Uncertain factor quantitative analysis method for active access of grid-connected main body to power distribution system

Abstract

The invention discloses an uncertain factor quantitative analysis method for active access of a grid-connected main body to a power distribution system. A sensor network is deployed to collect data in real time and preliminarily analyze the data, and the state of a power distribution system is monitored. The device defects are then detected using a multi-physical imaging technique based on the real-time operational data. Relevant data are integrated, risk assessment including data cleaning, time synchronization, space integration and other operations is carried out in combination with user-side power use data, and a three-dimensional visualization model is further established to assist assessment. Then, data are integrated, a risk matrix tool is used for sorting and priority division, a state space model is established for simulation, and the influence of uncertain factors on stability is analyzed; and finally, analyzing and evaluating factor influence degrees by using global sensitivity, updating a risk matrix and formulating a risk management strategy so as to provide effective support for ensuring stable operation of the power distribution system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a quantitative analysis method for analyzing uncertain factors faced by a grid-connected entity when it actively connects to a power distribution system. Background Art

[0002] With the rapid development of energy transformation and distributed energy resources (DER), more and more grid-connected entities (such as distributed generation, energy storage systems, etc.) are trying to actively connect to the distribution system. However, this process faces many challenges, among which uncertainty is one of the key issues. These uncertainties include but are not limited to the intermittent nature of renewable energy generation (such as solar energy and wind energy), the volatility of load demand, the randomness of equipment failures, and the uncertainty of market prices. They have a significant impact on the planning, operation, and control of the distribution system, such as voltage fluctuations, power imbalance, and reduced reliability. Therefore, accurate quantitative analysis of these uncertainties is crucial to ensure the stable and efficient operation of the distribution system. Summary of the invention

[0003] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a comprehensive, systematic and accurate method for quantitatively analyzing the uncertain factors therein, thereby providing strong support for the stable operation of the power distribution system.

[0004] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0005] The quantitative analysis method of uncertain factors of the grid-connected entity actively connected to the distribution system includes the following steps:

[0006] S1. Real-time data collection and preliminary analysis, deployment of sensor networks, real-time data transmission to the central monitoring system through the communication network, to achieve real-time monitoring of the power distribution system status;

[0007] S2. Equipment detection and defect identification: Based on the real-time operation data collected in S1, multi-physics imaging technology is used to detect distribution network equipment and identify physical defects and potential safety hazards of the equipment;

[0008] S3. Data integration and risk assessment: integrating the data of S1 and S2 and combining it with the user-side power usage data for risk assessment;

[0009] S4. Comprehensive data analysis and risk rating, combining the data from S1 to S3, and using the risk matrix tool to sort and prioritize risks;

[0010] S5. Establishment and simulation of state space model: Based on the data and analysis results of S1-S4, using time series analysis and state space model, combined with historical data and current monitoring data, establish a mathematical description of the system and quantitatively analyze the specific impact of uncertainty factors on the stability of the distribution system;

[0011] S6. Global sensitivity analysis and risk management formulation, based on the state space model of S5, use the global sensitivity analysis method to evaluate the relative impact of uncertainty factors.

[0012] Furthermore, in S2, the step of using multi-physics imaging technology to identify physical defects and potential safety hazards of distribution network equipment includes:

[0013] S21. Based on the data analysis results of S1, determine the power grid equipment that needs to be tested by multi-physics imaging, use an infrared thermal imager to conduct a comprehensive scan of the equipment, analyze the thermal image based on the scanning results, and identify abnormal hot spots or cold spots;

[0014] S22. Perform ultrasonic scanning on the components with abnormal hot spots or cold spots identified in S21, monitor the reflected signals, identify internal defects, and identify equipment defects by combining the imaging results with known failure modes;

[0015] Furthermore, in S3, the specific method of performing risk assessment in combination with the user-side power usage data includes:

[0016] S31. Data cleaning and formatting: Integrate the real-time operation data collected by S1 and the physical defect data identified in S2, clean the integrated data, remove invalid or redundant information, and convert the data into a unified format through data cleaning and formatting to facilitate integration and analysis;

[0017] S32. Time synchronization: synchronize data from different data sources according to timestamps to ensure that the data is under the same time base. The timestamp synchronization formula is:

[0018] t′=t+Δt;

[0019] In the formula, t′ is the corrected time, t is the original time, and Δt is the time offset;

[0020] S33. Spatial data integration and extraction, using GIS technology to integrate the geographic location data of the synchronized data in S32, extracting key features from the real-time operation data of S1 and the physical defect data of S2;

[0021] S34. Data fusion: real-time operation parameters, physical defect data and user-side power usage data are integrated in the database or data warehouse. The fusion formula is expressed as:

[0022] Z = {z|z∈X∨z∈Y};

[0023] In the formula, Z is the fused data set, X is the real-time operation data set, and Y is the physical defect data set;

[0024] S35. Create data views, design data views, and display the fused data in an easy-to-understand way;

[0025] S36. Establishment of three-dimensional visualization model: Use three-dimensional visualization software to establish a power grid model, map data onto the model, and design a user interaction interface;

[0026] S37. Risk assessment model, define risk assessment objectives, establish probability models, generate random numbers, simulate operation scenarios, calculate risk values, and perform statistical analysis of results.

[0027] Furthermore, in said S36, the three-dimensional visualization model establishing method:

[0028] S361. Using 3D visualization software, the integrated data is presented in the form of a 3D model for easy analysis and understanding. The 3D visualization technology makes complex power grid data easier to understand and analyze; the 3D visualization software is Autodesk 3ds Max or Unity 3D, and a 3D model of the distribution network including substations, transmission lines, and user loads is created in the software;

[0029] S362. Data mapping and interaction design, mapping the integrated data to the three-dimensional model, using colors to represent load levels and shapes to represent device states, and then designing a user interaction interface that allows users to explore the power grid model by rotating, zooming, and selecting; the mapping of the integrated data includes color mapping and device state shape mapping:

[0030] The color mapping is expressed as: C = f(L);

[0031] The device state shape mapping is represented as: S x =g(D);

[0032] C is the color value used to represent the load level; L is the actual value of the load level; f is the mapping function that converts the load value into a color representation;

[0033] S x is a shape parameter used to represent the device state; D is the actual data of the device state; g is a mapping function that converts the device state data into shape changes;

[0034] S363. Dynamic simulation and risk visualization, using time series data to dynamically display the operating status of the power grid, presenting the risk assessment results in a visual way, and highlighting high-risk areas with different colors or warning signs.

[0035] Furthermore, in S37, the method for evaluating the security risk on the user side by combining the power usage data on the user side and the three-dimensional visualization technology includes:

[0036] S371. Define the risk assessment objectives, determine the key risk indicators, and the specific aspects of the assessment, and determine the purpose of the simulation. The risk assessment objectives can be the probability of equipment failure, load fluctuations, or the impact of power outages;

[0037] S372. Establish a probability model, and establish a probability distribution model for each uncertainty factor defined in S331, wherein the probability distribution model includes:

[0038] Exponential distribution model:

[0039] f(t)=λe -λt ;

[0040] In the formula, λ is the failure rate, which represents the probability of equipment failure per unit time, and its reciprocal represents the mean time between equipment failures; t is the time interval between events, and e is the base of the natural logarithm;

[0041] Normal distribution model:

[0042]

[0043] In the formula, μ represents the average power consumption or load level in a period of time; x is the power consumption at any time; σ is the standard deviation, which indicates the dispersion or uncertainty of load fluctuations. σ is a measure of the magnitude of load fluctuations. The larger σ is, the greater the load fluctuation is and the more dispersed the data points are; the smaller σ is, the smaller the load fluctuation is and the more concentrated the data points are on the mean; e is the base of the natural logarithm;

[0044] Weibull distribution model:

[0045]

[0046] In the formula, l represents the time from the start of operation of the device to the occurrence of failure, or the survival time of the device, and l is a random variable that represents the length of time we are interested in until a specific fault or failure event occurs;

[0047] δ is the scale parameter, which is related to the average life of the equipment. δ determines the width of the distribution. δ is the shape parameter β. t =1, i.e., the average life expectancy of the equipment; t is a shape parameter, which determines the shape of the failure time distribution and reflects the degradation rate and failure mode of the equipment. tThe value of affects the skewness and tail characteristics of the distribution, and e is the base of the natural logarithm;

[0048] S373. Generate a random number, using the real-time operation data collected in S1 and the physical defect data identified in S2, and use a pseudo-random number generator to generate a random number, and the generated random number obeys the probability distribution model established in S332;

[0049] S374. Simulate operation scenarios, using the random numbers generated by S373 to simulate different grid operation conditions and predict the performance of the system under various conditions;

[0050] S375. Calculate the risk value. Calculate the risk value based on the probability and consequence of the risk. Quantify the risk in each simulation scenario. The quantitative formula is expressed as:

[0051] R i =Σ(P i ×C i );

[0052] In the formula, R i represents the risk value, P i is the probability of the event occurring, C i is the probability of the event occurring i the corresponding consequences;

[0053] S376. Statistical analysis of results, calculation of statistical indicators such as mean, variance, confidence interval, etc., use charts to display risk distribution, analyze the statistical characteristics of simulation results, and evaluate the distribution of risks.

[0054] Furthermore, in S4, the method of using the risk matrix tool to sort and prioritize risks includes:

[0055] S41. Data synthesis and analysis: synthesize the data collected in S1-S3, use database management system to integrate data from different sources, apply statistical methods to analyze the correlation and distribution characteristics of data, and identify basic trends and outliers in data;

[0056] S42. Cluster analysis and principal component analysis: Use cluster analysis to group samples in the data set according to their characteristics, so that the samples in the same group have high similarity and the samples in different groups have low similarity. Through the clustering results, identify the characteristics and behavior patterns related to risks; use PCA dimensionality reduction to extract the main variation components in the data, calculate the covariance matrix or correlation matrix of the data to identify the linear relationship in the data, and extract the principal components through orthogonal transformation; the orthogonal transformation formula is:

[0057] X=UΣV T ;

[0058] In the formula, X is the original data matrix, where rows represent observations and columns represent variables; U is an orthogonal matrix containing left singular vectors, whose column vectors are called left singular vectors, corresponding to the characteristic directions of the original data; Σ is a diagonal matrix containing singular values, which represent the size of the principal components and reflect the degree of variation of the data in the corresponding directions; V is an orthogonal matrix containing right singular vectors, whose column vectors are called right singular vectors, corresponding to the characteristic directions of the transformed data; V T is the transpose of the matrix V;

[0059] S43. Construct a risk matrix, classify risk factors according to likelihood and severity, and calculate the risk value of each factor using the risk quantification formula in S375;

[0060] S44. Perform sensitivity analysis to determine the degree of influence of key factors on system stability, and calculate the sensitivity coefficient of each factor using the following sensitivity analysis formula;

[0061] Sensitivity coefficient:

[0062] In the formula, C o Indicates the change in output, D i Indicates the change in input;

[0063] S45. Develop risk management methods, including preventive measures and emergency response plans based on risk values ​​and sensitivity coefficients.

[0064] Furthermore, the mathematical description established in S5 is:

[0065] x t+1 =Ax t +Bu t +ω t y t ;

[0066] y t =Cx t +v t ;

[0067] x t is the system state vector at time t; x t+1 is the system state vector at time t+1; u t is the control input; ω t and v t is the noise, ω t is process noise, simulating internal random disturbances, v t is the observation noise, simulation measurement error and external random interference; A is the state transfer matrix, describing the evolution of the state over time; B is the control input matrix, describing the influence of the control input on the state; y tis the observation vector at time t; C is the observation matrix, which describes the mapping from state to observation.

[0068] Furthermore, in S6, using the global sensitivity analysis method to evaluate the relative impact of uncertainty factors specifically includes:

[0069] S61. Determine all parameters in the state space model according to S5, including the state transfer matrix A, the control input matrix B and the observation matrix C;

[0070] S62. The sensitivity index of each parameter is calculated using the Sobol method to quantify the contribution of each parameter to the uncertainty of the system output. The calculation formula of the sensitivity index is:

[0071]

[0072] In the formula, S i is the parameter X i Sobol sensitivity index, E|Y||X i | is in parameter X i The expected value of output Y when fixed, Var(Y) is the variance of output Y;

[0073] S63. Conduct Monte Carlo simulation to generate a large number of system operation scenarios and evaluate the impact of uncertainty factors on system stability;

[0074] S64. Update the risk matrix. Combined with the sensitivity analysis results, update the risk matrix, optimize the model parameters, and improve the prediction accuracy. The updated risk matrix is:

[0075] R n =R i ×S;

[0076] R n is the updated risk value, R i represents the risk value quantified in step S3, S is the sensitivity coefficient;

[0077] S65. Formulate risk management strategies, identify key factors based on sensitivity analysis results, and develop preventive measures for each key factor to reduce the likelihood or impact of risks.

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

[0079] Through comprehensive data collection and the application of multi-physics imaging technology, the present invention can timely and accurately discover equipment defects and potential safety hazards in the power distribution system, thereby improving the reliability and safety of the system.

[0080] During the data integration and risk assessment process, multiple data sources and uncertainty factors are comprehensively considered and combined with three-dimensional visualization technology to make the risk assessment results more intuitive and comprehensive, which helps operation and maintenance personnel quickly understand the system risk status and make scientific decisions.

[0081] The application of state space models and global sensitivity analysis methods can accurately quantify the impact of uncertainties on system stability, identify key factors, provide a strong basis for formulating targeted risk management strategies, effectively reduce risks, and improve the operating efficiency and stability of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A logical block diagram of the quantitative analysis method for uncertain factors of the grid-connected entity's active access to the distribution system;

[0083] Figure 2 A logical block diagram of the method for identifying physical defects and potential safety hazards of equipment;

[0084] Figure 3 A logical block diagram of a risk assessment method combining user-side power usage data;

[0085] Figure 4 A logic block diagram of a method for establishing a three-dimensional visualization model in S36;

[0086] Figure 5 It is a logic block diagram of a method for evaluating security risks on the user side in S37;

[0087] Figure 6 A logical flowchart for the method of using the risk matrix tool to sort and prioritize risks;

[0088] Figure 7 The logical block diagram of the method for evaluating the relative impact of uncertainty factors using the global sensitivity analysis method. DETAILED DESCRIPTION

[0089] 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.

[0090] like Figure 1 As shown in the figure, the quantitative analysis method of uncertain factors of the grid-connected entity actively connected to the distribution system includes the following steps:

[0091] 1. A quantitative analysis method for uncertain factors of a grid-connected entity actively connected to a power distribution system, characterized in that it comprises the following steps:

[0092] S1. Real-time data collection and preliminary analysis, deployment of sensor networks, real-time data transmission to the central monitoring system through the communication network, to achieve real-time monitoring of the power distribution system status;

[0093] S2. Equipment detection and defect identification: Based on the real-time operation data collected in S1, multi-physics imaging technology is used to detect distribution network equipment and identify physical defects and potential safety hazards of the equipment;

[0094] like Figure 2 As shown, in S2, the steps of applying multi-physics imaging technology to identify physical defects and potential safety hazards of distribution network equipment include:

[0095] S21. Based on the data analysis results of S1, determine the power grid equipment that needs to be tested by multi-physics imaging, use an infrared thermal imager to conduct a comprehensive scan of the equipment, analyze the thermal image based on the scanning results, and identify abnormal hot spots or cold spots;

[0096] S22. Perform ultrasonic scanning on the components with abnormal hot spots or cold spots identified in S21, monitor the reflected signals, identify internal defects, and identify equipment defects by combining the imaging results with known failure modes;

[0097] S3. Data integration and risk assessment: integrating the data of S1 and S2 and combining it with the user-side power usage data for risk assessment;

[0098] like Figure 3 As shown, in S3, the specific method of performing risk assessment in combination with user-side power usage data includes:

[0099] S31. Data cleaning and formatting: Integrate the real-time operation data collected by S1 and the physical defect data identified in S2, clean the integrated data, remove invalid or redundant information, and convert the data into a unified format through data cleaning and formatting to facilitate integration and analysis;

[0100] S32. Time synchronization: synchronize data from different data sources according to timestamps to ensure that the data is under the same time base. The timestamp synchronization formula is:

[0101] t′=t+Δt;

[0102] In the formula, t′ is the corrected time, t is the original time, and Δt is the time offset;

[0103] S33. Spatial data integration and extraction, using GIS technology to integrate the geographic location data of the synchronized data in S32, extracting key features from the real-time operation data of S1 and the physical defect data of S2;

[0104] S34. Data fusion: real-time operation parameters, physical defect data and user-side power usage data are integrated in the database or data warehouse. The fusion formula is expressed as:

[0105] Z = {z|z∈X∨z∈Y};

[0106] In the formula, Z is the fused data set, X is the real-time operation data set, and Y is the physical defect data set;

[0107] S35. Create data views, design data views, and display the fused data in an easy-to-understand way;

[0108] S36. Establishment of three-dimensional visualization model: Use three-dimensional visualization software to establish a power grid model, map data onto the model, and design a user interaction interface;

[0109] S37. Risk assessment model, define risk assessment objectives, establish probability models, generate random numbers, simulate operation scenarios, calculate risk values, and perform statistical analysis of results.

[0110] like Figure 4 As shown, in S36, the three-dimensional visualization model establishment method is:

[0111] S361. Using 3D visualization software, the integrated data is presented in the form of a 3D model for easy analysis and understanding. The 3D visualization technology makes complex power grid data easier to understand and analyze; the 3D visualization software is Autodesk 3ds Max or Unity 3D, and a 3D model of the distribution network including substations, transmission lines, and user loads is created in the software;

[0112] S362. Data mapping and interaction design, mapping the integrated data to the three-dimensional model, using colors to represent load levels and shapes to represent device states, and then designing a user interaction interface that allows users to explore the power grid model by rotating, zooming, and selecting; the mapping of the integrated data includes color mapping and device state shape mapping:

[0113] The color mapping is expressed as: C = f(L);

[0114] The device state shape mapping is represented as: S x =g(D);

[0115] C is the color value used to represent the load level; L is the actual value of the load level; f is the mapping function that converts the load value into a color representation;

[0116] S x is a shape parameter used to represent the device state; D is the actual data of the device state; g is a mapping function that converts the device state data into shape changes;

[0117] S363. Dynamic simulation and risk visualization, using time series data to dynamically display the operating status of the power grid, presenting the risk assessment results in a visual way, and highlighting high-risk areas with different colors or warning signs.

[0118] like Figure 5 As shown, in S37, the method for evaluating the security risk on the user side by combining the power usage data on the user side and the three-dimensional visualization technology includes:

[0119] S371. Define the risk assessment objectives, determine the key risk indicators, and the specific aspects of the assessment, and determine the purpose of the simulation. The risk assessment objectives can be the probability of equipment failure, load fluctuations, or the impact of power outages;

[0120] S372. Establish a probability model, and establish a probability distribution model for each uncertainty factor defined in S331, wherein the probability distribution model includes:

[0121] Exponential distribution model:

[0122] f(t)=λe -λt ;

[0123] In the formula, λ is the failure rate, which represents the probability of equipment failure per unit time, and its reciprocal represents the mean time between equipment failures; t is the time interval between events, and e is the base of the natural logarithm;

[0124] Normal distribution model:

[0125]

[0126] In the formula, μ represents the average power consumption or load level in a period of time; x is the power consumption at any time; σ is the standard deviation, which indicates the dispersion or uncertainty of load fluctuations. σ is a measure of the magnitude of load fluctuations. The larger σ is, the greater the load fluctuation is and the more dispersed the data points are; the smaller σ is, the smaller the load fluctuation is and the more concentrated the data points are on the mean; e is the base of the natural logarithm;

[0127] Weibull distribution model:

[0128]

[0129] In the formula, l represents the time from the start of operation of the device to the occurrence of failure, or the survival time of the device, and l is a random variable that represents the length of time we are interested in until a specific fault or failure event occurs;

[0130] δ is the scale parameter, which is related to the average life of the equipment. δ determines the width of the distribution. δ is the shape parameter β.t =1, i.e., the average life expectancy of the equipment; t is a shape parameter, which determines the shape of the failure time distribution and reflects the degradation rate and failure mode of the equipment. t The value of affects the skewness and tail characteristics of the distribution, and e is the base of the natural logarithm;

[0131] S373. Generate a random number, using the real-time operation data collected in S1 and the physical defect data identified in S2, and use a pseudo-random number generator to generate a random number, and the generated random number obeys the probability distribution model established in S332;

[0132] S374. Simulate operation scenarios, using the random numbers generated by S373 to simulate different grid operation conditions and predict the performance of the system under various conditions;

[0133] S375. Calculate the risk value. Calculate the risk value based on the probability and consequence of the risk. Quantify the risk in each simulation scenario. The quantitative formula is expressed as:

[0134] R i =Σ(P i ×C i );

[0135] In the formula, R i represents the risk value, P i is the probability of the event occurring, C i is the probability of the event occurring i the corresponding consequences;

[0136] S376. Statistical analysis of results, calculation of statistical indicators such as mean, variance, confidence interval, etc., use charts to display risk distribution, analyze the statistical characteristics of simulation results, and evaluate the distribution of risks.

[0137] S4. Comprehensive data analysis and risk rating, combining the data from S1 to S3, and using the risk matrix tool to sort and prioritize risks;

[0138] like Figure 6 As shown, in S4, the method of using the risk matrix tool to sort and prioritize risks includes:

[0139] S41. Data synthesis and analysis: synthesize the data collected in S1-S3, use database management system to integrate data from different sources, apply statistical methods to analyze the correlation and distribution characteristics of data, and identify basic trends and outliers in data;

[0140] S42. Cluster analysis and principal component analysis: Use cluster analysis to group samples in the data set according to their characteristics, so that the samples in the same group have high similarity and the samples in different groups have low similarity. Through the clustering results, identify the characteristics and behavior patterns related to risks; use PCA dimensionality reduction to extract the main variation components in the data, calculate the covariance matrix or correlation matrix of the data to identify the linear relationship in the data, and extract the principal components through orthogonal transformation; the orthogonal transformation formula is:

[0141] X=UΣV T ;

[0142] In the formula, X is the original data matrix, where rows represent observations and columns represent variables; U is an orthogonal matrix containing left singular vectors, whose column vectors are called left singular vectors, corresponding to the characteristic directions of the original data; Σ is a diagonal matrix containing singular values, which represent the size of the principal components and reflect the degree of variation of the data in the corresponding directions; V is an orthogonal matrix containing right singular vectors, whose column vectors are called right singular vectors, corresponding to the characteristic directions of the transformed data; V T is the transpose of the matrix V;

[0143] S43. Construct a risk matrix, classify risk factors according to likelihood and severity, and calculate the risk value of each factor using the risk quantification formula in step S375;

[0144] S44. Perform sensitivity analysis to determine the degree of influence of key factors on system stability, and calculate the sensitivity coefficient of each factor using the following sensitivity analysis formula;

[0145] Sensitivity coefficient:

[0146] In the formula, C o Indicates the change in output, D i Indicates the change in input;

[0147] S45. Develop risk management methods, including preventive measures and emergency response plans based on risk values ​​and sensitivity coefficients.

[0148] S5: Establishment and simulation of state space model. Based on the data and analysis results of S1-S4, using time series analysis and state space model, combined with historical data and current monitoring data, establish a mathematical description of the system and quantitatively analyze the specific impact of uncertainty factors on the stability of the distribution system.

[0149] The mathematical description established in S5 is:

[0150] x t+1 =Ax t +Bu t +ω t yt ;

[0151] y t =Cx t +v t ;

[0152] x t is the system state vector at time t; x t+1 is the system state vector at time t+1; u t is the control input; ω t and v t is the noise, ω t is process noise, simulating internal random disturbances, v t is the observation noise, simulation measurement error and external random interference; A is the state transfer matrix, describing the evolution of the state over time; B is the control input matrix, describing the influence of the control input on the state; y t is the observation vector at time t; C is the observation matrix, which describes the mapping from state to observation.

[0153] S6: Global sensitivity analysis and risk management formulation, based on the state space model of S5, use the global sensitivity analysis method to evaluate the relative impact of uncertainty factors.

[0154] like Figure 7 As shown, in S6, the relative impact of uncertainty factors evaluated using the global sensitivity analysis method specifically includes:

[0155] S61. Determine all parameters in the state space model according to S5, including the state transfer matrix A, the control input matrix B and the observation matrix C;

[0156] S62. The sensitivity index of each parameter is calculated using the Sobol method to quantify the contribution of each parameter to the uncertainty of the system output. The calculation formula of the sensitivity index is:

[0157]

[0158] In the formula, S i is the parameter X i Sobol sensitivity index, E|Y||X i | is in parameter X i The expected value of output Y when fixed, Var(Y) is the variance of output Y;

[0159] S63. Conduct Monte Carlo simulation to generate a large number of system operation scenarios and evaluate the impact of uncertainty factors on system stability;

[0160] S64. Update the risk matrix. Combined with the sensitivity analysis results, update the risk matrix, optimize the model parameters, and improve the prediction accuracy. The updated risk matrix is:

[0161] R n =R i ×S;

[0162] R n is the updated risk value, R i represents the quantified risk value in S3, S sensitivity coefficient;

[0163] S65. Formulate risk management strategies, identify key factors based on sensitivity analysis results, and develop preventive measures for each key factor to reduce the likelihood or impact of risks.

[0164] 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 quantitative analysis method for uncertain factors of grid-connected entities actively connected to the distribution system, characterized in that: The following steps are involved: S1. Real-time data collection and preliminary analysis, deployment of sensor networks, real-time data transmission to the central monitoring system through the communication network, to achieve real-time monitoring of the power distribution system status; S2. Equipment detection and defect identification: Based on the real-time operation data collected in S1, multi-physics imaging technology is used to detect distribution network equipment and identify physical defects and potential safety hazards of the equipment; S3. Data integration and risk assessment: integrating the data of S1 and S2 and combining it with the user-side power usage data for risk assessment; S4. Comprehensive data analysis and risk rating, combining the data from S1 to S3, and using the risk matrix tool to sort and prioritize risks; S5: Establishment and simulation of state space model. Based on the data and analysis results of S1-S4, using time series analysis and state space model, combined with historical data and current monitoring data, establish a mathematical description of the system and quantitatively analyze the specific impact of uncertainty factors on the stability of the distribution system. S6: Global sensitivity analysis and risk management formulation, based on the state space model of S5, use the global sensitivity analysis method to evaluate the relative impact of uncertainty factors; In S2, the step of using multi-physics imaging technology to identify physical defects and potential safety hazards of distribution network equipment includes: S21. Based on the data analysis results of S1, determine the power grid equipment that needs to be tested by multi-physics imaging, use an infrared thermal imager to conduct a comprehensive scan of the equipment, analyze the thermal image based on the scanning results, and identify abnormal hot spots or cold spots; S22. Perform ultrasonic scanning on the components with abnormal hot spots or cold spots identified in S21, monitor the reflected signals, identify internal defects, and identify equipment defects by combining the imaging results with known failure modes; In S3, the specific method of performing risk assessment in combination with the user-side power usage data includes: S31. Data cleaning and formatting: Integrate the real-time operation data collected by S1 and the physical defect data identified in S2, clean the integrated data, remove invalid or redundant information, and convert the data into a unified format through data cleaning and formatting to facilitate integration and analysis; S32. Time synchronization: synchronize data from different data sources according to timestamps to ensure that the data is under the same time base. The timestamp synchronization formula is: t′=t+Δt; In the formula, t′ is the corrected time, t is the original time, and Δt is the time offset; S33. Spatial data integration and extraction, using GIS technology to integrate the geographic location data of the synchronized data in S32, extracting key features from the real-time operation data of S1 and the physical defect data of S2; S34. Data fusion: real-time operation parameters, physical defect data and user-side power usage data are integrated in the database or data warehouse. The fusion formula is expressed as: Z = {zz∈X∨z∈Y}; In the formula, Z is the fused data set, X is the real-time operation data set, and Y is the physical defect data set; S35. Create data views, design data views, and display the fused data in an easy-to-understand way; S36. Establishment of three-dimensional visualization model: Use three-dimensional visualization software to establish a power grid model, map data onto the model, and design a user interaction interface; S37. Risk assessment model, define risk assessment objectives, establish probability models, generate random numbers, simulate operation scenarios, calculate risk values, and perform statistical analysis of results.

2. According to claim 1, a quantitative analysis method for uncertain factors of a grid-connected entity actively connected to a power distribution system is characterized in that: In S36, the three-dimensional visualization model establishment method: S361. Using 3D visualization software, the integrated data is presented in the form of a 3D model for easy analysis and understanding. The 3D visualization technology makes complex power grid data easier to understand and analyze; the 3D visualization software is Autodesk 3ds Max or Unity 3D, and a 3D model of the distribution network including substations, transmission lines, and user loads is created in the software; S362. Data mapping and interaction design, mapping the integrated data to the three-dimensional model, using colors to represent load levels and shapes to represent device states, and then designing a user interaction interface that allows users to explore the power grid model by rotating, zooming, and selecting; the mapping of the integrated data includes color mapping and device state shape mapping: The color mapping is expressed as: C = f(L); The device state shape mapping is represented as: S x =g(D); C is the color value used to represent the load level; L is the actual value of the load level; f is the mapping function that converts the load value into a color representation; Sx is a shape parameter used to represent the device state; D is the actual data of the device state; g is a mapping function that converts the device state data into shape changes; S363. Dynamic simulation and risk visualization, using time series data to dynamically display the operating status of the power grid, presenting the risk assessment results in a visual way, and highlighting high-risk areas with different colors or warning signs.

3. The method for quantitatively analyzing uncertain factors of a grid-connected entity actively connected to a power distribution system according to claim 2, characterized in that: In S37, the method for evaluating the security risk on the user side by combining the power usage data on the user side and the three-dimensional visualization technology includes: S371. Define the risk assessment objectives, determine the key risk indicators, and the specific aspects of the assessment, and determine the purpose of the simulation. The risk assessment objectives can be the probability of equipment failure, load fluctuations, or the impact of power outages; S372. Establish a probability model, and establish a probability distribution model for each uncertainty factor defined in S331, wherein the probability distribution model includes: Exponential distribution model: f ( t ) =λe-λt; In the formula, λ is the failure rate, which represents the probability of equipment failure per unit time, and its reciprocal represents the mean time between equipment failures; t is the time interval between events, and e is the base of the natural logarithm; Normal distribution model: In the formula, μ represents the average power consumption or load level in a period of time; x is the power consumption at any time; σ is the standard deviation, which indicates the dispersion or uncertainty of load fluctuations. σ is a measure of the magnitude of load fluctuations. The larger σ is, the greater the load fluctuation is and the more dispersed the data points are; the smaller σ is, the smaller the load fluctuation is and the more concentrated the data points are on the mean; e is the base of the natural logarithm; Weibull distribution model: In the formula, l represents the time from the start of operation of the device to the occurrence of failure, or the survival time of the device, and l is a random variable that represents the length of time we are interested in until a specific fault or failure event occurs; δ is a scale parameter, which is related to the average life of the equipment and determines the width of the distribution. δ is the median life when the shape parameter βt=1, that is, the average expected life of the equipment. βt is a shape parameter, which determines the shape of the failure time distribution and reflects the degradation rate and failure mode of the equipment. The value of βt affects the skewness and tail characteristics of the distribution. e is the base of the natural logarithm. S373. Generate a random number, using the real-time operation data collected in S1 and the physical defect data identified in step S2, and use a pseudo-random number generator to generate a random number, and the generated random number obeys the probability distribution model established in S332; S374. Simulate operation scenarios, using the random numbers generated by S373 to simulate different grid operation conditions and predict the performance of the system under various conditions; S375. Calculate the risk value. Calculate the risk value based on the probability and consequence of the risk. Quantify the risk in each simulation scenario. The quantitative formula is expressed as: Ri=Σ(Pi×Ci); In the formula, Ri represents the risk value, Pi is the probability of the event occurring, and Ci is the consequence corresponding to the probability Pi of the event occurring; S376. Statistical analysis of results, calculation of statistical indicators such as mean, variance, confidence interval, etc., use charts to display risk distribution, analyze the statistical characteristics of simulation results, and evaluate the distribution of risks.

4. The method for quantitatively analyzing uncertain factors of a grid-connected entity actively connected to a power distribution system according to claim 1, characterized in that: In S4, the method of using the risk matrix tool to sort and prioritize risks includes: S41. Data synthesis and analysis: synthesize the data collected in S1-S3, use database management system to integrate data from different sources, apply statistical methods to analyze the correlation and distribution characteristics of data, and identify basic trends and outliers in data; S42. Cluster analysis and principal component analysis: Use cluster analysis to group samples in the data set according to their characteristics, so that the samples in the same group have high similarity and the samples in different groups have low similarity. Through the clustering results, identify the characteristics and behavior patterns related to risks; use PCA dimensionality reduction to extract the main variation components in the data, calculate the covariance matrix or correlation matrix of the data to identify the linear relationship in the data, and extract the principal components through orthogonal transformation; the orthogonal transformation formula is: X=UΣVT; In the formula, X is the original data matrix, where rows represent observations and columns represent variables; U is an orthogonal matrix containing left singular vectors, whose column vectors are called left singular vectors, corresponding to the characteristic directions of the original data; Σ is a diagonal matrix containing singular values, which represent the size of the principal components and reflect the degree of variation of the data in the corresponding directions; V is an orthogonal matrix containing right singular vectors, whose column vectors are called right singular vectors, corresponding to the characteristic directions of the transformed data; VT is the transpose of the matrix V; S43. Construct a risk matrix, classify risk factors according to likelihood and severity, and calculate the risk value of each factor using the risk quantification formula in step S375; S44. Perform sensitivity analysis to determine the degree of influence of key factors on system stability, and calculate the sensitivity coefficient of each factor using the following sensitivity analysis formula; Sensitivity coefficient: In the formula, Co represents the change in output, and Di represents the change in input; S45. Develop risk management methods, including preventive measures and emergency response plans based on risk values ​​and sensitivity coefficients.

5. The method for quantitatively analyzing uncertain factors of a grid-connected entity actively connected to a power distribution system according to claim 1, characterized in that: The mathematical description established in S5 is: xt+1=Axt+But+ωtyt; yt=Cxt+vt; xt is the system state vector at time t; xt+1 is the system state vector at time t+1; ut is the control input; ωt and vt are noises, ωt is process noise, simulating internal random disturbances, vt is observation noise, simulating measurement errors and external random interferences; A is the state transfer matrix, describing the evolution of the state over time; B is the control input matrix, describing the impact of the control input on the state; yt is the observation vector at time t; C is the observation matrix, describing the mapping from state to observation.

6. The method for quantitatively analyzing uncertain factors of a grid-connected entity actively connected to a power distribution system according to claim 1, characterized in that: In S6, the relative impact of uncertainty factors evaluated using the global sensitivity analysis method specifically includes: S61. Determine all parameters in the state space model according to S5, including the state transfer matrix A, the control input matrix B and the observation matrix C; S62. The sensitivity index of each parameter is calculated using the Sobol method to quantify the contribution of each parameter to the uncertainty of the system output. The calculation formula of the sensitivity index is: In the formula, Si is the Sobol sensitivity index of parameter Xi, and EY‖Xi| is the sensitivity index of parameter X. i The expected value of output Y when fixed, Var(Y) is the variance of output Y; S63. Conduct Monte Carlo simulation to generate a large number of system operation scenarios and evaluate the impact of uncertainty factors on system stability; S64. Update the risk matrix. Combined with the sensitivity analysis results, update the risk matrix, optimize the model parameters, and improve the prediction accuracy. The updated risk matrix is: Rn=Ri×S; Rn is the updated risk value, Ri represents the quantified risk value in S3, and S is the sensitivity coefficient; S65. Formulate risk management strategies, identify key factors based on sensitivity analysis results, and develop preventive measures for each key factor to reduce the likelihood or impact of risks.

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