A correction method and system based on reliability of independent power grid power supply system

Abstract

The present invention discloses a correction method and system based on the reliability of an independent power grid power supply system, which relates to the field of electric power automation technology, including: predicting the power supply load state of an independent power grid in the future; judging whether the predicted power supply load state of the independent power grid in the future is within the actual power supply load demand range of the independent power grid, and if it is determined that the independent power grid is abnormal, generating a fault probability distribution diagram of component components and a risk type database of component components; performing correlation matching to determine the risk coefficients of the component components of the independent power grid; correcting the predicted power supply load state of the independent power grid in the future according to the risk coefficients of the component components of the independent power grid, performing reliability verification according to a simulation function SIM, and generating a reliability score of the independent power grid; generating maintenance tasks for the independent power grid power supply system according to the stability level corresponding to the reliability score of the independent power grid; the present invention has the advantages of improving the power supply reliability of the independent power grid and enhancing the stability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power automation, and particularly to a correction method and system for the reliability of an independent power grid power supply system. Background Art

[0002] As the components of an independent power grid age, the overall stability gradually decreases. However, when no substantial power grid disaster occurs, the components can still issue restart requests in the face of abnormal fluctuations until all the components of the independent power grid reach the online state to ensure the normal operation of the independent power grid. However, when the overall stability of the components of the independent power grid is at a relatively low level, repeated restart requests will further consume the resources of the entire system, causing the independent power grid power supply system to collapse. Summary of the Invention

[0003] To solve the above technical problems, a correction method and system for the reliability of an independent power grid power supply system are provided, and the present technical solution solves the above problems.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A correction method and system for the reliability of an independent power grid power supply system, comprising:

[0006] Based on the historical power supply data of the independent power grid, analyze the power supply trend of the historical power supply data, and predict the future power supply load status of the independent power grid;

[0007] Judge whether the predicted future power supply load status of the independent power grid is within the range of the actual power supply load demand of the independent power grid. If so, it is determined that the independent power grid is normal; if not, it is determined that the independent power grid is abnormal;

[0008] If it is determined that the independent power grid is abnormal, obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution map of the components;

[0009] Based on the known risk types of the power grid components, establish a risk type database for the components;

[0010] Perform correlation matching according to the failure probability distribution map of the components of the independent power grid and the risk type database of the components to determine the risk coefficient of the components of the independent power grid;

[0011] According to the risk coefficient of the components of the independent power grid, correct the predicted future power supply load status of the independent power grid, perform reliability verification according to the simulation function SIM, and generate a reliability score of the independent power grid;

[0012] Generate maintenance tasks for the independent power grid power supply system according to the stability level corresponding to the reliability score of the independent power grid;

[0013] Among them, the correction of the predicted future power supply load status of the independent power grid according to the risk coefficient of the components of the independent power grid is specifically as follows:

[0014] ;

[0015] In the formula, is the corrected index of the power supply load status of the predicted future independent power grid at t unit times, is the index of the power supply load status of the predicted future independent power grid at t unit times, is the risk coefficient of the v-th component of the independent power grid;

[0016] Among them, the reliability score of the independent power grid is specifically as follows:

[0017] ;

[0018] In the formula, is the reliability score of the independent power grid at the t unit time node.

[0019] Preferably, based on the historical power supply data of the independent power grid, analyze the power supply trend of the historical power supply data, and predicting the future power supply load status of the independent power grid specifically includes:

[0020] Based on the historical power supply data of the independent power grid, preprocess the missing values and outliers in the historical power supply data;

[0021] According to the historical power supply data of the independent power grid, divide it according to the time attribute to obtain the historical power supply time series data of the independent power grid;

[0022] Based on the historical power supply time series data of the independent power grid, use the STL time series analysis method to extract the characteristics of the historical power supply time series data and obtain the historical power supply time series characteristic data of the independent power grid;

[0023] Based on the ARIMA autoregressive moving average model, construct a power supply load analysis model for the independent power grid;

[0024] Based on the historical power supply time series characteristic data of the independent power grid, substitute it into the power supply load analysis model of the independent power grid, use the historical power supply time series characteristics as the input, use the minimum model error function under the optimal autoregressive coefficient and moving average coefficient of the model as the training end target, and use the predicted future power supply load status index of the independent power grid as the output;

[0025] Among them, the power supply load analysis model of the independent power grid is specifically as follows:

[0026] ;

[0027] Wherein, is the power supply load status index for predicting the future independent power grid in t unit time, is the constant term, is the autoregressive coefficient of the i-th lag order, is for the independent power grid historical power supply time series characteristic values of the lag order of unit time, is the j-th moving average coefficient, is for the independent power grid random error of the moving average order of unit time, n is the total number of lag orders, and m is the total number of moving average orders.

[0028] Preferably, determining whether the predicted future power supply load status of the independent power grid is within the range of the actual power supply load demand of the independent power grid specifically includes:

[0029] According to the time series, mark the power supply load status index of the predicted future independent power grid and the range of the actual power supply load demand of the independent power grid, and determine the power supply load status index of each unit time node of the predicted future independent power grid and the range of the actual power supply load demand of each unit time node of the independent power grid;

[0030] Use linear mapping to perform vector conversion on the power supply load status index of each unit time node of the predicted future independent power grid and the range of the actual power supply load demand of each unit time node of the independent power grid, and obtain the power supply load status vector index of each unit time node of the predicted future independent power grid and the range of the actual power supply load vector demand of each unit time node of the independent power grid;

[0031] Use the Euclidean distance formula to calculate the spatial range between the power supply load status vector index of each unit time node of the predicted future independent power grid and the range of the actual power supply load vector demand of each unit time node of the independent power grid, and determine that the power supply load status vector index of each unit time node is within the range of the actual power supply load vector demand.

[0032] Preferably, the determination that the power supply load status vector index of each unit time node is within the range of the actual power supply load vector demand is specifically:

[0033] ;

[0034] Wherein, is that the predicted future independent power grid at the t-th unit time node is within the range of the actual power supply load vector demand of the t-th unit time node of the independent power grid, To predict the power supply load status vector index of the independent power grid at the t-th unit time node in the future, is the lower limit of the actual power supply load vector demand range of the independent power grid at the t-th unit time node, is the upper limit of the actual power supply load vector demand range of the independent power grid at the t-th unit time node.

[0035] Preferably, if it is determined that there is an abnormality in the independent power grid, obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution map of the components, which specifically includes:

[0036] Based on the service life of the independent power grid, determine the basic performance loss status of the components of the independent power grid;

[0037] According to the basic performance loss status of the components of the independent power grid, initialize the standardized operation parameter array of the components of the independent power grid;

[0038] Based on the operation log of the independent power grid, obtain the historical operation parameters of the components of the independent power grid and perform deviation operations with the initialized standardized operation parameter array of the components of the independent power grid, mark the deviation parameters of the components of the independent power grid, and record them as the abnormal characteristic data of the components of the independent power grid;

[0039] Take the abnormal characteristic data of the components of the independent power grid as positive samples, take the initialized standardized operation parameter array of the components of the independent power grid as negative samples, and establish the original data set of the components of the independent power grid;

[0040] Based on Logistic regression, construct a failure probability prediction model for the components of the independent power grid;

[0041] Substitute the original data set of the components of the independent power grid into the failure probability prediction model of the components of the independent power grid, use the abnormal characteristic data of the components of the independent power grid as the input, and use the failure probability value of the components of the independent power grid as the output to generate a failure probability distribution map of the components of the independent power grid.

[0042] Preferably, the failure probability prediction model of the components of the independent power grid is specifically:

[0043] ;

[0044] In the formula, is the failure probability value of the components of the independent power grid under the abnormal characteristic data U of the components of the given independent power grid, is the k-th abnormal characteristic data of the v-th component of the independent power grid, is the intercept term, , , is the regression coefficient, is the logarithmic function.

[0045] Preferably, the failure probability distribution map of the components of the independent power grid is associated and matched with the risk type database of the components to determine the risk coefficient of the components of the independent power grid;

[0046] Based on the failure probability distribution map of the components of the independent power grid, the failure probability distribution values of the components of each independent power grid are standardized;

[0047] The one-hot encoding is used to perform vector conversion on the failure probability distribution map of the components of the independent power grid to obtain the failure probability distribution vector of the components of each independent power grid;

[0048] Analyze the matching degree between the failure probability distribution vector of the components of each independent power grid and the risk types in the risk type database of the components to determine the failure risk type matching index of the components of each independent power grid;

[0049] According to the failure risk type matching index of the components of each independent power grid, assign weights to the failure risk types of the components of the independent power grid;

[0050] Calculate the risk coefficient of the components of the independent power grid according to the failure probability value of the components of the independent power grid and the weight of the failure risk type of the components of the independent power grid.

[0051] Preferably, the determination of the failure risk type matching index of the components of each independent power grid specifically includes:

[0052] ;

[0053] In the formula, is the z-th risk type matching index of the failure of the v-th component of the independent power grid, is the failure probability distribution vector of the v-th component of the independent power grid, is the z-th risk type of the v-th component in the risk type database;

[0054] Among them, the risk coefficient of the components of the independent power grid specifically includes:

[0055] ;

[0056] In the formula, is the risk coefficient of the v-th component of the independent power grid, is the failure probability value of the v-th component of the independent power grid, is the z-th risk type weight of the failure of the v-th component of the independent power grid.

[0057] The present invention also discloses a correction system based on the reliability of an independent power grid power supply system, including:

[0058] A load prediction module, based on the historical power supply data of the independent power grid, analyzes the power supply trend of the historical power supply data, and predicts the power supply load status of the future independent power grid;

[0059] An abnormality judgment module, which is electrically connected to the load prediction module. The abnormality judgment module is used to judge whether the predicted power supply load status of the future independent power grid is within the range of the actual power supply load demand of the independent power grid. If so, it is determined that the independent power grid has no abnormality. If not, it is determined that the independent power grid has an abnormality;

[0060] A failure probability graph module, which is electrically connected to the abnormality judgment module. The failure probability graph module is used to, if it is determined that the independent power grid has an abnormality, obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution graph of the components;

[0061] A risk database, based on the known risk types of power grid components, establishes a risk type database for the components;

[0062] A risk coefficient module, which is electrically connected to the risk database and the failure probability graph module. The risk coefficient module is used to perform correlation matching according to the failure probability distribution graph of the components of the independent power grid and the risk type database of the components, and determine the risk coefficient of the components of the independent power grid;

[0063] A reliability verification module, which is electrically connected to the risk coefficient module. The reliability verification module is used to correct the predicted power supply load status of the future independent power grid according to the risk coefficient of the components of the independent power grid, perform reliability verification according to the simulation function SIM, and generate a reliability score of the independent power grid;

[0064] A maintenance task module, which is electrically connected to the reliability verification module. The maintenance task module is used to generate a maintenance task for the independent power grid power supply system according to the stability level corresponding to the reliability score of the independent power grid.

[0065] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0066] The present invention proposes a correction scheme for the reliability of an independent power grid power supply system. By analyzing the historical power supply data and real-time power supply data of the independent power grid, the future power supply load status is predicted and compared with the actual demand to determine whether there is an abnormality in the power grid. Once an abnormality is detected, the system evaluates the risk coefficient of the power grid by analyzing the operation logs and failure probability distributions of the power grid components and combining with a known risk type database. The future load status is corrected using this risk coefficient, and reliability verification is performed through a simulation function, and finally a reliability score of the power grid is generated. The beneficial effects of this scheme are: improving the power supply reliability of the independent power grid and enhancing the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flowchart of a correction method for the reliability of an independent power grid power supply system;

[0068] Figure 2 It is a flowchart of a method for predicting the power supply load status of an independent power grid in the future;

[0069] Figure 3 It is a flowchart of a method for determining whether the predicted power supply load status of an independent power grid in the future is within the range of the actual power supply load demand of the independent power grid;

[0070] Figure 4 It is a flowchart of a method for generating a failure probability distribution diagram of component parts;

[0071] Figure 5 It is a flowchart of a method for determining the risk coefficient of the component parts of an independent power grid;

[0072] Figure 6 It is a framework diagram of a correction system for the reliability of an independent power grid power supply system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0074] Referring to Figure 1 As shown, a correction method for the reliability of an independent power grid power supply system includes:

[0075] Based on the historical power supply data of the independent power grid, analyze the power supply trend of the historical power supply data and predict the power supply load status of the independent power grid in the future;

[0076] Determine whether the predicted power supply load status of the independent power grid in the future is within the range of the actual power supply load demand of the independent power grid. If so, it is determined that the independent power grid is normal; if not, it is determined that the independent power grid is abnormal;

[0077] If it is determined that the independent power grid is abnormal, obtain the operation log of the independent power grid, mark the historical operation parameters of the components that make up the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution map of the components;

[0078] Based on the known risk types of the power grid components, establish a risk type database for the components;

[0079] Perform correlation matching according to the failure probability distribution map of the components of the independent power grid and the risk type database of the components to determine the risk coefficient of the components of the independent power grid;

[0080] According to the risk coefficient of the components of the independent power grid, correct the predicted future power supply load status of the independent power grid, perform reliability verification according to the simulation function SIM, and generate a reliability score for the independent power grid;

[0081] Generate maintenance tasks for the power supply system of the independent power grid according to the stability level corresponding to the reliability score of the independent power grid;

[0082] Among them, the correction of the predicted future power supply load status of the independent power grid according to the risk coefficient of the components of the independent power grid is specifically:

[0083] ;

[0084] In the formula, is the index for correcting the predicted future power supply load status of the independent power grid at t unit times, is the index for predicting the future power supply load status of the independent power grid at t unit times, is the risk coefficient of the v-th component of the independent power grid;

[0085] Among them, the reliability score of the independent power grid is specifically:

[0086] ;

[0087] In the formula, is the reliability score of the independent power grid at the t-th unit time node.

[0088] It should be noted that the simulation function SIM here is the interface of the simulink model.

[0089] This solution analyzes the historical power supply data and real-time power supply data of an independent power grid to predict the future power supply load status, compares it with the actual demand, and determines whether there are abnormalities in the power grid. Once an abnormality is detected, the system evaluates the risk coefficient of the power grid by analyzing the operation logs and failure probability distributions of power grid components and combining with a known risk type database. The future load status is corrected using this risk coefficient, and reliability verification is performed through a simulation function, finally generating a reliability score for the power grid. The beneficial effects of this solution are: improving the power supply reliability of the independent power grid and enhancing the stability of the power system.

[0090] Refer to Figure 2 As shown, based on the historical power supply data of the independent power grid, analyzing the power supply trend of the historical power supply data and predicting the future power supply load status of the independent power grid specifically includes:

[0091] Based on the historical power supply data of the independent power grid, preprocess the missing values and outliers in the historical power supply data;

[0092] According to the historical power supply data of the independent power grid, divide it according to time attributes to obtain the historical power supply time series data of the independent power grid;

[0093] Based on the historical power supply time series data of the independent power grid, use the STL time series analysis method to extract the characteristics of the historical power supply time series data and obtain the historical power supply time series characteristic data of the independent power grid;

[0094] Based on the ARIMA autoregressive moving average model, construct a power supply load analysis model for the independent power grid;

[0095] Substitute the historical power supply time series characteristic data of the independent power grid into the power supply load analysis model of the independent power grid, use the historical power supply time series characteristics as the input, use the minimum model error function under the optimal autoregressive coefficient and moving average coefficient of the model as the training end target, and use the prediction of the future power supply load status index of the independent power grid as the output;

[0096] Among them, the power supply load analysis model of the independent power grid is specifically:

[0097] ;

[0098] In the formula, is the index for predicting the power supply load status of the independent power grid at t unit times in the future, is the constant term, is the autoregressive coefficient of the i-th lag order, is for the independent power grid historical power supply time series characteristic values of the unit time lag order, is the moving average coefficient of the j-th, is for the independent power grid The random error of the moving average order per unit time, n is the total number of lag orders, and m is the total number of moving average orders.

[0099] Refer to Figure 3 As shown, determining whether the predicted power supply load status of the future independent power grid is within the range of the actual power supply load demand of the independent power grid specifically includes:

[0100] According to the time series, mark the power supply load status index of the predicted future independent power grid and the range of the actual power supply load demand of the independent power grid, and determine the power supply load status index of each unit time node of the predicted future independent power grid and the range of the actual power supply load demand of each unit time node of the independent power grid;

[0101] Use linear mapping to perform vector conversion on the power supply load status index of each unit time node of the predicted future independent power grid and the range of the actual power supply load demand of each unit time node of the independent power grid, and obtain the power supply load status vector index of each unit time node of the predicted future independent power grid and the range of the actual power supply load vector demand of each unit time node of the independent power grid;

[0102] Use the Euclidean distance formula to calculate the spatial range between the power supply load status vector index of each unit time node of the predicted future independent power grid and the range of the actual power supply load vector demand of each unit time node of the independent power grid, and determine that the power supply load status vector index of each unit time node is within the range of the actual power supply load vector demand;

[0103] Among them, the determination that the power supply load status vector index of each unit time node is within the range of the actual power supply load vector demand is specifically:

[0104] ;

[0105] In the formula, is that the predicted power supply load status of the future independent power grid at the t-th unit time node is within the range of the actual power supply load vector demand of the t-th unit time node of the independent power grid, is the power supply load status vector index of the predicted future independent power grid at the t-th unit time node, is the lower limit of the actual power supply load vector demand of the t-th unit time node of the independent power grid, is the upper limit of the actual power supply load vector demand of the t-th unit time node of the independent power grid.

[0106] This solution predicts the power supply load and the actual power supply load demand through time series markers, converts these data into vector form using linear mapping, and then determines whether the predicted power supply load state is within the range of the actual power supply load demand by calculating the Euclidean distance, thereby determining whether the independent power grid is abnormal. The beneficial effect is that it can accurately and efficiently evaluate the power supply load state of the independent power grid, and timely discover and solve potential power grid abnormal problems.

[0107] Refer to Figure 4 As shown, if it is determined that the independent power grid is abnormal, obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution map of the components, which specifically includes:

[0108] Based on the service life of the independent power grid, determine the basic performance loss state of the components of the independent power grid;

[0109] According to the basic performance loss state of the components of the independent power grid, initialize the standardized operation parameter array of the components of the independent power grid;

[0110] Based on the operation log of the independent power grid, obtain the historical operation parameters of the components of the independent power grid and perform deviation operations with the initialized standardized operation parameter array of the components of the independent power grid, mark the deviation parameters of the components of the independent power grid, and record them as the abnormal feature data of the components of the independent power grid;

[0111] Take the abnormal feature data of the components of the independent power grid as positive samples, and take the initialized standardized operation parameter array of the components of the independent power grid as negative samples to establish the original data set of the components of the independent power grid;

[0112] Based on Logistic regression, construct a failure probability prediction model for the components of the independent power grid;

[0113] Substitute the original data set of the components of the independent power grid into the failure probability prediction model of the components of the independent power grid, use the abnormal feature data of the components of the independent power grid as the input, and use the failure probability value of the components of the independent power grid as the output to generate a failure probability distribution map of the components of the independent power grid;

[0114] Among them, the failure probability prediction model of the components of the independent power grid is specifically:

[0115] ;

[0116] In the formula, is the failure probability value of the components of the independent power grid under the abnormal feature data U of the components of the given independent power grid, is the k-th abnormal feature data of the v-th component of the independent power grid, is the intercept term, , , are the regression coefficients, is the logarithmic function.

[0117] Evaluate the basic performance loss status of the components based on the service life of the independent power grid, and initialize the standardized operation parameter array. Then, obtain the historical operation parameters of the components using the power grid operation log, perform deviation calculation with the standardized parameters, and mark the abnormal feature data. Next, construct the original data set including the positive samples of the abnormal feature data and the negative samples of the standardized parameters. Based on these data, use Logistic regression to construct the fault probability prediction model. Finally, substitute the original data set into the model to generate the fault probability distribution map of the components, providing a reference vector for subsequent identification of specific faults of the components.

[0118] Refer to Figure 5 As shown, perform correlation matching between the fault probability distribution map of the components of the independent power grid and the risk type database of the components to determine the risk coefficient of the components of the independent power grid;

[0119] Based on the fault probability distribution map of the components of the independent power grid, perform standardization processing on the fault probability distribution values of each component of the independent power grid;

[0120] Use one-hot encoding to perform vector conversion on the fault probability distribution map of the components of the independent power grid to obtain the fault probability distribution vector of each component of the independent power grid;

[0121] Analyze the matching degree between the fault probability distribution vector of each component of the independent power grid and the risk types in the risk type database of the components to determine the fault risk type matching index of each component of the independent power grid;

[0122] According to the fault risk type matching index of each component of the independent power grid, assign weights to the fault risk types of the components of the independent power grid;

[0123] Calculate the risk coefficient of the components of the independent power grid according to the fault probability value of the components of the independent power grid and the weight of the fault risk type of the components of the independent power grid;

[0124] Among them, the determination of the fault risk type matching index of each component of the independent power grid specifically includes:

[0125] ;

[0126] In the formula, is the z-th risk type matching index of the v-th component failure of the independent power grid, is the failure probability distribution vector of the v-th component of the independent power grid, is the z-th risk type of the v-th component in the risk type database;

[0127] Among them, the risk coefficients of the components of the independent power grid specifically include:

[0128] ;

[0129] In the formula, is the risk coefficient of the v-th component of the independent power grid, is the failure probability value of the v-th component of the independent power grid, is the weight of the z-th risk type of the failure of the v-th component of the independent power grid.

[0130] In this solution, by standardizing the failure probability distribution diagram of the components of the independent power grid, it is ensured that the failure probability values of each component are on the same scale, and the one-hot encoding technology is used to convert these failure probability distribution diagrams into vectors to match the risk type database of the components, and the failure risk type matching index of each component is determined. Based on the failure risk type matching index, corresponding weights are assigned to each failure risk type. Finally, by combining the failure probability value of the component and the weight of the failure risk type, the risk coefficient of the components of the independent power grid is comprehensively calculated, which can more truly reflect the actual risk situation of the power grid.

[0131] Referring to Figure 6 shown, based on the same inventive concept of a correction method based on the reliability of the independent power grid power supply system, a correction system based on the reliability of the independent power grid power supply system is proposed, including:

[0132] A load prediction module, based on the historical power supply data of the independent power grid, analyzes the power supply trend of the historical power supply data, and predicts the power supply load status of the future independent power grid;

[0133] An abnormality judgment module, the abnormality judgment module is electrically connected to the load prediction module, and the abnormality judgment module is used to judge whether the predicted power supply load status of the future independent power grid is within the range of the actual power supply load demand of the independent power grid. If so, it is determined that the independent power grid has no abnormality. If not, it is determined that the independent power grid has an abnormality;

[0134] A failure probability diagram module, the failure probability diagram module is electrically connected to the abnormality judgment module, and the failure probability diagram module is used to, if it is determined that the independent power grid has an abnormality, obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a failure probability distribution diagram of the components;

[0135] A risk database, based on the known risk types of the power grid components, establishes a risk type database of the components;

[0136] A risk coefficient module, electrically connected to a risk database and a failure probability diagram module. The risk coefficient module is used to perform correlation matching based on the failure probability distribution diagram of the components of an independent power grid and the risk type database of the components, and determine the risk coefficients of the components of the independent power grid;

[0137] A reliability verification module, electrically connected to the risk coefficient module. The reliability verification module is used to correct the predicted future power supply load status of the independent power grid according to the risk coefficients of the components of the independent power grid, and perform reliability verification according to the simulation function SIM to generate the reliability score of the independent power grid;

[0138] A maintenance task module, electrically connected to the reliability verification module. The maintenance task module is used to generate the maintenance tasks of the power supply system of the independent power grid according to the stability level corresponding to the reliability score of the independent power grid.

[0139] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A correction method based on the reliability of an independent power grid power supply system, characterized in that: include: Based on the historical power supply data of the independent power grid, analyze the power supply trend of the historical power supply data and predict the power supply load status of the independent power grid in the future; Determine whether the predicted future power supply load state of the independent power grid is within the actual power supply load demand range of the independent power grid. If so, determine that the independent power grid is normal; if not, determine that the independent power grid is abnormal; If it is determined that the independent power grid is abnormal, the operation log of the independent power grid is obtained, the historical operation parameters of the components of the independent power grid are marked, the deviation distribution of the components is analyzed, and a failure probability distribution diagram of the components is generated; Based on the known risk types of power grid components, a risk category database of component components is established; Determine the risk coefficient of the components of the independent power grid by associating and matching the failure probability distribution map of the components of the independent power grid with the risk category database of the components; According to the risk coefficients of the components of the independent power grid, the power supply load state of the predicted future independent power grid is corrected, and the reliability verification is performed according to the simulation function SIM to generate the reliability score of the independent power grid; Generate maintenance tasks for the independent power grid power supply system according to the stability level corresponding to the reliability score of the independent power grid; The correction according to the risk coefficient of the components of the independent power grid for predicting the power supply load state of the future independent power grid is specifically: ； In the formula, To correct and predict the power supply load state index of the future independent power grid for t unit time, To predict the power supply load state index of the future independent power grid for t unit time, is the risk factor of the vth component of the independent power grid; The reliability score of the independent power grid is specifically: ； In the formula, is the reliability score of the node in the independent power grid at t unit time.

2. A method for correcting the reliability of an independent power grid power supply system according to claim 1, characterized in that: Based on the historical power supply data of the independent power grid, the power supply trend of the historical power supply data is analyzed, and the power supply load status of the independent power grid in the future is predicted, including: Based on the historical power supply data of the independent power grid, pre-process the missing values ​​and abnormal values ​​in the historical power supply data; According to the historical power supply data of the independent power grid, the data is divided according to the time attribute to obtain the historical power supply time series data of the independent power grid; Based on the historical power supply time series data of the independent power grid, the STL time series analysis method is used to extract the features of the historical power supply time series data to obtain the historical power supply time series feature data of the independent power grid; Based on the ARIMA autoregressive moving average model, an independent power grid power supply load analysis model is constructed; The historical power supply time series characteristic data of the independent power grid is substituted into the power supply load analysis model of the independent power grid, with the historical power supply time series characteristics as input, the minimum model error function under the optimal autoregressive coefficient and moving average coefficient of the model as the training end target, and the predicted power supply load state index of the future independent power grid as the output; The power supply load analysis model of the independent power grid is specifically as follows: ； In the formula, To predict the power supply load state index of the future independent power grid for t unit time, is a constant term, is the i-th lag autoregressive coefficient, For independent power grid The historical power supply timing characteristic value of unit time lag order, is the jth moving average coefficient, For independent power grid The random error of the moving average order per unit time, n is the total number of lag orders, and m is the total number of moving average orders.

3. A correction method based on the reliability of an independent power grid power supply system according to claim 2, characterized in that: The judgment of whether the power supply load state of the predicted future independent power grid is within the actual power supply load demand range of the independent power grid specifically includes: According to the time series, the power supply load state index of the predicted future independent power grid and the actual power supply load demand range of the independent power grid are marked, and the power supply load state index of each unit time node of the predicted future independent power grid and the actual power supply load demand range of each unit time node of the independent power grid are determined; The linear mapping is used to perform vector conversion on the power supply load state index of each unit time node of the predicted future independent power grid and the actual power supply load demand range of each unit time node of the independent power grid, so as to obtain the power supply load state vector index of each unit time node of the predicted future independent power grid and the actual power supply load vector demand range of each unit time node of the independent power grid; The Euclidean distance formula is used to calculate and predict the spatial range between the power supply load state vector index of each unit time node of the future independent power grid and the actual power supply load vector demand range of each unit time node of the independent power grid, and to determine whether the power supply load state vector index of each unit time node is within the actual power supply load vector demand range.

4. A method for correcting the reliability of an independent power grid power supply system according to claim 3, characterized in that: The specific determination of the power supply load state vector index of each unit time node being within the actual power supply load vector demand range is: ； In the formula, To predict the actual power supply load vector demand range of the future independent power grid at the t-th unit time node, To predict the power supply load state vector index of the t-th unit time node of the future independent power grid, is the lower limit of the actual power supply load vector demand range of the t-th unit time node of the independent power grid, It is the upper limit of the actual power supply load vector demand range of the t-th unit time node of the independent power grid.

5. A method for correcting the reliability of an independent power grid power supply system according to claim 4, characterized in that: If it is determined that the independent power grid is abnormal, the operation log of the independent power grid is obtained, the historical operation parameters of the components of the independent power grid are marked, the deviation distribution of the components is analyzed, and the fault probability distribution diagram of the components is generated. Specifically, the following steps are performed: Based on the service life of the independent power grid, determine the basic performance loss status of the components of the independent power grid; Initialize the standardized operation parameter array of the components of the independent power grid according to the basic performance loss status of the components of the independent power grid; Based on the operation log of the independent power grid, the historical operation parameters of the components of the independent power grid are obtained and the standardized operation parameter array of the components of the initialized independent power grid is used to perform deviation operation, and the deviation parameters of the components of the independent power grid are marked and recorded as abnormal characteristic data of the components of the independent power grid; The abnormal characteristic data of the components of the independent power grid are taken as positive samples, and the standardized operating parameter arrays of the components of the initialized independent power grid are taken as negative samples, so as to establish the original data set of the components of the independent power grid; Based on Logistic regression, a failure probability prediction model for components of an independent power grid is constructed; The original data set of the components of the independent power grid is substituted into the failure probability prediction model of the components of the independent power grid, the abnormal characteristic data of the components of the independent power grid is taken as input, and the failure probability value of the components of the independent power grid is taken as output to generate a failure probability distribution map of the components of the independent power grid.

6. A method for correcting the reliability of an independent power grid power supply system according to claim 5, characterized in that: The failure probability prediction model of the components of the independent power grid is specifically: ； In the formula, is the failure probability value of the components of the independent power grid under the given abnormal characteristic data U of the components of the independent power grid, is the kth abnormal characteristic data of the vth component of the independent power grid, is the intercept term, , , is the regression coefficient, is a logarithmic function.

7. A method for correcting the reliability of an independent power grid power supply system according to claim 6, characterized in that: Determine the risk coefficient of the components of the independent power grid by associating and matching the failure probability distribution map of the components of the independent power grid with the risk category database of the components; Based on the failure probability distribution diagram of the components of the independent power grid, the failure probability distribution value of each component of the independent power grid is standardized; Using one-hot encoding to perform vector conversion on the fault probability distribution graph of the components of the independent power grid, the fault probability distribution vector of each component of the independent power grid is obtained; Analyze the matching degree between the failure probability distribution vector of the components of each independent power grid and the risk types in the risk type database of the components, and determine the failure risk type matching index of the components of each independent power grid; According to the fault risk type matching index of each component of the independent power grid, weights are assigned to the fault risk types of the components of the independent power grid; The risk coefficients of the components of the independent power grid are calculated according to the failure probability values ​​of the components of the independent power grid and the failure risk type weights of the components of the independent power grid.

8. The method for correcting the reliability of an independent power grid power supply system according to claim 7, characterized in that: Determine the fault risk type matching index of each independent power grid component, including: ； In the formula, is the z-th risk type matching index of the v-th component failure of the independent power grid, is the failure probability distribution vector of the vth component of the independent power grid, is the zth risk category of the vth component in the risk category database; The risk factors of the components of the independent power grid specifically include: ； In the formula, is the risk factor of the vth component of the independent power grid, is the failure probability value of the vth component of the independent power grid, is the zth risk type weight of the vth component failure of the independent power grid.

9. A correction system based on the reliability of an independent power grid power supply system, characterized in that: A method for correcting the reliability of an independent power grid power supply system according to any one of claims 1 to 8, comprising: The load forecasting module analyzes the power supply trend of the historical power supply data based on the independent power grid and predicts the power supply load status of the independent power grid in the future; The abnormality judgment module is electrically connected to the load prediction module. The abnormality judgment module is used to judge whether the power supply load state of the predicted future independent power grid is within the actual power supply load demand range of the independent power grid. If so, it is determined that the independent power grid has no abnormality. If not, it is determined that the independent power grid has an abnormality. A fault probability map module, the fault probability map module is electrically connected to the abnormality judgment module, and the fault probability map module is used to obtain the operation log of the independent power grid, mark the historical operation parameters of the components of the independent power grid, analyze the deviation distribution of the components, and generate a fault probability distribution map of the components if it is determined that the independent power grid has an abnormality; Risk database, based on the known risk types of power grid components, establish a risk category database of component components; A risk coefficient module, the risk coefficient module is electrically connected to the risk database and the fault probability map module, and the risk coefficient module is used to determine the risk coefficient of the component of the independent power grid by associating and matching the fault probability distribution map of the component of the independent power grid with the risk category database of the component; A reliability verification module, the reliability verification module is electrically connected to the risk coefficient module, and the reliability verification module is used to correct the power supply load state of the predicted future independent power grid according to the risk coefficient of the components of the independent power grid, perform reliability verification according to the simulation function SIM, and generate a reliability score of the independent power grid; A maintenance task module is electrically connected to the reliability verification module, and is used to generate maintenance tasks for the independent power grid power supply system according to the stability level corresponding to the reliability score of the independent power grid.

Images