A method for identifying self-organized critical state of power grid containing high proportion of wind power generation

Abstract

The present application belongs to the technical field of electric power, and discloses a power grid self-organizing critical state identification method containing high proportion of wind power generation. The method considers the influence of wind power generation on the power grid self-organizing critical state, through multi-index collaborative analysis, so that the index weight can be dynamically adjusted according to the real-time working conditions such as power grid topological structure change, load fluctuation, wind power output, and a comprehensive judgment index which can more comprehensively reflect the power grid self-organizing critical state is proposed, and the reliability of the critical state identification is improved. Compared with the traditional fixed weight or neural network method which needs training and learning and the method which depends on historical data, the present application has better environmental adaptability and scene generalization ability, and can be applied to power grid systems of different scales and different structures without relying on historical data. Compared with the existing probabilistic evaluation method, the deterministic index system proposed by the present application can directly output quantitative evaluation results, providing clear operation basis for dispatching decision, and effectively reducing the risk of misjudgment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric power and relates to a self-organizing critical state identification method for a power grid containing high-proportion wind power generation. BACKGROUND

[0002] With the rapid development of renewable energy, wind power generation has become an important source of electric power. However, after high-proportion wind power generation is connected to a power grid, new challenges are brought to the stability and safety of the power grid.

[0003] Due to the intermittent and uncertain characteristics of wind power generation, power fluctuations and voltage instability may occur in the power grid during operation, which may lead the power grid to enter a self-organizing critical state and further cause power outage accidents.

[0004] Related personnel have conducted various researches on identifying the self-organizing critical state of the power grid, and these researches have the following defects:

[0005] On the one hand, the existing methods only care about the probability of the power grid being in a self-organizing critical state or the evolution law of the self-organizing critical state of the power grid, and lack identification criteria for whether the currently running power grid is in a self-organizing critical state.

[0006] On the other hand, the influence of wind power generation characteristics on the critical state of the power grid is not fully considered or only the wind power generation characteristics are considered without comprehensively considering other factors of power grid operation, so it is difficult to achieve accurate critical state evaluation and prediction. SUMMARY

[0007] The purpose of the application is to provide a self-organizing critical state identification method for a power grid containing high-proportion wind power generation, which comprehensively considers the characteristics of wind power generation to improve the identification ability of the self-organizing critical state of the power grid under the condition of high-proportion wind power generation connection, so as to accurately judge whether the power grid is in a self-organizing critical state, and the method has high reliability and good adaptability.

[0008] In order to achieve the above purpose, the application adopts the following technical scheme:

[0009] A self-organizing critical state identification method for a power grid containing high-proportion wind power generation, comprising the following steps:

[0010] Step 1. According to the wind speed value of the region where the wind farm is located in the power grid, the output power of the wind power farm is calculated;

[0011] Step 2. Six indexes of wind power output proportion, wind power penetration rate, power grid voltage deviation rate, power grid line load rate, node degree and node betweenness are defined, and the calculation formulas of the corresponding indexes are given;

[0012] Step 3. Real-time acquisition of network parameters of the power grid containing a high proportion of wind power generation, setting the output power of the wind farm in the power grid according to the output power of the wind farm, and calculating the current power grid wind power output proportion index according to step 2;

[0013] Simulate under the current wind power output proportion of the power grid, add random load disturbance to the power grid until a power outage accident occurs, and when the power outage accident occurs, it indicates that the power grid evolves to a self-organized critical state;

[0014] Perform power flow calculation using the network parameters of the power grid each time a power outage accident occurs, and obtain the parameters required for calculating each index according to the power flow calculation results, and then calculate the index values according to the index calculation formula;

[0015] Step 4. According to the index values of the power grid each time a power outage accident occurs, use the entropy weight method to assign weights to the indexes, and perform weighted calculation according to the weight assignment results and the index values to obtain the comprehensive index value of the power grid each time a power outage accident occurs. Calculate the average value of the comprehensive index value under multiple power outage accidents, and take the average value as the power grid self-organized critical state judgment index;

[0016] Step 5. Perform power flow calculation using the network parameters of the power grid at the current running time, obtain the required parameters for calculating the index values from the power flow calculation results, and perform weighted calculation on the indexes according to step 4 to obtain the comprehensive index value of the power grid at the current running time. Compare it with the power grid self-organized critical state judgment index to determine whether the power grid is close to or has entered a self-organized critical state.

[0017] In addition, on the basis of the above-mentioned power grid self-organized critical state identification method, the present application also proposes a computer device, which comprises a memory and one or more processors. The memory stores executable code. When the processor executes the executable code, it is used to realize the steps of the power grid self-organized critical state identification method containing a high proportion of wind power generation.

[0018] In addition, on the basis of the above-mentioned power grid self-organized critical state identification method, the present application also proposes a computer readable storage medium, which stores a program. When the processor executes the program, it is used to realize the steps of the power grid self-organized critical state identification method containing a high proportion of wind power generation.

[0019] The present application has the following advantages:

[0020] As described above, the present application proposes a power grid self-organized critical state recognition method containing a high proportion of wind power generation, considers the influence of wind power generation on the self-organized critical state of the power grid, and can accurately recognize the self-organized critical state of the power grid containing a high proportion of wind power generation by proposing a self-organized critical state comprehensive judgment index. Through multi-index collaborative analysis, the index weight can be dynamically adjusted according to the real-time working conditions such as the change of the power grid topology structure, the load fluctuation, and the wind power output, thereby improving the reliability of the recognition. Compared with the traditional fixed weight or neural network method which needs to be trained and learned and the method which depends on historical data, the method of the present application has better adaptability and scene generalization ability, and can be applied to power grid systems with different wind power outputs and different topology structures without relying on historical data. When the wind power output of the power grid increases or decreases or the topology structure changes, the calculated wind power output proportion, wind power penetration, node degree, and node betweenness index will increase or decrease. Especially for the power grid lacking historical operation data or data loss, only the existing real-time operation data of the power grid is needed to recognize the self-organized critical state through the simulation method. Compared with the existing probabilistic evaluation method, the deterministic index system proposed by the present application can directly output the quantitative evaluation results, provide clear operation basis for dispatching decision, effectively reduce the risk of misjudgment, and also can provide reference for the planning of the power grid wind farm. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flow chart of the power grid self-organized critical state recognition method containing a high proportion of wind power generation in the embodiment of the present application.

[0022] Figure 2 The simulation diagram of the power grid self-organized critical state recognition method containing a high proportion of wind power generation in the embodiment of the present application.

[0023] Figure 3 The IEEE39 node power grid diagram in the embodiment of the present application.

[0024] Figure 4 The wind power output power statistical diagram with a time scale of 500 days in the embodiment of the present application.

[0025] Figure 5 The statistical diagram of the load loss of 500 power outage accidents (32, 33 wind farms) in the embodiment of the present application.

[0026] Figure 6 The scale-frequency double logarithmic coordinate diagram based on the load loss statistical data in the embodiment of the present application. DETAILED DESCRIPTION

[0027] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0028] Embodiment 1

[0029] The embodiment 1 describes a self-organizing critical state recognition method of power grid containing high proportion of wind power generation, as shown in Figure 1 The self-organizing critical state recognition method of power grid containing high proportion of wind power generation comprises the following steps:

[0030] Step 1. According to the wind speed value of the region where the wind farm in the power grid is located, the output power of the wind power farm is calculated.

[0031] The functional relationship between the output power of the wind turbine and the wind speed is:

[0032] .

[0033] In the formula, is the actual output power of the wind turbine of the th wind farm; is the wind speed of the region where the th wind farm is located; is the cut-in wind speed of the wind turbine of the th wind farm; is the cut-out wind speed of the wind turbine of the th wind farm; is the rated wind speed of the wind turbine of the th wind farm; is the rated output power of the wind turbine of the th wind farm.

[0034] The output power of the wind power farm is represented as:

[0035] .

[0036] In the formula, is the output power of the th wind farm; is the number of wind turbines of the th wind farm.

[0037] Step 2. Define six indexes of wind power output ratio, wind power penetration rate, power grid voltage deviation rate, power grid line load rate, node degree and node betweenness, and give the calculation formula of the corresponding indexes.

[0038] Among them, the output ratio and the wind power penetration rate are two wind power generation indexes, the voltage deviation rate and the line load rate are two power grid operation safety indexes, and the node degree and the node betweenness are two power grid topology structure indexes.

[0039] The application starts from three aspects of wind power generation, power grid operation safety and power grid topology, through multi-index collaborative analysis, the index weight can be dynamically adjusted according to the real-time working conditions such as power grid topology change, load fluctuation and wind power output, the comprehensive characteristics of the self-organizing critical state of the power grid can be more comprehensively reflected, and the reliability of identification is improved.

[0040] The calculation formula of the wind power output proportion p index is as follows:

[0041] .

[0042] In the formula, the sum of output powers of all wind power plants is indicated; the sum of output powers of all power plants in the power grid including the wind power plants is indicated.

[0043] The calculation formula of the wind power penetration rate a index is as follows:

[0044] .

[0045] In the formula, the sum of output powers of all wind power plants in unit time is indicated; the sum of power loads in the whole network in the same period is indicated.

[0046] The calculation formula of the power grid voltage deviation rate index is as follows:

[0047] , .

[0048] In the formula, the actual voltage of node is indicated; the rated voltage of node is indicated; the total number of nodes in the power grid is indicated; the voltage deviation rate of node is indicated.

[0049] The calculation formula of the power grid line load rate index is as follows:

[0050] , .

[0051] In the formula, the load power borne by the line is indicated; the maximum load power that can be borne by the line is indicated; the load rate of the line is indicated. The number of lines representing normal operation of the power grid.

[0052] The calculation formula of the node degree index is as follows:

[0053] , .

[0054] In the formula, is a node adjacent to node ; is the total number of nodes adjacent to node ; is the number of connecting edges between node and ; is the total number of nodes of the power grid; is the average degree of the power grid.

[0055] The calculation formula of the node betweenness index is as follows:

[0056] .

[0057] In the formula, is the number of all shortest paths between node and node ; is the number of shortest paths passing through node ; represents the betweenness of node ; is the total number of nodes of the power grid; is the average betweenness of the power grid.

[0058] Step 3. Real-time acquisition of network parameters of the power grid with a high proportion of wind power generation, setting the output power of the wind farm in the power grid according to the output power of the wind farm, calculating the current wind power output proportion index of the power grid according to step 2. Under the current wind power output proportion of the power grid, add random load disturbance to the power grid until a power outage accident occurs, which indicates that the power grid has experienced a large number of small disturbances in a long period of time, and these disturbances cumulatively eventually lead the system to evolve to a self-organized critical state.

[0059] The network parameters at each power outage accident of the power grid are used for power flow calculation, and the parameters required for calculating each index are obtained according to the power flow calculation results, and then each index value is calculated according to the calculation formula of the index.

[0060] Step 3.1. Set the output power of the current wind farm according to the output power of the wind farm, calculate the wind power output proportion index of the power grid according to step 2; set the total number of power outage accidents , and initialize the current number of power outage accidents .

[0061] In order to avoid errors and accidental factors from leading to inaccurate index calculation, For example, it can be 100 or more, but not too large, for example, the maximum value of N can be set to 800, of course, 800 is only exemplary, and does not constitute a limitation on the present application.

[0062] Step 3.2. Randomly select a node from the power grid to add a load disturbance, so that the load is increased Then calculate the power flow of the power grid; wherein, Set to .

[0063] Step 3.3. According to the calculation result of the power flow after adding the load disturbance to the power grid, judge whether the power grid has line overload.

[0064] If there is line flow overrun, disconnect the line, and the remaining lines are disconnected according to the line hidden fault model with a probability, and enter step 3.4; if there is no line overload, return to step 3.2. The line hidden fault model is a relay protection hidden fault probability model for line flow overrun, which is used to judge the operating state of the line.

[0065] Step 3.4. After disconnecting the overloaded line, judge whether the power grid has island formation or load shedding.

[0066] If so, let the current power outage accident number At this time, the network parameters of the power grid are the network parameters when the power grid enters the self-organized critical state, and the network parameters of the power grid at this time are used for power flow calculation. According to the calculation result of the power flow, the parameters required for each index except the wind power output ratio in step 2 are obtained, and then step 3.5 is entered.

[0067] Here, each index except the wind power output ratio includes two power grid operation safety and two power grid topology structure indexes. The network parameters of the power grid include the node parameters, line parameters, topology structure parameters and load parameters of the power grid.

[0068] If not, modify the network parameters of the power grid, remove the disconnected lines and failed nodes from the power grid, and return to step 3.2.

[0069] Step 3.5. Compare whether the current power outage accident number Reaches the preset total number of power outage accidents ; if so, end this process; if not, reset the power grid parameters to the network parameters when the power grid is not added with load disturbance, i.e. put the disconnected lines and failed nodes of the power grid back into operation, and reset the load parameters to the load when the power grid is not added with load disturbance, and then return to step 3.2.

[0070] Step 4. According to the index value of each power grid outage accident, the entropy weight method is used to assign weights to the index, and the comprehensive index value of each power grid outage accident is obtained by weighted calculation according to the weight assignment result and the index value.

[0071] The mean value of the comprehensive index value under multiple outage accidents is calculated, and the mean value is taken as the self-organizing critical state judgment index of the power grid.

[0072] The entropy weight method is used to calculate the weight of each index in step 2, and the process is as follows:

[0073] Step 4.1. Construct the index evaluation matrix:

[0074] .

[0075] In the formula, is the value of the first index when the power grid occurs the first outage accident, is the number of indexes, ; is the number of power grid outage accidents.

[0076] The index is normalized and the heterogeneous index is de-dimensioned to obtain the standardized index .

[0077] The calculation formula of the standardized index is as follows:

[0078] .

[0079] In the formula, is the value of the first index when the power grid occurs the first outage accident; , is the maximum and minimum value of the first index in all power grid outage accidents; indicates the normalization result, and .

[0080] Step 4.2. Calculate the index weight.

[0081] First, define the intermediate variable , and its calculation formula is as follows:

[0082] .

[0083] Then, the entropy value of the first index when the power grid occurs the first outage accident is calculated, and the entropy value Information redundancy of each index and the weight value of the first index , the formulas are as follows:

[0084] , , .

[0085] The indexes defined in step 2 are weighted and calculated according to the weight value, and the comprehensive index of the power grid in the occurrence of the first power failure accident is obtained , and the calculation formula is as follows:

[0086] .

[0087] In the formula, respectively represent the weight of each index in step 2 obtained by using the entropy weight method.

[0088] The calculation formula of the power grid self-organized critical state judgment index is as follows:

[0089] .

[0090] In the formula, represents the comprehensive index of the power grid in the occurrence of the first power failure accident; represents the total number of power failure accidents of the power grid.

[0091] Step 5. The network parameters of the current running time of the power grid are used for power flow calculation, and the required parameters are obtained from the power flow calculation results to calculate the index values. According to step 4, the comprehensive index value of the current running time of the power grid is obtained by weighted calculation of each index, and it is compared with the power grid self-organized critical state judgment index to judge whether the power grid is close to or enters the self-organized critical state.

[0092] Specifically, the comprehensive index value of the current running time of the power grid is calculated according to the weight of each index obtained in step 4 , and it is compared with the power grid self-organized critical state judgment index . If reaches , it indicates that the power grid is close to the self-organized critical state at this time; if reaches or exceeds , the power grid has entered the self-organized critical state at this time.

[0093] ​The IEEE39-bus power grid is taken as an example below to prove the effectiveness and rationality of the method of the application. In this example, the wind speed in the region where the wind farm is located is simulated using a Weibull distribution. The Weibull distribution cumulative probability density function is defined as:

[0094] .

[0095] In the formula, F(v) represents the Weibull wind frequency cumulative probability density (%); v represents the wind speed (m / s); c represents the Weibull scale parameter (m / s); and k represents the Weibull shape parameter.

[0096] Subsequently, the simulated wind speed in the region where the wind farm is located is calculated using the following formula:

[0097] .

[0098] In the formula, is the simulated wind speed in the region where the wind farm is located; is a random variable that is uniformly distributed in the interval .

[0099] First, 500 power outage accident simulations are performed on the IEEE39-bus power grid. The selected wind farms are the 32nd and 33rd nodes, and 500 days of simulated wind speed are generated using the Weibull distribution.

[0100] According to the recorded wind speed values, the function relationship between the output power of the wind turbine generator set and the wind speed is used:

[0101] .

[0102] The output power of the wind farm at different wind speeds is calculated.

[0103] Figure 4 is a wind power output model with a time scale of 500 days established using the Weibull distribution. When the wind power model is introduced into the power grid model, the wind farm is regarded as a PV node with adjustable reactive power output and determined active power output by the wind power model. The total installed capacity of the IEEE39-bus power grid is 6297.871 MW. The installed capacity of the 32nd and 33rd nodes is set as the installed capacity of the wind farm. According to the high proportion of wind power judgment method described in step 3, the calculated wind power installed capacity proportion is 20.36% at this time, which has reached the standard of high proportion of wind power generation.

[0104] After the scenario of high proportion of wind power in the power grid is set, according to the simulation flowchart shown in Figure 2 , under the condition of random output of the wind farm, 1%-2% of the load disturbance is randomly added to the power grid each time This continues until a loss of load occurs.

[0105] Load loss It can be represented as:

[0106] .

[0107] In the formula: This represents the total number of power grid nodes. It is a node The original load; It is the node after a power outage accident. The actual load. This embodiment assumes one power outage per day, accumulating 500 power outages, and calculates the load loss caused by each power outage, such as... Figure 5 As shown.

[0108] To simplify the calculation of power grid topology indicators, the power grid topology was simplified by making it unweighted and undirected, with only one connecting edge between any two nodes. The comprehensive indicator values ​​of the power grid were calculated and recorded for each power outage, and statistical analysis was performed after the simulation was completed. Table 1 only lists the indicator values ​​for the first 10 power outages.

[0109] Table 1. Values ​​of various indicators during the first ten power outages.

[0110]

[0111] Then, the comprehensive index values ​​from 500 power outages were used for weighted processing.

[0112] In this embodiment, the topological indices obtained are the degree and betweenness number of the power grid in its initial state, with their weights set to 1. The remaining four indices are weighted using the entropy weighting method. An index evaluation matrix X is formed based on the comprehensive indices from all power outage incidents.

[0113] .

[0114] The weights of the remaining four indicators are obtained using the weighting method of the present invention. Table 2 shows the weights assigned to the remaining four indicators.

[0115] Table 2 Weights assigned to the indicators

[0116]

[0117] Take the comprehensive index value of 500 power outage incidents average =1.0993.

[0118] The loss load statistics scale and loss load statistics frequency are set after the scale of each power failure accident is reordered from small to large, and the scale and frequency data of the loss load are plotted into a scale-frequency double logarithmic coordinate graph, linear regression is performed on each data point by using a least square method, and a scale-frequency double logarithmic coordinate graph obtained is as shown in Figure 6 .

[0119] When the wind power plant is No. 32 or No. 33 node, the regression fitting equation of the scale-frequency of the loss load of 500 power failure accidents is y = -9.72x + 34.23, and the correlation coefficient is 0.9482 (significant at β = 0.01), that is, the scale-frequency of the loss load of the power failure accident obeys a power law distribution, which indicates that the power grid enters a self-organized critical state at this time.

[0120] Therefore, the self-organized critical state of the power grid can be identified according to the relationship between the comprehensive index and at this time.

[0121] In addition, the present example also simulates under different conditions of the proportion of wind power generation, wherein the proportion of wind power generation is 3.97%, 10.32%, 20.356%, 24.326%, 28.42%, 40.2% and 48.27% respectively. The power grid with different proportions of wind power generation is simulated for 3 times, and 500 power failure accidents are generated each time.

[0122] The mean value of the comprehensive index value of the power grid when a power failure accident occurs is as shown in Table 3.

[0123] It can be seen from Table 3 that the mean value of the comprehensive index when the power grid enters the self-organized critical state increases with the increase of the proportion of wind power generation, thereby indicating that the method has good adaptability to the change of the proportion of wind power generation.

[0124] Table 3 Comparison of simulation results of different proportions of wind power generation

[0125]

[0126] Figure 6 The regression fitting of the scale-frequency of the loss load of the power grid at different proportions of wind power generation is given in Table 4, and the regression equation and the correlation coefficient fitted under each proportion in the first simulation are given in Table 4.

[0127] Table 4 Fitting of the loss load of the power failure accident under each proportion of wind power generation

[0128]

[0129] Table 5 is a correlation coefficient critical value table in the embodiment of the present application, as shown below:

[0130] Table 5 Correlation coefficient critical value table

[0131]

[0132] According to the correlation coefficient test table of Table 5, all the regression equations fitted are significant, and all conform to the power law distribution, that is, under these wind power generation proportions, the power grid enters a self-organized critical state after step 3 processing. The results of Table 3 can prove that the method of the present application has good adaptability to the change of the wind power generation proportion of the power grid and can adjust following the change. Therefore, only needs to judge whether the comprehensive index of the power grid at the current operation reaches or exceeds the self-organized critical state judgment index calculated according to the network parameters of the power grid at this time , that is, whether the power grid enters a self-organized critical state. In actual application, a prompt can be sent to the operator when , and a certain margin is left.

[0133] The new type of power grid self-organized critical state identification method in this embodiment can comprehensively consider the characteristics of wind power generation, improve the recognition ability of the power grid self-organized critical state under the condition of high proportion of wind power generation access, and accurately judge whether the power grid is in a self-organized critical state, which will help to monitor and warn the state of the power grid, ensure the stable operation of the power grid, reduce the risk of large-scale power outages and economic losses due to sudden events, and provide a basis for optimization decision-making.

[0134] Embodiment 2

[0135] This embodiment 2 describes a computer device including a memory and one or more processors. The memory stores executable code. When the processor executes the executable code, the steps of the power grid self-organized critical state identification method with high proportion of wind power generation in the above-mentioned embodiment 1 are implemented.

[0136] The computer device in this embodiment is any device or apparatus with data processing capability, which will not be described here.

[0137] Embodiment 3

[0138] This embodiment 3 describes a computer readable storage medium, which stores a program. When the program is executed by a processor, the steps of the power grid self-organized critical state identification method with high proportion of wind power generation in the above-mentioned embodiment 1 are implemented.

[0139] ​​​The computer readable storage medium can be an internal storage unit of any data processing capable device or apparatus, such as a hard disk or a memory, or an external storage device of any data processing capable device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc.

[0140] Of course, the above description is merely preferred embodiments of the present application, and the present application is not limited to the above-described embodiments. It should be understood that any equivalent substitutions or obvious modifications made by those skilled in the art based on the teachings of the present specification fall within the scope of the present specification, and should be protected by the present application.

Claims

1. A method for identifying self-organized critical state of power grid with high proportion of wind power generation, characterized in that, The method comprises the following steps: Step 1. According to the wind speed value of the wind farm in the wind power grid area, the output power of the wind power farm is calculated; Step 2. Define the wind power output ratio, wind power penetration rate, grid voltage deviation rate, grid line load rate, node degree and node betweenness six indexes, and give the corresponding index calculation formula; Step 3. Real-time acquisition of the network parameters of the power grid containing high proportion of wind power generation, setting the output power of the wind power farm in the power grid according to the output power of the wind power farm, calculating the current power grid wind power output ratio index according to step 2; Under the current wind power output ratio of the power grid, simulation is carried out, random load disturbance is added to the power grid until the power grid outage accident occurs, which indicates that the power grid evolves to the self-organized critical state; The network parameters of the power grid at each power outage accident are used for power flow calculation, and the parameters required for calculating each index are obtained according to the power flow calculation result, and then each index value is calculated according to the index calculation formula; Step 4. According to the index values of the power grid at each power outage accident, the entropy weight method is used to weight the indexes, and the weighted calculation is carried out according to the weighting result and the index value to obtain the comprehensive index value of the power grid at each power outage accident; The mean value of the comprehensive index value under multiple power outage accidents is calculated, and the mean value is taken as the self-organized critical state judgment index of the power grid; Step 5. The network parameters of the power grid at the current running time are used for power flow calculation, the required parameters are obtained from the power flow calculation result, the index values are calculated, the comprehensive index value of the power grid at the current running time is obtained by weighting calculation according to step 4, and it is compared with the self-organized critical state judgment index of the power grid to judge whether the power grid is close to or enters the self-organized critical state.

2. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 1, characterized in that, The step 3 is specifically: Step 3.

1. Set the output power of the current wind farm according to the output power of the wind farm, and calculate the wind power output proportion index of the power grid according to step 2; set the total number of power failure accidents as , and initialize the current number of power failure accidents ; Step 3.

2. Randomly select one node from the grid to add a load disturbance, increasing its load by Then, calculate the grid flow; where, Set to be the total load of the grid ; Step 3.

3. According to the power flow calculation result after adding load disturbance to the power grid, it is judged whether the power grid has line overload; If there is line flow overrun, disconnect the line, and the remaining lines are disconnected according to the line hidden fault model with a certain probability, and enter step 3.4; if there is no line overload, return to step 3.2; Step 3.

4. After disconnecting the overload line, it is judged whether the power grid has island formation or load shedding; If yes, let the current power failure accident times At this time, the network parameters of the power grid are the network parameters when the power grid enters the self-organized critical state. The network parameters of the power grid at this time are used for power flow calculation. According to the power flow calculation result, the parameters required for each index except the wind power output ratio in step 2 are obtained, and then step 3.5 is entered. If not, modify the network parameters of the power grid, remove the disconnected line and failed node from the power grid, and return to step 3.2; Step 3.

5. Compare and determine the current number of power outage incidents. Has the preset total number of power outage incidents been reached? If yes, end this process; otherwise, reset the grid parameters to the network parameters when no load disturbance was added to the grid, that is, put the disconnected lines and failed nodes back into operation, reset the load parameters to the load when no load disturbance was added to the grid, and then return to step 3.

2.

3. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 1, characterized in that, In step 4, the entropy weight method is used to calculate the weight of each index in step 2, and the process is as follows: Step 4.

1. Construct the index evaluation matrix: ； In the formula, is the value of the first index when the power grid has the first power outage accident, is the number of indexes, ; is the number of power grid power outage accidents; The indicators are normalized, and the heterogeneous indicators are de-dimensioned to obtain standardized indicators ; Step 4.

2. Calculate the index weight; First define an intermediate variable The formula for which is as follows: ； Then calculate the number in sequence. The first indicator occurs when the power grid experiences its first [indicator / response]. Entropy value during the power outage , No. Information redundancy of each indicator and the The weight values ​​of each indicator The formulas are as follows: ， ， 。 4. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 3, characterized in that, In step 4.1, the standardization index is calculated according to the following formula: ； wherein is the value of the i-th index at the time of the n-th power failure of the power grid; is the value of the i-th index at the time of the n-th power failure of the power grid; is the value of the i-th index at the time of the n-th power failure of the power grid; , is the maximum and minimum value of the i-th index among all power failures of the power grid; is the maximum and minimum value of the i-th index among all power failures of the power grid; denotes the normalized result, and .

5. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 3, characterized in that, In step 4, the calculation process of the comprehensive index value of the power grid at each power outage accident is as follows: The indexes defined in step 2 are weighted and calculated according to the weight values to obtain a comprehensive index of the power grid in the case of the first power failure , and the calculation formula is as follows: ​ Power grid critical state determination index The calculation formula is as follows: ； In the formula, represents the comprehensive index of the power grid when the first power failure accident occurs; represents the total number of power failure accidents.

6. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 1, wherein, The step 5 is specifically: The network parameters of the current operation time of the power grid are used for power flow calculation, the required parameters are obtained from the power flow calculation results to calculate the index values in step 2, and the comprehensive index value of the current operation time of the power grid is obtained by weighting calculation of the indexes according to step 4 , and is compared with the self-organizing critical state judgment index of the power grid; if reaches , it indicates that the power grid is close to the self-organizing critical state at this time; if reaches or exceeds , it indicates that the power grid has entered the self-organizing critical state at this time.

7. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 1, characterized in that, The step 1 is specifically: The functional relationship between the output power of the wind turbine and the wind speed is: ； In the formula, For the first The actual output power of the wind turbines in the No. 1 wind farm; For the first Wind speed at the location of wind farm No. 1; For the first Cut-in wind speed of wind turbine units in wind farm No. 1; For the first Cut-off wind speed of wind turbine units in wind farm No. 1; For the first The rated wind speed of the wind turbine units in the No. 1 wind farm; For the first Rated output power of wind turbine units in wind farm No. 1; The output power of the wind farm is expressed as: ; In the formula, is the output power of the wind farm No. is the number of wind turbines of the wind farm No. is the output power of the wind farm No. is the number of wind turbines of the wind farm No.

8. The method of identifying the self-organized critical state of a power grid with high wind power penetration according to claim 1, characterized in that, In step 2, the calculation formulas of each index defined are as follows: The calculation formula of the wind power output ratio p index is as follows: ； wherein denotes the sum of the output powers of all wind farms; denotes the sum of the output powers of all power plants within the grid, including the wind farms. The calculation formula of the wind power penetration rate a index is as follows: ； wherein denotes the sum of all wind farm output powers in a unit of time; denotes the sum of all power loads in the grid in the same time period; Grid voltage deviation rate The calculation formula of the index is as follows: ， ； wherein is the actual voltage of node ; is the rated voltage of node ; is the total number of grid nodes; is the voltage deviation rate of node ; Grid line load factor The calculation formula of the index is as follows: ， ； wherein represents the number of lines in the grid; represents the load power of the i-th line; represents the number of lines in the grid; represents the maximum load power of the i-th line; represents the load rate of the i-th line; represents the number of lines in the grid; represents the number of lines in the grid; The calculation formula of the node degree index is as follows: ， ； wherein, is the number of nodes adjacent to node ; is the total number of nodes adjacent to node ; is the number of connecting edges between node and ; is the total number of nodes in the power grid; is the average degree of the power grid; The calculation formula of the node betweenness index is as follows: ； wherein is the number of all shortest paths between nodes and nodes ; is the number of shortest paths passing through node ; denotes the betweenness of node ; is the total number of grid nodes; is the average betweenness of the grid.

9. A computer device comprising a memory and one or more processors; the memory having stored therein executable code; the computer device being characterized by, When the processor executes the executable code, the steps of the self-organized critical state identification method of the power grid containing high proportion of wind power generation in any one of the above claims 1 to 8 are implemented.

10. A computer readable storage medium having stored thereon a program; characterized in that, The program, when executed by a processor, implements the steps of the method for identifying a self-organized critical state of a power grid with a high proportion of wind power according to any one of claims 1 to 8.

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