Risk scenario identification method and system for power grid cascading failure, device, and medium

Abstract

A risk scenario identification method and system for a power grid cascading failure, a device, and a medium. The method comprises: acquiring power grid historical operation data (S101); performing cascading failure simulation on the power grid historical operation data, so as to obtain an active power flow entropy (S102); visualizing the power grid historical operation data and the active power flow entropy, so as to obtain a multi-row heat map (S103); dividing the multi-row heat map into a training set and a test set (S104); training a neural network by using the training set, so as to obtain a power grid cascading failure risk scenario identification model (S105); and testing the power grid cascading failure risk scenario identification model by using the test set, so as to obtain a risk scenario identification result (S106).

Description

Power grid cascading failure risk scenario identification method, system, equipment and medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on July 28, 2023, with application number 202310939393.1. The entire contents of the above application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of power risk scenarios, for example, to a method, system, equipment and medium for identifying power grid cascading failure risk scenarios. Background Art

[0003] The application of flexible AC / DC transmission technology and the integration of new loads such as electric vehicles in current power systems have led to increasingly complex system structures. This has led to the possibility that some low-probability events in the power grid could trigger cascading failures, resulting in serious consequences. To improve the reliability and stability of the power grid, avoid chain reactions and widespread blackouts, reduce repair costs and time, and safeguard public safety, timely prediction and identification of potential risks can enable appropriate measures to mitigate cascading failures and ensure the normal operation of the power system. Therefore, accurately identifying the current operating status of the power grid is crucial to ensuring its normal operation.

[0004] With the rise of a new generation of artificial intelligence technologies represented by deep learning technology, new ideas have emerged for online identification of power grid cascading failure risk scenarios. Using deep learning technology to explore the intrinsic connection between historical operating conditions and risk scenarios and establish an identification model can improve the online identification speed of power grid cascading failure risk scenarios, which is crucial in practical applications.

[0005] Summary of the Invention

[0006] The present application provides a method, system, device and medium for identifying power grid cascading failure risk scenarios, which can accurately realize risk identification of the current power grid operation scenario.

[0007] This application provides a method for identifying power grid cascading failure risk scenarios, including:

[0008] Obtain historical power grid operation data;

[0009] Performing a cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy;

[0010] Graphically representing the historical operation data of the power grid and the active power flow entropy to obtain a plurality of rows of thermal diagrams;

[0011] Dividing the plurality of rows of heat maps into a training set and a test set;

[0012] The neural network is trained using the training set to obtain a grid cascading failure risk scenario identification model;

[0013] The power grid cascading failure risk scenario identification model is tested using a test set to obtain risk scenario identification results.

[0014] Optionally, performing a cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy includes:

[0015] Building a power grid simulation model based on the historical power grid operation data, setting multiple system operation scenarios, and performing cascading failure simulations on the lines in the power grid to obtain cascading failure chains and cascading failure load rates;

[0016] Active power flow entropy is determined according to the cascading failure chain and the cascading failure load rate.

[0017] Optionally, the historical power grid operation data includes a power grid cascading failure risk scenario; the historical power grid operation data and the active power flow entropy are graphically represented to obtain a multi-row heat map, including:

[0018] Using the load rate of the power grid cascading failure risk scenario under the current working conditions, draw the first row of a multi-row heat map corresponding to the power grid cascading failure risk scenario;

[0019] Using the line capacity of the power grid cascading failure risk scenario, drawing a second row of a multi-row heat map corresponding to the power grid cascading failure risk scenario;

[0020] Using the node feature vector of the power grid cascading failure risk scenario, drawing the third row of the multi-row heat map corresponding to the power grid cascading failure risk scenario;

[0021] Using the active power flow entropy, drawing a fourth row of a multi-row heat map corresponding to the power grid cascading failure risk scenario;

[0022] The fifth row of the multi-row heat map corresponding to the power grid cascading failure risk scenario is drawn using the proportion of historical cascading failures of the power grid cascading failure risk scenario to total failures.

[0023] Optionally, a neural network is trained using the training set to obtain a grid cascading failure risk scenario identification model, specifically including:

[0024] The multi-row heat maps of the power grid under various operating conditions in the training set are used as the input of the Convolutional Neural Network (CNN) model, and the risk scenarios corresponding to each operating condition in the training set are used as the output of the CNN model. The cross-entropy loss function is used as the loss function to train the CNN model to obtain a power grid cascading failure risk scenario identification model.

[0025] Optionally, after testing the power grid cascading failure risk scenario identification model using a test set to obtain a risk scenario identification result, the method further includes:

[0026] A clustering algorithm is used to construct a significant risk scenario set based on the risk scenario identification results.

[0027] Optionally, the active power flow entropy includes the active power flow entropy of the overloaded line and the active power flow entropy of the non-overloaded line;

[0028] The expression of the active power flow entropy of the non-overloaded line is:

[0029] Among them, H is the active power flow entropy, R is a constant, Z k is the load rate of the kth branch, Z k+1 is the load rate of the k+1th branch, n is the total number of branches, k is the kth branch, P k The load rate is (Z k , Z k+1 The ratio of the number of branches of ] to the total number of branches;

[0030] The expression of the active power flow entropy of the overloaded line is:

[0031] Among them, H m is the active power flow entropy of the overload line m, h is the basic entropy value of the overload line, (Z w , Z w+1 ] is the load rate interval of line m; n w is the number of branches located in the load rate interval of line m, C m is the capacity of line m; C max is the maximum capacity of the system line, F max The maximum allowable load rate of the line.

[0032] This application also provides a power grid cascading failure risk scenario identification system, including:

[0033] An acquisition module is configured to acquire historical operation data of the power grid;

[0034] a cascading failure simulation module configured to perform cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy;

[0035] A graphical representation module is configured to graphically represent the historical operation data of the power grid and the active power flow entropy to obtain a plurality of rows of thermal diagrams;

[0036] A partitioning module configured to partition the plurality of rows of heat maps into a training set and a test set;

[0037] A training module is configured to train a neural network using a training set to obtain a power grid cascading failure risk scenario identification model;

[0038] The testing module is configured to test the power grid cascading failure risk scenario identification model using a test set to obtain a risk scenario identification result.

[0039] Optionally, the cascading failure simulation module includes:

[0040] a construction and simulation unit configured to construct a power grid simulation model based on the historical operation data of the power grid, set multiple system operation scenarios, simulate cascading failures on lines in the power grid, and obtain cascading failure chains and cascading failure load rates;

[0041] The active power flow entropy determining unit is configured to determine the active power flow entropy according to the cascading failure chain and the cascading failure load rate.

[0042] The present application also provides an electronic device, comprising:

[0043] at least one processor;

[0044] a storage device having at least one program stored thereon;

[0045] When the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method described above.

[0046] The present application also provides a computer storage medium having a computer program stored thereon, wherein the computer program implements the method described above when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The following is an introduction to the drawings required for use in the embodiments.

[0048] FIG1 is a schematic diagram of a method for identifying power grid cascading failure risk scenarios provided by an embodiment of the present application;

[0049] FIG2 is a multi-row heat map corresponding to each power grid cascading failure risk scenario provided in an embodiment of the present application;

[0050] FIG3 is a structural diagram of a power grid cascading failure risk scenario identification model provided in an embodiment of the present application;

[0051] FIG4 is a flow chart of a method for identifying power grid cascading failure risk scenarios according to an embodiment of the present application;

[0052] FIG5 is a structural block diagram of a system for identifying power grid cascading failure risk scenarios according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. The described embodiments are part of the embodiments of this application, but not necessarily all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The present application provides a method, system, device and medium for identifying power grid cascading failure risk scenarios, which can accurately realize risk identification of the current power grid operation scenario.

[0055] As shown in Figures 1 and 4, the present application provides a method for identifying power grid cascading failure risk scenarios, including:

[0056] Step 101: Obtain historical power grid operation data.

[0057] Step 102: Perform cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy.

[0058] Step 102 includes: constructing a power grid simulation model based on the historical power grid operation data, setting different system operation scenarios, simulating cascading failures on the lines in the power grid, and obtaining a cascading failure chain and a cascading failure load rate; and determining active power flow entropy based on the cascading failure chain and the cascading failure load rate.

[0059] Historical power grid operation data is collected to conduct cascading failure simulations, and active power flow entropy is defined as a scenario identification indicator for power grid cascading failures. A power grid simulation model is built, different system operation scenarios are set, and N-1 cascading failure simulations are conducted on the line considering line overload-dominated cascading failures to obtain the cascading failure chain and cascading failure load loss rate.

[0060] Entropy indicates the uniformity of the spatial distribution of objects and is used to characterize the uncertainty of a system. The flow entropy is a measure of the balance of the flow of multiple branches in the system, which is quantified by the distribution of branch load rates. Suppose the load rate arithmetic sequence Z = [Z1, Z2, ..., Z n ], the system power flow entropy F is:

[0061] Where: P kThe load rate is (Z k , Z k+1 ] is the ratio of the number of branches to the total number of branches; R is a constant, taken as ln10.

[0062] The number of branches is the number of lines in the power grid.

[0063] To more specifically reflect the impact of system indicators on the power grid, the active power flow entropy is set based on the active power characteristics of the power flow. Major power outages have shown that small disturbances can cause high-load lines to overload and disconnect, leading to cascading failures. In addition, the line load factor is positively correlated with its outage probability. Therefore, when the load factor of all lines is less than 1, the active power flow entropy of the system's unloaded lines is:

[0064] H is the active power flow entropy, R is a constant, Z k is the load rate of the kth branch, Z k+1 is the load rate of the k+1th branch, n is the total number of branches, k is the kth branch, P k The load rate is (Z k , Z k+1 ] is the ratio of the number of non-overloaded branches to the total number of branches. Formula (2) shows that when the line is not overloaded, the higher the load rate of the line, the greater its contribution to the overall active power flow entropy of the system; at the same time, the higher the proportion of overloaded lines, the greater the active power flow entropy of the system, and the higher the risk of cascading failures. When the line is overloaded, the overheating of the line will cause its disconnection probability to increase significantly with the increase of the overload degree, and the disconnection of high-capacity lines will significantly increase the probability of a major power outage. In order to highlight the importance of different lines in the power grid, the active power flow entropy H of the overloaded line m is set m for:

[0065] Where: h is the basic entropy value of the overloaded line, which is ln0.005, that is, the maximum entropy value corresponding to the line when it is not overloaded; C m is the capacity of line m; C max is the maximum capacity of the system line; (Z w , Z w+1 ] is the load rate interval of line m; n w For the load rate range (Z w , Z w+1 ] the number of branches; P w The load rate is (Z w , Z w+1 ] is the ratio of the number of overloaded branches to the total number of branches; maxis the maximum allowable load rate of the line. If the load rate exceeds this value, the line will be disconnected. In this application, the value is 1.4. From formula (3), it can be seen that for different overloaded lines in the same load rate range, the high-capacity line contributes more to the overall active power flow entropy of the system.

[0066] Step 103: Graphically represent the historical grid operation data and the active power flow entropy to obtain multiple rows of heat maps, wherein the historical grid operation data includes grid cascading failure risk scenarios.

[0067] Step 103 includes:

[0068] The first row of a multi-row heat map corresponding to the power grid cascading failure risk scenario is drawn using the load rate of the power grid cascading failure risk scenario under the current working condition in the power grid historical operation data.

[0069] The line capacity of the power grid cascading failure risk scenario in the historical power grid operation data is used to draw a second row of the multi-row heat map corresponding to the power grid cascading failure risk scenario.

[0070] The node feature vector of the power grid cascading failure risk scenario in the power grid historical operation data is used to draw the third row of the multi-row heat map corresponding to the power grid cascading failure risk scenario.

[0071] The active power flow entropy is used to draw the fourth row of the multi-row heat map corresponding to the power grid cascading failure risk scenario.

[0072] The fifth row of the multi-row heat map corresponding to the power grid cascading failure risk scenario is drawn using the proportion of historical cascading failures of the power grid cascading failure risk scenario to total failures in the power grid historical operation data.

[0073] Each grid cascading failure risk scenario is graphically represented, and a CNN-based grid cascading failure risk scenario identification model is constructed, including: using multiple rows of heat maps to graphically represent each grid cascading failure risk scenario, generating multiple rows of heat maps corresponding to each grid cascading failure risk scenario; inputting the multiple rows of heat maps corresponding to each grid cascading failure risk scenario into a pre-trained machine learning model, and obtaining the classification results output by the machine learning model for each grid cascading failure risk scenario. As shown in Figure 3, the basic structure of a CNN consists of an input layer, a convolutional layer, a pooling layer (also called a sampling layer), a fully connected layer, and an output layer.

[0074] The CNN-based power grid cascading failure risk scenario identification model consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The input layer inputs multiple rows of heat map data.

[0075] Referring to FIG2 , for each of the grid cascading failure risk scenarios, the load rate of the grid cascading failure risk scenario under the current working conditions is used to draw the first row of the multi-row heat map corresponding to the grid cascading failure risk scenario, and the load rate is represented by Z k .

[0076] Using the line capacity of the power grid cascading failure risk scenario, draw the second row of the multi-row heat map corresponding to the power grid cascading failure risk scenario. The line capacity is expressed as C m .

[0077] Using the node feature vectors in the power grid cascading failure risk scenario, draw the third row of the multi-row heat map corresponding to the power grid cascading failure risk scenario; in this example, the node feature vector is represented by X Input .

[0078] Using the active power flow entropy, the fourth row of the multi-row heat map corresponding to the grid cascading failure risk scenario is drawn. The active power flow entropy is expressed as H and H m .

[0079] Using the proportion of historical cascading failures to total failures in the power grid cascading failure risk scenario, the fifth row of the multi-row heat map corresponding to the power grid cascading failure risk scenario is drawn. The proportion of historical cascading failures to total failures is expressed as Q m , thus generating multiple rows of heat maps corresponding to the power grid cascading failure risk scenario.

[0080] Step 104: Divide the multiple rows of heat maps into a training set and a test set.

[0081] Step 105: Use the training set to train the neural network to obtain a power grid cascading failure risk scenario identification model.

[0082] Step 105 includes: using multiple rows of heat maps under different power grid operating conditions in the training set as input to the CNN model, using the power grid cascading failure risk scenarios corresponding to the operating conditions in the training set as output of the CNN model, and using the cross-entropy loss function as the loss function to train the CNN model to obtain a power grid cascading failure risk scenario identification model.

[0083] A CNN-based power grid cascading failure risk scenario identification model is constructed. Multiple rows of heat map samples under different power grid operating conditions are used as model input. The model output is the power grid cascading failure risk scenario under the operating condition. The output can be expressed as: R Output ={a1,a2,...,a i ,...,a n}

[0084] Where R Outputrepresents the output identification result of the power grid cascading failure risk scenario, a i The value of indicates whether the working condition is a risk scenario, if yes, it is 1, otherwise it is 0.

[0085] The loss function F in the model loss The cross entropy loss function is used, and its formula is:

[0086] Among them, a i is the grid operation status category, is the probability value of whether the grid operation status is a risk scenario output by the Softmax function. N is the total number of training sets, and i is the i-th sample.

[0087] The constructed CNN-based power grid cascading failure risk scenario identification model can analyze the uncertain cascading failures that may occur in the power grid under different operating conditions, and output whether the operating state is a risk scenario.

[0088] Step 106: Using the test set, the power grid cascading failure risk scenario identification model is tested to obtain a risk scenario identification result.

[0089] In practical applications, after the power grid cascading failure risk scenario identification model is tested using a test set to obtain risk scenario identification results, the method further includes: constructing a significant risk scenario set using a clustering algorithm based on the risk scenario identification results.

[0090] The multi-row heat map is divided into a training set and a test set, and then fed into the identification model for offline training. A clustering method is used to construct a set of significant risk scenarios, and the corresponding power grid cascading failure risk scenarios are output. The multi-row heat map is divided into a training set and a test set, and the samples are fed into a CNN-based power grid cascading failure risk scenario identification model. The CNN model learns the features of the multi-row heat map, and a clustering method is used to construct a set of significant risk scenarios. The model is trained and outputs whether each operating state is a cascading failure risk scenario. The model's identification ability is then tested using the test set. The model is tested using the test set, and when fed with a multi-row heat map for a given operating state, it can identify whether the scenario corresponding to that operating state is a risk scenario and output a 0 or 1, with 0 representing no and 1 representing yes.

[0091] The present application obtains historical operation data of the power grid; performs cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy; graphically represents the historical operation data of the power grid and the active power flow entropy to obtain multiple rows of heat maps; divides the multiple rows of heat maps into a training set and a test set; uses the training set to train a neural network to obtain a power grid cascading failure risk scenario identification model; uses the test set to test the power grid cascading failure risk scenario identification model to obtain risk scenario identification results. When applied online, the active power flow entropy is directly used as the input of the power grid cascading failure risk scenario identification model to obtain risk scenario identification results, thereby accurately realizing risk identification of the current operation scenario of the power grid.

[0092] The present application also provides a system for identifying power grid cascading failure risk scenarios, as shown in FIG5 , comprising:

[0093] The acquisition module 10 is configured to acquire historical operation data of the power grid.

[0094] The cascading failure simulation module 20 is configured to perform cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy.

[0095] The graphical representation module 30 is configured to graphically represent the historical operation data of the power grid and the active power flow entropy to obtain a plurality of rows of thermal diagrams.

[0096] The division module 40 is configured to divide the multiple rows of heat maps into a training set and a test set.

[0097] The training module 50 is configured to train the neural network using the training set to obtain a power grid cascading failure risk scenario identification model.

[0098] The testing module 60 is configured to test the power grid cascading failure risk scenario identification model using a test set to obtain a risk scenario identification result.

[0099] As an optional implementation, the cascading failure simulation module 20 includes:

[0100] The construction and simulation unit is configured to construct a power grid simulation model based on the historical operation data of the power grid, set different system operation scenarios, simulate cascading failures on the lines in the power grid, and obtain the cascading failure chain and the cascading failure load rate.

[0101] The active power flow entropy determining unit is configured to determine the active power flow entropy according to the cascading failure chain and the cascading failure load rate.

[0102] The present application also provides an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the embodiments of the present application.

[0103] The present application also provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the embodiments of the present application.

[0104] This application takes into account the active power flow of the line, defines the active power flow entropy that reflects the risk of cascading failures in the current scenario of the system, and uses the active power flow entropy of the line as an important indicator for identifying the risk scenario of cascading failures in the power grid. The deep learning model is used to mine the correspondence between the active power flow entropy of the line and the risk scenario of cascading failures in the power grid. Combined with the power grid load rate, line capacity, and line nodes, a multi-row heat map corresponding to the risk scenario of cascading failures in the power grid is drawn; based on the multi-row heat map, a power grid cascading failure risk scenario identification model based on a convolutional neural network (CNN) is constructed. The neural network in the model is used to autonomously learn the mapping relationship between the multi-row heat map and the risk scenario, and the Softmax classifier is used to output the risk scenario identification result. This method first learns the correspondence between the active power flow entropy of the historical line and the risk scenario of cascading failures in the power grid through an offline training model to obtain a set of significant risk scenarios. When applied online, the active power flow entropy is directly input into the model to realize the risk identification of the current operation scenario of the power grid.

Claims

1. A method for identifying power grid cascading failure risk scenarios, comprising: Obtain historical power grid operation data; Performing a cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy; Graphically representing the historical operation data of the power grid and the active power flow entropy to obtain a plurality of lines of thermal diagrams; Dividing the multiple rows of heat maps into a training set and a test set; The neural network is trained using the training set to obtain a grid cascading failure risk scenario identification model; The power grid cascading failure risk scenario identification model is tested using a test set to obtain risk scenario identification results.

2. The method according to claim 1, wherein: Performing a cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy includes: Building a power grid simulation model based on the historical operation data of the power grid, setting a variety of system operation scenarios, simulating cascading failures on the lines in the power grid, and obtaining cascading failure chains and cascading failure load rates; Active power flow entropy is determined according to the cascading fault chain and the cascading fault load rate.

3. The method according to claim 1, wherein: The historical operation data of the power grid includes risk scenarios of power grid cascading failures; The historical operation data of the power grid and the active power flow entropy are graphically represented to obtain a plurality of lines of thermal diagrams, including: Using the load rate of the power grid cascading failure risk scenario under the current working condition, draw the first row of a multi-row heat map corresponding to the power grid cascading failure risk scenario; Using the line capacity of the power grid cascading failure risk scenario, drawing a second row of the multi-row heat map corresponding to the power grid cascading failure risk scenario; Using the node feature vector of the power grid cascading failure risk scenario, drawing the third row of the multi-row heat map corresponding to the power grid cascading failure risk scenario; Using the active power flow entropy, drawing a fourth row of a multi-row heat map corresponding to the power grid cascading failure risk scenario; The fifth row of the multi-row heat map corresponding to the power grid cascading failure risk scenario is drawn by using the proportion of historical cascading failures in the power grid cascading failure risk scenario to total failures.

4. The method according to claim 1, wherein: The neural network is trained using the training set to obtain a grid cascading failure risk scenario identification model, including: The multi-row heat maps of the power grid under various operating conditions in the training set are used as the input of the convolutional neural network (CNN) model, and the risk scenarios corresponding to each operating condition in the training set are used as the output of the CNN model. The cross entropy loss function is used as the loss function to train the CNN model to obtain a power grid cascading failure risk scenario identification model.

5. The method according to claim 1, after testing the power grid cascading failure risk scenario identification model using a test set to obtain a risk scenario identification result, further comprises: A clustering algorithm is used to construct a significant risk scenario set based on the risk scenario identification results.

6. The method according to claim 1, wherein: The active power flow entropy includes the active power flow entropy of the overloaded line and the active power flow entropy of the non-overloaded line; The expression of the active power flow entropy of the non-overloaded line is: Among them, H is the active power flow entropy, R is a constant, and Z k is the load rate of the kth branch, Z k61 is the load rate of the k+1th branch, n is the total number of branches, k is the kth branch, P k The load rate is located at (Z k , Z k+1 ] is the ratio of the number of non-overloaded branches to the total number of branches; The expression of the active power flow entropy of the overloaded line is: Among them, H m is the active power flow entropy of the overloaded line m, h is the basic entropy value of the overloaded line, (Z w , Z w+1 ] is the load rate interval of line m; n w is the number of branches located in the load rate interval of line m, C m is the capacity of line m; C max is the maximum capacity of the system line, F max is the maximum allowable load rate of the line, P w The load rate is located at (Z w , Z w+1 ] is the ratio of the number of overloaded branches to the total number of branches.

7. A power grid cascading failure risk scenario identification system, comprising: An acquisition module is configured to acquire historical operation data of the power grid; A cascading failure simulation module, configured to perform cascading failure simulation on the historical operation data of the power grid to obtain active power flow entropy; A graphical representation module, configured to graphically represent the historical operation data of the power grid and the active power flow entropy to obtain a plurality of lines of thermal diagrams; A partitioning module, configured to partition the plurality of rows of heat maps into a training set and a test set; A training module, configured to train the neural network using the training set to obtain a power grid cascading failure risk scenario identification model; The test module is configured to test the power grid cascading failure risk scenario identification model using a test set to obtain a risk scenario identification result.

8. The system according to claim 7, wherein: The cascading failure simulation module includes: A construction and simulation unit, configured to construct a power grid simulation model according to the historical operation data of the power grid, set a variety of system operation scenarios, simulate cascading failures on the lines in the power grid, and obtain cascading failure chains and cascading failure load rates; The active power flow entropy determination unit is configured to determine the active power flow entropy according to the cascading failure chain and the cascading failure load rate.

9. An electronic device, comprising: at least one processor; a storage device having at least one program stored thereon; When the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method according to any one of claims 1 to 6.

10. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 6.

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