithm.

[0007] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0008] The method for evaluating voltage sag in distribution network based on power supply parameter identification includes the following steps:

[0009] S1. Build a power parameter identification model, deploy smart meters and sensors, collect voltage, current and power data of key nodes in the distribution network in real time, use the collected real-time data to build a power parameter identification model, simulate and predict voltage sag events. The input feature of the model is the collected real-time data, and the output is the estimated value of the power parameter;

[0010] S2. Voltage sag feature analysis: Based on the power supply parameters identified in S1, the key features of the voltage sag event are extracted, and the extracted features are analyzed using statistical methods to identify the patterns and laws of the voltage sag event, and analyze the relationship between the voltage sag features and the power supply parameters;

[0011] S3. Voltage sag impact assessment model. Based on the features analyzed in S2 and the power supply parameters in S1, a voltage sag impact assessment model is constructed. The FMEA risk assessment method is used to quantify the risk of voltage sag and assess its impact on the distribution network.

[0012] S4. Optimization and governance strategy formulation: Based on the impact assessment results of S3, use multi-objective optimization algorithms to balance costs and benefits and formulate targeted governance strategies.

[0013] Furthermore, the S1 includes:

[0014] S11. Data collection preparation: determine the installation locations of smart meters and sensors at key nodes of the distribution network to ensure that the operating status of the distribution network can be fully and accurately reflected; calibrate and debug smart meters and sensors, and set the data collection frequency;

[0015] S12. Data collection and transmission: Smart meters and sensors collect voltage, current, active power and reactive power data of key nodes of the distribution network in real time according to the set frequency, and transmit the collected data to the data processing center stably and quickly through wired or wireless communication to ensure the integrity and timeliness of the data;

[0016] S13. Model construction and parameter estimation. The Thevenin equivalent circuit model is selected to represent the power supply as a voltage source and a series resistor. The equation group is established based on the circuit principle and the collected data. Based on Ohm's law and the power formula, the following equation group is constructed for n sampling points:

[0017] Active power equations:

[0018]

[0019] Reactive power equations:

[0020]

[0021] Written in matrix form:

[0022]

[0023]

[0024] Where P(i) represents the active power at the i-th sampling point; Q(i) represents the reactive power at the i-th sampling point; I(i) represents the current at the i-th sampling point; R s is the internal resistance of the power supply; θ represents the phase difference between current and voltage;

[0025] Solve the above overdetermined equations using the least squares method to obtain the power supply parameters electromotive force E and power supply internal resistance R s The estimated value of is:

[0026]

[0027] Furthermore, S2 includes:

[0028] Feature extraction includes voltage sag amplitude extraction and voltage sag duration extraction, wherein the voltage sag amplitude extraction method is:

[0029] The collected voltage data is searched and the minimum value during the voltage sag is found. To avoid interference from local minimum values, the moving average method is used to smooth the data. The window size is set to k (e.g., k=10), and the average value of the voltage data in each window is calculated as a new voltage sequence. Then, the minimum value is found in the voltage sequence as the minimum value during the voltage sag.

[0030] The voltage sag amplitude is calculated based on the minimum value during the voltage sag period, which is calculated as:

[0031]

[0032] Among them, V sag is the voltage sag amplitude; U min is the minimum value during voltage sag; U nom is the rated voltage of the distribution network.

[0033] Furthermore, the voltage sag duration extraction method is:

[0034] Set voltage threshold U th (such as 0.9U nom), by performing differential operation on the voltage data, when the voltage differential changes from positive to negative and the voltage value is lower than , record the time point at this time as the start time of the voltage sag; when the voltage differential changes from negative to positive and the voltage value is higher than , record the time point as the end time of the voltage sag, and calculate the duration according to the end time of the voltage sag and the start time of the voltage sag:

[0035] T sag = t2-t1;

[0036] Among them, T sag is the duration of voltage sag; t2 is the end of voltage sag; t1 is the start time of voltage sag.

[0037] Furthermore, in S2, a method for analyzing the extracted features using a statistical method is as follows:

[0038] S21. Probability distribution calculation: divide the voltage sag amplitude range into m intervals, count the number of voltage sag events falling in each interval, and calculate the probability of each interval. in, Indicates the total number of voltage sag events, n i Indicates the number of times the voltage sag event falls within the i-th interval; draws a probability distribution histogram or curve of the voltage sag amplitude;

[0039] S22. Correlation analysis: Pearson correlation coefficient is used to analyze the correlation between voltage sag characteristics and power supply parameters;

[0040] Voltage sag amplitude V sag The Pearson correlation coefficient with the power supply electromotive force E is:

[0041]

[0042] Among them, V sag (i) is the i-th voltage sag amplitude sample; E(i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the internal resistance of the power supply; is the average value of the power supply electromotive force;

[0043] Voltage sag amplitude V sag With the power supply internal resistance R s The calculation formula of Pearson correlation coefficient is:

[0044]

[0045] Among them, V sag (i) is the i-th voltage sag amplitude sample; R s (i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the power supply internal resistance; m is the number of samples;

[0046] Through correlation analysis, the influence of power supply parameters on voltage sag characteristics is determined.

[0047] Strong positive correlation, when r VE When they are close, it indicates that there is a strong positive correlation, which means that the power supply electromotive force and the voltage sag amplitude have a strong positive correlation, that is, the increase of the power supply electromotive force will lead to a significant increase of the voltage sag amplitude;

[0048] Strong negative correlation, when When it is close to -1, it indicates that there is a strong negative correlation, which means that the power supply internal resistance has a strong negative correlation with the voltage sag amplitude, that is, an increase in the power supply internal resistance will lead to a significant decrease in the voltage sag amplitude;

[0049] By comprehensively evaluating the degree of influence and calculating and analyzing the above correlation coefficients, the influence of the power supply parameters on the voltage sag characteristics can be determined; if the absolute value of the correlation coefficient is large, it means that the power supply parameters have a significant influence on the corresponding voltage sag characteristics; if the absolute value of the correlation coefficient is small, it means that the influence is small.

[0050] The S3 includes:

[0051] S31. Failure mode analysis and parameter determination:

[0052] S311. Count the frequency of voltage sag events based on historical data, classify voltage sag events according to factors such as amplitude and duration, such as mild and other types, and count the probability of occurrence of each type of event;

[0053] S312. Severity of fault impact. A scoring standard for the severity of fault impact is established for different types of loads. For example, for industrial motors, a voltage drop may cause the motor to stall, overheat, or even be damaged. The score is set based on factors such as motor power and importance.

[0054] S313. Detection difficulty: evaluate the monitoring system's ability to detect voltage sag events. Consider factors such as monitoring equipment accuracy, response time, and data transmission reliability.

[0055] S32. Calculation and evaluation of risk priority numbers,

[0056] S321. Calculate the risk priority number. According to the FMEA method, the risk priority number is:

[0057] RPN = P × S × D;

[0058] Among them, P is the probability of occurrence, S is the severity of the fault impact, and D is the difficulty of detection; for each type of voltage sag fault mode, calculate its RPN value respectively;

[0059] S322. Analyze the evaluation results and sort the RPN values ​​of all failure modes. The larger the value, the higher the risk. Draw a risk matrix diagram with the probability of occurrence as the horizontal axis and the severity of the failure impact as the vertical axis. Mark the different failure modes in the diagram to intuitively display the risk distribution. For failure modes in high-risk areas, take control measures as a priority.

[0060] The S4 includes:

[0061] S41. Objective function and constraint setting:

[0062] Objective function:

[0063] Governance costs include equipment purchase costs, installation costs, operation and maintenance costs, etc.

[0064] The benefit is measured by reducing the economic losses caused by voltage sag events. For industrial users, the production interruption losses and equipment damage repair costs caused by voltage sag are determined through statistical analysis or consultation with users. The benefit is shown as:

[0065]

[0066] L prod (i) is the production interruption loss caused by voltage sag; L rep (i) is the equipment damage repair cost; i is the user or equipment affected by the voltage sag, and a multi-objective optimization function is established:

[0067]

[0068] O1 is to minimize the governance cost C; O2 is to maximize the benefit B;

[0069] Constraints:

[0070] Equipment capacity constraints: the capacity of the installed voltage regulation equipment (such as DVR, static VAR compensator SVC, etc.) should meet the needs of the distribution network, that is, Q eq ≥Q max , where Q eq is the equipment capacity; Q max is the maximum reactive power demand of the distribution network;

[0071] Budget constraint: The sum of the costs of various governance measures cannot exceed the budget, that is, Among them, j is different governance measures; C j is the cost of each governance measure; B total For budget;

[0072] Technical feasibility constraints: Some governance technologies may have an impact on the harmonics, power factor and other indicators of the power grid, and need to meet relevant power standards, that is, THD ≤ THD lim , THD is the harmonic distortion rate of the power grid after the implementation of the adopted control measures, and THD lim To limit the harmonic distortion rate of the network;

[0073] S41.Optimization algorithm selection and solution;

[0074] The genetic algorithm is used, and its basic principle is to simulate the inheritance, mutation and selection operations in the biological evolution process; first, the governance strategy is encoded, and binary coding is used to indicate whether a certain governance device or measure is selected;

[0075] Initialize a population, where each individual represents a possible combination of governance strategies; calculate the objective function value and fitness value of each individual. The fitness value is defined based on the objective function value, and the linear weighted method is used to convert the cost and benefit into the fitness value to guide the population to evolve towards the optimal solution;

[0076] Solution process: During the iterative process of the genetic algorithm, individuals with high fitness are selected through selection operations to enter the next generation of the population, and crossover and mutation operations are performed on the selected individuals to generate new individuals to increase the diversity of the population; this process is repeated until the stopping criteria are met, such as reaching the maximum number of iterations or the optimal solution of the population converges.

[0077] After solving the problem through the optimization algorithm, a set of Pareto optimal solutions is obtained, that is, a set of governance strategy combinations that achieve a good balance between cost and benefit; from the Pareto optimal solution set, the final governance strategy is selected according to the actual situation; if the budget is relatively tight, a strategy with lower cost but relatively reasonable benefit is selected; if the power supply reliability requirements are extremely high, a strategy with higher benefit but relatively higher cost is selected.

[0078] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0079] Accurate power supply parameter identification and feature analysis: By deploying smart meters and sensors at key nodes of the distribution network, comprehensive and high-frequency collection of voltage, current and power data is achieved, and these data are used to build a power supply parameter identification model, which can accurately estimate power supply parameters. On this basis, the key features of voltage sag are extracted, and statistical methods are used for in-depth analysis to successfully identify the patterns and laws of voltage sag events, as well as their close relationship with power supply parameters, providing solid data support and theoretical basis for subsequent accurate evaluation and effective governance.

[0080] Accurate risk assessment and reasonable resource allocation: Based on detailed voltage sag characteristics and accurate power supply parameters, the FMEA risk assessment method is used, combined with careful consideration of different load types and evaluation of the detection capabilities of the monitoring system, to accurately quantify the voltage sag risk. Through the fine classification of failure modes and accurate calculation of risk priority numbers, the risk distribution of different areas and equipment in the distribution network can be clearly displayed, so that governance resources can be accurately invested in high-risk areas, achieve reasonable resource allocation, and improve the overall power supply reliability of the distribution network.

[0081] Optimized governance strategy and cost-benefit balance: Utilize multi-objective optimization algorithm, comprehensively consider governance cost and the benefits of reducing voltage sag, and formulate targeted governance strategy. Under the constraints of equipment capacity, budget and technical feasibility, the most appropriate solution is selected from the Pareto optimal solution set to achieve a good balance between cost and benefit. Whether in industrial distribution network or civil distribution network, the most economical and effective governance measures can be selected according to actual conditions, effectively reducing the impact of voltage sag on power users, while avoiding over-investment, and improving the operation economy and power quality of distribution network.

[0082] In summary, the present invention provides a comprehensive, scientific and effective evaluation and management solution for the voltage sag problem in the distribution network, which is of great significance for ensuring the stable operation of the power system and the normal power consumption of power users. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a logic block diagram of a distribution network voltage sag assessment method based on power supply parameter identification;

[0084] Figure 2 The logic block diagram of the power supply parameter identification model construction method;

[0085] Figure 3 A logical block diagram of the method for analyzing the extracted features using statistical methods;

[0086] Figure 4 A method for establishing a voltage sag impact assessment model and a logic block diagram of the assessment method;

[0087] Figure 5 Logical block diagram of the approach to developing optimization and governance strategies. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0089] like Figure 1 As shown, the distribution network voltage sag assessment method based on power supply parameter identification includes the following steps:

[0090] S1. Build a power parameter identification model, deploy smart meters and sensors, collect voltage, current and power data of key nodes in the distribution network in real time, use the collected real-time data to build a power parameter identification model, simulate and predict voltage sag events. The input feature of the model is the collected real-time data, and the output is the estimated value of the power parameter;

[0091] S2. Voltage sag feature analysis: Based on the power supply parameters identified in S1, the key features of the voltage sag event are extracted, and the extracted features are analyzed using statistical methods to identify the patterns and laws of the voltage sag event, and analyze the relationship between the voltage sag features and the power supply parameters;

[0092] S3. Voltage sag impact assessment model. Based on the features analyzed in S2 and the power supply parameters in S1, a voltage sag impact assessment model is constructed. The FMEA risk assessment method is used to quantify the risk of voltage sag and assess its impact on the distribution network.

[0093] S4. Optimization and governance strategy formulation: Based on the impact assessment results of S3, use multi-objective optimization algorithms to balance costs and benefits and formulate targeted governance strategies.

[0094] like Figure 2 As shown, the S1 includes:

[0095] S11. Data collection preparation: determine the installation locations of smart meters and sensors at key nodes of the distribution network (such as substation busbars, important load access points, etc.) to ensure that they can fully and accurately reflect the operating status of the distribution network; calibrate and debug smart meters and sensors, and set a suitable data collection frequency, such as collecting voltage, current and power data once every millisecond to meet the monitoring needs of fast-changing phenomena such as voltage sags;

[0096] S12. Data collection and transmission: Smart meters and sensors collect voltage, current, active power and reactive power data of key nodes of the distribution network in real time according to the set frequency, and transmit the collected data to the data processing center stably and quickly through wired or wireless communication (such as optical fiber communication, ZigBee, etc.) to ensure the integrity and timeliness of the data;

[0097] S13. Model construction and parameter estimation. The Thevenin equivalent circuit model is selected to represent the power supply as a voltage source and a series resistor. The equation group is established based on the circuit principle and the collected data. Based on Ohm's law and the power formula, the following equation group is constructed for n sampling points:

[0098] Active power equations:

[0099]

[0100] Reactive power equations:

[0101]

[0102] Written in matrix form:

[0103]

[0104]

[0105] Where P(i) represents the active power at the i-th sampling point; Q(i) represents the reactive power at the i-th sampling point; I(i) represents the current at the i-th sampling point; R s is the internal resistance of the power supply; θ represents the phase difference between current and voltage;

[0106] Solve the above overdetermined equations using the least squares method to obtain the power supply parameters electromotive force E and power supply internal resistance R s The estimated value of is:

[0107]

[0108] In S2:

[0109] Feature extraction includes voltage sag amplitude extraction and voltage sag duration extraction, wherein the voltage sag amplitude extraction method is:

[0110] The collected voltage data is searched and the minimum value during the voltage sag is found. To avoid interference from local minimum values, the moving average method is used to smooth the data. The window size is set to k (e.g., k=10), and the average value of the voltage data in each window is calculated as a new voltage sequence. Then, the minimum value is found in the voltage sequence as the minimum value during the voltage sag.

[0111] The voltage sag amplitude is calculated based on the minimum value during the voltage sag period, which is calculated as:

[0112]

[0113] Among them, V sag is the voltage sag amplitude; U min is the minimum value during voltage sag; U nom is the rated voltage of the distribution network;

[0114] The voltage sag duration extraction method:

[0115] Set voltage threshold U th (such as 0.9Unom ), by performing differential operation on the voltage data, when the voltage differential changes from positive to negative and the voltage value is lower than , record the time point at this time as the start time of the voltage sag; when the voltage differential changes from negative to positive and the voltage value is higher than , record the time point as the end time of the voltage sag, and calculate the duration according to the end time of the voltage sag and the start time of the voltage sag:

[0116] T sag = t2-t1;

[0117] Among them, T sag is the duration of voltage sag; t2 is the end of voltage sag; t1 is the start time of voltage sag.

[0118] like Figure 3 As shown, in S2, the method of using statistical methods to analyze the extracted features is:

[0119] S21. Probability distribution calculation: divide the voltage sag amplitude range into m intervals, count the number of voltage sag events falling in each interval, and calculate the probability of each interval. in, Indicates the total number of voltage sag events, n i Indicates the number of times the voltage sag event falls within the i-th interval; draws a probability distribution histogram or curve of the voltage sag amplitude;

[0120] S22. Correlation analysis, using Pearson correlation coefficient to analyze voltage sag characteristics (voltage sag duration T sag and voltage sag amplitude V sag ) and power supply parameters (electromotive force E and power supply internal resistance R s ) between them;

[0121] Voltage sag amplitude V sag The Pearson correlation coefficient with the power supply electromotive force E is:

[0122]

[0123] Among them, V sag (i) is the i-th voltage sag amplitude sample; E(i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the internal resistance of the power supply; is the average value of the power supply electromotive force;

[0124] Voltage sag amplitude V sag With the power supply internal resistance R s The calculation formula of Pearson correlation coefficient is:

[0125]

[0126] Among them, V sag (i) is the i-th voltage sag amplitude sample; R s (i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the power supply internal resistance; m is the number of samples;

[0127] Through correlation analysis, the influence of power supply parameters on voltage sag characteristics is determined.

[0128] Strong positive correlation, when r VE When they are close, it indicates that there is a strong positive correlation, which means that the power supply electromotive force and the voltage sag amplitude have a strong positive correlation, that is, the increase of the power supply electromotive force will lead to a significant increase of the voltage sag amplitude;

[0129] Strong negative correlation, when When it is close to -1, it indicates that there is a strong negative correlation, which means that the power supply internal resistance has a strong negative correlation with the voltage sag amplitude, that is, an increase in the power supply internal resistance will lead to a significant decrease in the voltage sag amplitude;

[0130] By comprehensively evaluating the degree of influence and calculating and analyzing the above correlation coefficients, the influence of the power supply parameters on the voltage sag characteristics can be determined; if the absolute value of the correlation coefficient is large, it means that the power supply parameters have a significant influence on the corresponding voltage sag characteristics; if the absolute value of the correlation coefficient is small, it means that the influence is small.

[0131] like Figure 4 As shown, the S3 includes:

[0132] S31. Failure mode analysis and parameter determination:

[0133] S311. Based on the historical data, the frequency of voltage sag events is counted and the voltage sag events are classified according to the amplitude, duration and other factors, such as mild (0.8U nom <V sag <0.9U nom And T sag <100ms), moderate (0.7U nom <V sag <0.8U nom or 100ms≤T sag <500ms), severe (V sag <0.7U nom or T sag ≥500ms) and other types, and count the occurrence probability of each type of event respectively;

[0134] S312. Severity of fault impact. For different types of loads (such as industrial motors, electronic equipment, lighting equipment, etc.), a fault impact severity scoring standard is established. For example, for industrial motors, voltage sag may cause motor stalling, overheating, or even damage. The score is set based on factors such as motor power and importance. If the motor power is above 100KW and is a key production equipment, the score is 8-10 points in the case of severe voltage sag; for electronic equipment such as computer servers, voltage sag may cause data loss and system crash. The score is based on the importance of data and equipment sensitivity. The score is 4-6 points in the case of moderate voltage sag, etc., using a 1-10 point system;

[0135] S313. Detection difficulty, evaluate the monitoring system's ability to detect voltage sag events. Consider factors such as monitoring equipment accuracy, response time, and data transmission reliability. If the monitoring equipment can accurately detect and alarm within 10ms after the voltage sag occurs, with an accuracy of ±1%, and no data transmission loss, the detection difficulty score is 1-3 points; if the detection delay exceeds 100ms, the accuracy is poor, and data transmission is sometimes lost, the detection difficulty score is 7-9 points, also using a 1-10 point system;

[0136] S32. Calculation and evaluation of risk priority numbers,

[0137] S321. Calculate the risk priority number. According to the FMEA method, the risk priority number is:

[0138] RPN = P × S × D;

[0139] Among them, P is the probability of occurrence, S is the severity of the fault impact, and D is the difficulty of detection; for each type of voltage sag fault mode, calculate its RPN value respectively;

[0140] S322. Analyze the evaluation results and sort the RPN values ​​of all failure modes. The larger the value, the higher the risk. Draw a risk matrix diagram with the probability of occurrence as the horizontal axis and the severity of the failure impact as the vertical axis. Mark the different failure modes in the diagram to intuitively display the risk distribution. For failure modes in high-risk areas, take control measures as a priority.

[0141] like Figure 5 As shown, the S4 includes:

[0142] S41. Objective function and constraint setting:

[0143] Objective function:

[0144] Governance costs include equipment purchase costs, installation costs, operation and maintenance costs, etc.

[0145] The benefit is measured by reducing the economic losses caused by voltage sag events. For industrial users, the production interruption losses and equipment damage repair costs caused by voltage sag are determined through statistical analysis or consultation with users. The benefit is shown as:

[0146]

[0147] L prod (i) is the production interruption loss caused by voltage sag; L rep (i) is the equipment damage repair cost; i is the user or equipment affected by the voltage sag, and a multi-objective optimization function is established:

[0148]

[0149] O1 is to minimize the governance cost C; O2 is to maximize the benefit B;

[0150] Constraints:

[0151] Equipment capacity constraints: the capacity of the installed voltage regulation equipment (such as DVR, static VAR compensator SVC, etc.) should meet the needs of the distribution network, that is, Q eq ≥Q max , where Q eq is the equipment capacity; Q max is the maximum reactive power demand of the distribution network;

[0152] Budget constraint: The sum of the costs of various governance measures cannot exceed the budget, that is, Among them, j is different governance measures; C j is the cost of each governance measure; B total For budget;

[0153] Technical feasibility constraints: Some governance technologies may have an impact on the harmonics, power factor and other indicators of the power grid, and need to meet relevant power standards, that is, THD ≤ THD lim , THD is the harmonic distortion rate of the power grid after the implementation of the adopted control measures, and THD lim To limit the harmonic distortion rate of the network;

[0154] S42. Optimization algorithm selection and solution;

[0155] The genetic algorithm is used, and its basic principle is to simulate the inheritance, mutation and selection operations in the biological evolution process; first, the governance strategy is encoded, and binary coding is used to indicate whether a certain governance device or measure is selected;

[0156] Initialize a population, where each individual represents a possible combination of governance strategies; calculate the objective function value (i.e., cost and benefit) and fitness value of each individual. The fitness value is defined based on the objective function value, and a linear weighted method is used to convert the cost and benefit into the fitness value to guide the population to evolve toward the optimal solution;

[0157] Solution process: During the iterative process of the genetic algorithm, individuals with high fitness are selected through selection operations to enter the next generation of the population, and crossover and mutation operations are performed on the selected individuals to generate new individuals to increase the diversity of the population; this process is repeated until the stopping criteria are met, such as reaching the maximum number of iterations or the optimal solution of the population converges.

[0158] After solving the problem using the optimization algorithm, a set of Pareto optimal solutions is obtained, that is, a set of governance strategy combinations that achieve a good balance between cost and benefit. From the Pareto optimal solution set, the final governance strategy is selected based on actual conditions (such as decision maker preferences, budget constraints, etc.). If the budget is relatively tight, a strategy with lower cost but relatively reasonable benefit is selected. If the power supply reliability requirements are extremely high, a strategy with higher benefit but relatively higher cost is selected.

[0159] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A distribution network voltage sag assessment method based on power supply parameter identification includes the following steps: S1. Build a power parameter identification model, deploy smart meters and sensors, collect voltage, current and power data of key nodes in the distribution network in real time, use the collected real-time data to build a power parameter identification model, simulate and predict voltage sag events. The input feature of the model is the collected real-time data, and the output is the estimated value of the power parameter; S2. Voltage sag feature analysis: Based on the power supply parameters identified in S1, the key features of the voltage sag event are extracted, and the extracted features are analyzed using statistical methods to identify the patterns and laws of the voltage sag event, and analyze the relationship between the voltage sag features and the power supply parameters; S3. Voltage sag impact assessment model. Based on the features analyzed in S2 and the power supply parameters in S1, a voltage sag impact assessment model is constructed. The FMEA risk assessment method is used to quantify the risk of voltage sag and assess its impact on the distribution network. S4. Optimization and governance strategy formulation: Based on the impact assessment results of S3, use multi-objective optimization algorithms to balance costs and benefits and formulate targeted governance strategies.

2. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: The S1 includes: S11. Data collection preparation: determine the installation locations of smart meters and sensors at key nodes of the distribution network to ensure that they can fully and accurately reflect the operating status of the distribution network; calibrate and debug smart meters and sensors, and collect voltage, current and power data every millisecond to meet the monitoring needs of rapid changes in voltage sags; S12. Data collection and transmission. Smart meters and sensors collect voltage, current, active power and reactive power data of key nodes of the distribution network in real time according to the set frequency, and transmit the collected data to the data processing center stably and quickly through wired or wireless communication to ensure the integrity and timeliness of the data; S13. Model construction and parameter estimation. The Thevenin equivalent circuit model is selected to represent the power supply as a voltage source and a series resistor. The equation group is established based on the circuit principle and the collected data. Based on Ohm's law and the power formula, the following equation group is constructed for n sampling points: Active power equations: Reactive power equations: Written in matrix form: Where P(i) represents the active power at the i-th sampling point; Q(i) represents the reactive power at the i-th sampling point; I(i) represents the current at the i-th sampling point; R s is the internal resistance of the power supply; θ represents the phase difference between current and voltage; Solve the above overdetermined equations using the least squares method to obtain the power supply parameters electromotive force E and power supply internal resistance R s The estimated value of is:

3. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: The S2 includes: S21. Feature extraction, the feature extraction includes voltage sag amplitude extraction and voltage sag duration extraction, wherein the voltage sag amplitude extraction method is: The collected voltage data is searched and the minimum value during the voltage sag is found. To avoid interference from local minimum values, the moving average method is used to smooth the data. The window size is set to k, and the average value of the voltage data in each window is calculated as a new voltage sequence. Then the minimum value is found in the voltage sequence as the minimum value during the voltage sag. The voltage sag amplitude is calculated based on the minimum value during the voltage sag period, which is calculated as: Among them, V sag is the voltage sag amplitude; U min is the minimum value during voltage sag; U nom is the rated voltage of the distribution network.

4. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: The voltage sag duration extraction method: Set voltage threshold U th , by performing differential operation on the voltage data, when the voltage differential changes from positive to negative and the voltage value is lower than , record the time point at this time as the start time of the voltage sag; when the voltage differential changes from negative to positive and the voltage value is higher than , record the time point as the end time of the voltage sag, and calculate the duration based on the end time of the voltage sag and the start time of the voltage sag: T sag =t2-t1; Among them, T sag is the duration of voltage sag; t2 is the end of voltage sag; t1 is the start time of voltage sag.

5. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: In S2, a method for analyzing the extracted features using statistical methods: S21. Probability distribution calculation: divide the voltage sag amplitude range into m intervals, count the number of voltage sag events falling in each interval, and calculate the probability of each interval. in, Indicates the total number of voltage sag events, n i Indicates the number of times the voltage sag event falls within the i-th interval; draws a probability distribution histogram or curve of the voltage sag amplitude; S22. Correlation analysis: Pearson correlation coefficient is used to analyze the correlation between voltage sag characteristics and power supply parameters; Voltage sag amplitude V sag The Pearson correlation coefficient with the power supply electromotive force E is: Among them, V sag (i) is the i-th voltage sag amplitude sample; E(i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the internal resistance of the power supply; is the average value of the power supply electromotive force; Voltage sag amplitude V sag With the power supply internal resistance R s The calculation formula of Pearson correlation coefficient is: Among them, V sag (i) is the i-th voltage sag amplitude sample; R s (i) is the i-th power supply internal resistance sample; It is the average value of voltage sag amplitude; is the average value of the power supply internal resistance; m is the number of samples; Through correlation analysis, the influence of power supply parameters on voltage sag characteristics is determined. Strong positive correlation, when r VE When they are close, it indicates that there is a strong positive correlation, which means that the power supply electromotive force and the voltage sag amplitude have a strong positive correlation, that is, the increase of the power supply electromotive force will lead to a significant increase of the voltage sag amplitude; Strong negative correlation, when When it is close to -1, it indicates that there is a strong negative correlation, which means that the power supply internal resistance has a strong negative correlation with the voltage sag amplitude, that is, an increase in the power supply internal resistance will lead to a significant decrease in the voltage sag amplitude; By comprehensively evaluating the degree of influence and calculating and analyzing the above correlation coefficients, the influence of the power supply parameters on the voltage sag characteristics can be determined; if the absolute value of the correlation coefficient is large, it means that the power supply parameters have a significant influence on the corresponding voltage sag characteristics; if the absolute value of the correlation coefficient is small, it means that the influence is small.

6. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: The S3 includes: S31. Failure mode analysis and parameter determination: S311. Count the frequency of voltage sag events based on historical data, classify voltage sag events into mild, moderate and severe types according to amplitude and duration factors, and count the probability of occurrence of each type of event; S312. Fault impact severity. Establish a fault impact severity scoring standard for different types of loads. For example, for industrial motors, voltage sag may cause motor stalling, overheating, or even damage. Set the score based on motor power and importance factors. S313. Detection difficulty: evaluate the monitoring system's ability to detect voltage sag events. Consider factors such as monitoring equipment accuracy, response time, and data transmission reliability; S32. Calculation and evaluation of risk priority numbers, S321. Calculate the risk priority number. According to the FMEA method, the risk priority number is: RPN = P × S × D; Among them, P is the probability of occurrence, S is the severity of the fault impact, and D is the difficulty of detection; for each type of voltage sag fault mode, calculate its RPN value respectively; S322. Analyze the evaluation results and sort the RPN values ​​of all failure modes. The larger the value, the higher the risk. Draw a risk matrix diagram with the probability of occurrence as the horizontal axis and the severity of the failure impact as the vertical axis. Mark the different failure modes in the diagram to intuitively display the risk distribution. For failure modes in high-risk areas, take control measures as a priority.

7. The method for evaluating voltage sag in distribution network based on power supply parameter identification according to claim 1, characterized in that: The S4 includes: S41. Objective function and constraint setting: Objective function: The governance cost includes equipment purchase cost, installation cost, operation and maintenance cost. The benefit is measured by reducing the economic losses caused by voltage sag events. For industrial users, the production interruption losses and equipment damage repair costs caused by voltage sag are determined through statistical analysis or consultation with users. The benefits are shown as: L prod (i) is the production interruption loss caused by voltage sag; L rep (i) is the equipment damage repair cost; i is the user or equipment affected by the voltage sag, and a multi-objective optimization function is established: O1 is to minimize the governance cost C; O2 is to maximize the benefit B; Constraints: Equipment capacity constraint, the capacity of the installed voltage regulation equipment should meet the needs of the distribution network, that is, Q eq ≥Q max , where Q eq is the equipment capacity; Q max is the maximum reactive power demand of the distribution network; Budget constraint: The sum of the costs of various governance measures cannot exceed the budget, that is, Among them, j is different governance measures; C j is the cost of each governance measure; B total For budget; Technical feasibility constraints: Some governance technologies may have an impact on the harmonics and power factor indicators of the power grid, and need to meet relevant power standards, that is, THD ≤ THD lim , THD is the harmonic distortion rate of the power grid after the implementation of the adopted control measures, and THD lim To limit the harmonic distortion rate of the network; S42. Optimization algorithm selection and solution; The genetic algorithm is used, and its basic principle is to simulate the inheritance, mutation and selection operations in the biological evolution process; first, the governance strategy is encoded, and binary coding is used to indicate whether a certain governance device or measure is selected; Initialize a population, where each individual represents a possible combination of governance strategies; calculate the objective function value and fitness value of each individual. The fitness value is defined based on the objective function value, and the linear weighted method is used to convert the cost and benefit into the fitness value to guide the population to evolve towards the optimal solution; Solution process: During the iterative process of the genetic algorithm, individuals with high fitness are selected through selection operations to enter the next generation of the population, and crossover and mutation operations are performed on the selected individuals to generate new individuals to increase the diversity of the population; this process is repeated until the stopping criteria are met, such as reaching the maximum number of iterations or the optimal solution of the population converges. After solving the problem through the optimization algorithm, a set of Pareto optimal solutions is obtained, that is, a set of governance strategy combinations that achieve a good balance between cost and benefit; from the Pareto optimal solution set, the final governance strategy is selected according to the actual situation; if the budget is relatively tight, a strategy with lower cost but relatively reasonable benefit is selected; if the power supply reliability requirements are extremely high, a strategy with higher benefit but relatively higher cost is selected.

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