Power system cascading failure risk assessment method and system considering uncertainty of new energy power generation

Abstract

The invention discloses an electric power system cascading failure risk assessment method and system considering new energy power generation uncertainty, and belongs to the technical field of electric power system safety. According to the fault risk assessment method disclosed by the invention, the cascading fault of the power system is modeled to obtain system power flow redistribution and relay protection execution conditions after large-scale transfer of system power flow after the fault, and in addition, rapid simulation of a cascading fault risk model is realized based on a depth-first algorithm of risk estimation knowledge. According to early-stage risk assessment experience and fault feature knowledge, key simulation paths are intelligently screened, high-risk node calculation is preferentially processed, unnecessary calculation branches are greatly reduced, simulation efficiency is effectively improved, the dynamic propagation process of cascading faults in a complex power system is rapidly simulated, and risks can be comprehensively assessed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system security, and particularly relates to a method and system for evaluating the risk of cascading failures in a power system considering the uncertainty of new energy generation. Background Art

[0002] A power system cascading failure refers to the successive failures of one or more related components triggered by an initial failure, ultimately leading to a large-scale power outage of the system. The historical power outages caused by cascading failures have resulted in significant economic losses and social chaos. The increasing popularity of new energy generation has brought great uncertainty to the power system and increased the risk of cascading failures. This is because as the penetration rate of new energy generation continues to increase, the increased uncertainty makes the power system closer to its operating limit, which will have a negative impact on the reliability of the power system. In order to reduce the risk of power system cascading failures, it is necessary to develop a fast and accurate method to evaluate the risk of cascading failures with uncertain new energy generation.

[0003] For power systems containing uncertain new energy generation, numerous methods for evaluating the risk of cascading failures have been proposed. These methods can be roughly divided into two categories: scenario clustering methods and probability evaluation methods. The scenario clustering method represents the uncertainty of new energy generation as a set of deterministic typical scenarios that cover the possible range of new energy generation output power. For each scenario, a cascading failure simulation is performed, and the overall risk of cascading failures is evaluated by considering the occurrence probability of each scenario and its related cascading failure results. In order to further incorporate the probability characteristics of new energy generation, a probability evaluation method has been proposed. The probability evaluation method utilizes the probability distribution of new energy generation output power to calculate the risk of cascading failures. This type of method provides a comprehensive risk assessment by calculating the occurrence probability and consequences of the fault evolution paths during the cascading failure propagation process. In order to improve the calculation efficiency, this type of method usually uses predefined thresholds to filter out the cascading failure propagation paths with relatively low occurrence probabilities. However, cascading failures essentially involve events with low occurrence probabilities but severe consequences. If the cascading failure risk assessment method solely relies on probability thresholds to cut off low-probability propagation paths, it will ignore the cascading failure paths that are rare but may lead to catastrophic consequences. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the risk of cascading failures in a power system considering the uncertainty of new energy generation, so as to solve the technical problem that the existing evaluation methods are difficult to comprehensively evaluate the risk.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] The present invention discloses a method for risk assessment of cascading failures in a power system considering the uncertainty of new energy power generation, comprising the following steps:

[0007] Collect power system data information, perform standardization processing on the data information to obtain standard information; establish a cascading failure propagation model based on the standard information;

[0008] Determine the initial fault information of the power system, and perform probabilistic power flow calculation based on the system state after the initial fault according to the cascading failure propagation model to obtain the power flow redistribution and relay protection execution situation after the fault;

[0009] Based on the power flow redistribution and relay protection execution situation after the fault, use the depth-first algorithm to perform rapid simulation of cascading failure risk to obtain simulation results;

[0010] Combine the obtained simulation results to perform dispatching simulation and risk calculation, and output a cascading failure risk assessment report.

[0011] Further, the power system data information includes topological structure, node injection power, node injection power probability distribution, node parameters, line parameters, and node types.

[0012] Further, the node injection power, node parameters, and line parameters are all converted into per-unit values for calculation.

[0013] Further, when establishing the cascading failure propagation model based on the standard information, the uncertainty of new energy power generation is combined with the power flow redistribution characteristics and the response of the relay protection system.

[0014] Further, the power flow redistribution characteristics are calculated based on the semi-invariant method, and the formula used is as follows:

[0015]

[0016] Among them, Y is the power flow variable output by the power system; X i is multiple input random variables, α i is the weight factor, indicating the contribution degree of X i to Y; k n (Y) is the nth-order semi-invariant, describing the probability distribution characteristics of the random variable; n is the order of the semi-invariant;

[0017]

[0018] Among them, is the probability density function of the standard normal distribution, γ1 is the standardized skewness, γ2 is the standardized kurtosis, H n (y) is the Hermite polynomial of the nth order; f Y(y) is the probability density function of the system power flow.

[0019] Furthermore, the formula for combining the uncertainty of new energy power generation with the response of the relay protection system is as follows:

[0020]

[0021] Where, P ij is the lower bound of the power flow distribution of line ij, is the upper bound of the power flow distribution, δ ij is the lower bound of the overload severity, is the upper bound of the overload severity, P ij,th is the limit transmission capacity of line ij.

[0022] Furthermore, based on the system state after the initial fault, probability power flow calculation is performed according to the cascading fault propagation model, and the calculation formulas for obtaining the post-fault system power flow redistribution and the relay protection execution situation are as follows:

[0023]

[0024] Where: Pr is the action probability of line ij when the power flow distribution is f(P ij ).

[0025] Furthermore, when using the depth-first algorithm for rapid simulation of cascading fault risks, the estimated overload risk value and island risk value are used as approximate values of the overall risk;

[0026] The calculation formula for the estimated overload risk value is:

[0027]

[0028] Where, Pr is the state transition probability of the system under the action of the relay protection of line ij, and |P ij | is the absolute value of the line power flow;

[0029] The calculation formula for the estimated island risk value is:

[0030]

[0031] Where, PTDF k is the sensitivity coefficient of the power flow change of line k after line ij is taken out of service.

[0032] Furthermore, the steps for dispatching simulation and risk calculation by combining the obtained simulation results are:

[0033] When there is no new overloaded tripped line in the power system, perform dispatching simulation on the power system to obtain the load loss of each node in the system under the action of the dispatching strategy, and calculate the cascading failure risk based on the load loss and the line state transition probability during the cascading failure propagation process; the calculation formula for the cascading failure risk is:

[0034]

[0035] where DL is the load loss of the system after the cascading failure propagation ends, obtained based on the system state after the dispatching strategy is executed, Pr is the probability that the system transfers from one state to another under the action of line tripping, is the set of system states, the superscript represents the stage of cascading propagation, and the subscript represents the mth specific state within that stage;

[0036] The calculation formula for DL is:

[0037]

[0038] In the formula, is the load power of each node in the power system before the initial fault occurs, and P i is the load power of each node after the cascading failure propagation ends.

[0039] The present invention also discloses a cascading failure risk assessment system for a power system considering the uncertainty of new energy power generation, including:

[0040] Data acquisition and modeling module: used to collect power system data information, perform standardized processing on the data information to obtain standard information; establish a cascading failure propagation model based on the standard information;

[0041] Initial fault setting and probability flow calculation module: used to determine the initial fault information of the power system, and perform probability flow calculation based on the system state after the initial fault according to the cascading failure propagation model to obtain the post-fault system power flow redistribution and relay protection execution situation;

[0042] Dispatching simulation and risk calculation module: used to perform rapid simulation of cascading failure risk using the depth-first algorithm based on the post-fault system power flow redistribution and relay protection execution situation to obtain the simulation result; combine the obtained simulation result for dispatching simulation and risk calculation, and output a cascading failure risk assessment report.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention discloses a method for risk assessment of cascading failures in a power system considering the uncertainty of new energy power generation. By modeling the cascading failures in the power system, the uncertainty of new energy power generation is combined with power flow redistribution and the response of the relay protection system to describe the mechanism of action of uncertain new energy power generation in the propagation of cascading failures. Combining with probabilistic power flow calculation, the power flow redistribution and the execution of relay protection after the large-scale transfer of the system power flow after the fault are obtained. In addition, a depth-first algorithm based on risk prediction knowledge is used to realize the rapid simulation of the cascading failure risk model. According to the previous risk assessment experience and fault feature knowledge, key simulation paths are intelligently selected, and high-risk nodes are preferentially processed for calculation, greatly reducing unnecessary calculation branches, effectively improving the simulation efficiency, and quickly simulating the dynamic propagation process of cascading failures in a complex power system, and being able to comprehensively assess the risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of the method for risk assessment of cascading failures in a power system considering the uncertainty of new energy power generation according to the present invention;

[0046] Figure 2 It is a cascading failure propagation model established according to the present invention;

[0047] Figure 3 It is a schematic diagram of the depth-first algorithm based on risk prediction knowledge according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0050] The present invention discloses a method for risk assessment of cascading failures in a power system considering the uncertainty of new energy power generation, comprising the following steps:

[0051] Collect power system data information and perform standardization processing on the data information; the collected data information includes: topological structure, node injection power, probability distribution of node injection power, node parameters, line parameters, and node types; among them, the node injection power, node parameters, and line parameters are all converted into per-unit values for calculation;

[0052] Based on the collected standard information, model the cascading failures in the power system, combine the uncertainty of new energy power generation with power flow redistribution and the response of the relay protection system, and describe the mechanism of action of uncertain new energy power generation in the propagation of cascading failures;

[0053] Determine the initial fault information of the power system, where the initial fault information includes the number and location of fault lines, and perform probabilistic power flow calculation based on the state of the system after the initial fault to obtain the power flow redistribution and relay protection execution conditions after large-scale power flow transfer in the system after the fault;

[0054] Implement fast simulation of the cascading failure risk model based on the depth-first algorithm of risk prediction knowledge. During the execution of the algorithm, according to the previous risk assessment experience and fault feature knowledge, intelligently screen key simulation paths, prioritize the calculation of high-risk nodes, significantly reduce unnecessary calculation branches, effectively improve the simulation efficiency, and quickly simulate the dynamic propagation process of cascading failures in a complex power system.

[0055] When there are no new overloaded tripped lines in the system, perform dispatching simulation on the power system to obtain the load loss conditions of each node in the system under the action of the dispatching strategy. Calculate the cascading failure risk based on the load loss conditions and the line state transition probability during the propagation process of cascading failures.

[0056] Preferably, for the modeling of cascading failures in the power system, combine the uncertainty of new energy power generation with power flow redistribution and the response of the relay protection system, and describe the mechanism of action of uncertain new energy power generation in the propagation of cascading failures;

[0057] Among them, the power flow redistribution situation is obtained based on the probabilistic power flow calculation of the system. The probabilistic power flow calculation of the system adopts the semi-invariant method; if the output power flow variable Y of the system is composed of a linear combination of multiple input random variables X i i.e., Y = ∑α i X i i, then the semi-invariants of each order of the output Y can be obtained by weighted summation of the semi-invariants of the order of the input variable X i i:

[0058]

[0059] In the formula, n represents the order of the semi - invariant;

[0060] Once a series of semi - invariants of the output variable Y are obtained, the Gram - Charlier A series can be used to reconstruct the probability density function of the output; the Gram - Charlier A series is expressed as:

[0061]

[0062] In the formula, is the probability density function of the standard normal distribution, γ1 is the standardized skewness, γ2 is the standardized kurtosis, and H n (y) is the Hermite polynomial of the nth order;

[0063] Through the above formula, the probability distribution characteristics of the power flow in the power system can be estimated based on the semi - invariants of the known probability distribution of new - energy power generation uncertainty;

[0064] Relay protection realizes the accurate removal of overloaded lines based on the comparison between the calculation results of the system probability power flow and the relay protection action threshold; specifically, at each stage of the propagation of cascading faults, as the system probability power flow calculation outputs the power flow distribution information in real - time, the relay protection device continuously monitors the line power flow. It determines whether the current line is overloaded based on the upper and lower bound information of the power flow distribution, and its calculation formula is:

[0065]

[0066] In the formula, P ij is the lower bound of the power flow distribution of line ij, is the upper bound of the power flow distribution, δ ij is the lower bound of the overload severity, is the upper bound of the overload severity, and P ij,th is the limit transmission capacity of line ij.

[0067] Preferably, according to the state of the system after the initial fault, the probability power flow is calculated to obtain the power flow redistribution and the relay protection execution situation after the large - scale transfer of the system power flow after the fault. The calculation formula for the state transition probability of the system under relay protection action is:

[0068]

[0069] In the formula, Pr is the action probability of line ij when the power flow distribution is f(P ij ).

[0070] Preferably, a depth-first algorithm based on risk prediction knowledge is used to implement the fast simulation of the cascading failure risk assessment model. This algorithm uses the predicted overload and islanding risk values as the approximate values of the overall risk to guide the process of searching for cascading failure paths; by sorting according to the risk estimation values, the lines with higher failure risks are processed first, enabling this method to effectively identify high-risk failure paths and achieve a balance between accurately identifying high-risk failure paths and maintaining the overall assessment efficiency;

[0071] The predicted risk knowledge includes overload risk value and islanding risk value. Among them, the calculation formula for the overload risk value is:

[0072]

[0073] In the formula, Pr is the state transition probability of the system under the action of the relay protection of line ij, and |P ij | is the absolute value of the line power flow;

[0074] The calculation formula for the islanding risk value is:

[0075]

[0076] In the formula, PTDF k is the sensitivity coefficient of the power flow change of line k after line ij is taken out of operation.

[0077] Preferably, when there is no new overloaded tripped line in the system, the power system is scheduled and simulated to obtain the load loss of each node in the system under the action of the scheduling strategy, and the cascading failure risk is calculated based on the load loss and the line state transition probability during the cascading failure propagation process. The calculation formula for the cascading failure risk is:

[0078]

[0079] In the formula, DL is the load loss of the system after the cascading failure propagation ends, which is obtained according to the system state after the implementation of the scheduling strategy. Pr is the probability that the system transfers from one state to another under the action of line tripping, is the set of system states, the superscript represents the stage of cascading propagation, and the subscript represents the mth specific state in that stage;

[0080] The load loss DL of the system after the cascading failure propagation ends depends on the implementation of the scheduling strategy, and its calculation formula is:

[0081]

[0082] In the formula, is the load power of each node in the power system before the initial fault occurs, P iIt is the load power of each node after the end of cascading failure propagation.

[0083] The present invention will be further described in detail below with reference to the accompanying drawings:

[0084] See Figure 1 , the risk assessment method for cascading failures in a power system considering the uncertainty of new energy power generation disclosed by the present invention includes steps S101 to S105, and the specific steps are as follows:

[0085] S101. Collect power system data information and perform standardization processing on the data information. The collected data information includes: topological structure, node injection power, probability distribution of node injection power, node parameters, line parameters, and node types; among them, the node injection power includes active and reactive power, the node parameters include voltage amplitude and phase angle, the line parameters are line impedance, and the node types include traditional power generation nodes, new energy power generation nodes, and load nodes;

[0086] S102. Based on the collected standard information, model the cascading failures in the power system, combine the uncertainty of new energy power generation with power flow redistribution and relay protection system response, and describe the action mechanism of uncertain new energy power generation in the propagation of cascading failures;

[0087] In this embodiment, during the fault propagation process, different injection levels at new energy power generation nodes will lead to different power flow distributions, which will in turn cause different branch overloads and subsequent faults. Therefore, due to the power injection changes caused by the uncertainty of renewable energy power generation, the same initial fault may trigger different power flow redistributions and relay protection actions, and then lead to different fault propagation paths;

[0088] The fault propagation path is defined as the line tripping sequence caused by the initial fault; the fault propagation path can be expressed as:

[0089]

[0090] In the formula, is the state set of the system, the superscript i represents the stage of cascading propagation, the subscript m represents the mth specific state within this stage, s j is the state of line j, 1 represents running, and 0 represents tripping;

[0091] See Figure 2, which is the power system cascading failure model provided by the embodiments of the present invention. Considering the uncertainty of new energy power generation, when analyzing all possible fault propagation paths originating from the same initial state, a tree-like structure will be presented. At the same time, cascading fault propagation exhibits Markov characteristics, which means that the occurrence of the next fault only depends on the current state of the power system, rather than the sequence of events that led to the fault. In order to capture the diversity of fault propagation paths under the condition of new energy power generation uncertainty and describe the cascading fault process, a tree structure is used to describe the cascading fault propagation process. In the model, nodes represent the set of system states Edges represent the power flow transfer and line relay protection actions in the system considering the uncertainty of new energy power generation. When the fault propagation ends, calculate the load loss DL(Path) of each fault path to measure the propagation result of cascading faults;

[0092] S103. Determine the initial fault information of the power system. The initial fault information includes the number and location of fault lines, and perform probabilistic power flow calculation based on the state of the system after the initial fault to obtain the power flow redistribution and relay protection execution situation after large-scale power flow transfer in the system after the fault;

[0093] In this embodiment, determine the initial fault location of the power system in this embodiment, simulate emergencies such as extreme weather and equipment failures that cause problems such as line tripping in the power system, and then simulate the N-k cascading faults in the power system. After the initial fault, the power flow in the power system will be redistributed. Some lines may be overloaded due to undertaking the power transmission task of the fault branch. When a line is overloaded, the relay protection mechanism will trip, resulting in a further change in the power transmission path and triggering a larger-scale power flow redistribution; this power flow redistribution will overload more lines, leading to a series of interrelated component failures and ultimately triggering a large-scale cascading power outage accident;

[0094] In the embodiment, step S103 includes but is not limited to the following steps:

[0095] The power flow redistribution situation is obtained based on the system probabilistic power flow calculation; the system probabilistic power flow calculation uses the semi-invariant method. If the power flow variable Y output by the system is composed of a linear combination of multiple input random variables X i That is, Y = ∑α i X i , then the semi-invariants of each order of the output Y can be obtained by weighted summation of the semi-invariants of the order of the input variable X i :

[0096]

[0097] In the formula, n represents the order of the semi-invariant;

[0098] Having obtained a series of semi-invariants of the output variable Y, the Gram-Charlier A series can be used to reconstruct the probability density function of the output; the Gram-Charlier A series is expressed as:

[0099]

[0100] wherein, is the probability density function of the standard normal distribution, γ1 is the standardized skewness, γ2 is the standardized kurtosis, and H n (y) is the Hermite polynomial of the nth order;

[0101] Through the above formula, the probability distribution characteristics of the power flow in the power system can be estimated based on the semi-invariants of the known probability distribution of new energy power generation uncertainties;

[0102] The implementation of relay protection is to accurately cut off the overloaded line based on the comparison between the system probability power flow calculation result and the relay protection action threshold; specifically, at each stage of the cascading fault propagation, as the system probability power flow calculation outputs the power flow distribution information in real time, the relay protection device will continuously monitor the line power flow. When the system is in a certain operating scenario, according to the power flow value P ij of line ij obtained from the probability power flow calculation and the preset relay protection action threshold P ij,th are compared; if P ij >P ij,th , the relay protection device starts to act and cuts off the overloaded line;

[0103] S104. Implement fast simulation of the cascading fault risk model based on the depth-first algorithm with risk prediction knowledge; during the execution of the algorithm, according to the previous risk assessment experience and fault feature knowledge, key simulation paths are intelligently selected, high-risk node calculations are preferentially processed, unnecessary calculation branches are significantly reduced, the simulation efficiency is effectively improved, and the dynamic propagation process of cascading faults in a complex power system is quickly simulated.

[0104] Figure 3 FIG. is a schematic diagram of the depth-first algorithm based on risk prediction knowledge. In some embodiments, step S104 includes but is not limited to the following steps:

[0105] Execute the initial fault and update the system state.

[0106] Determine whether there are islands in the system. If there are no islands, continue with the following steps. If there are islands, perform the following steps in each island:

[0107] Based on the current power system state, calculate the probability power flow distribution;

[0108] If there is interval information on the system probabilistic power flow distribution, it is determined that there are overloaded lines in the system, and an alternative tripping set Ω is formed; if there are multiple overloaded lines in the alternative tripping set, the risk prediction values of each line in the alternative tripping set are calculated, including overload risk and islanding risk. Among them, the calculation formula for the overload risk value is:

[0109]

[0110] The calculation formula for the islanding risk value is:

[0111]

[0112] Delete the lines with low risk in the alternative tripping set according to the set threshold, and the final alternative tripping set is:

[0113]

[0114] Calculate and record the system state transition probability after each line in the alternative tripping set trips according to the probabilistic power flow result; the calculation formula for the system state transition probability is:

[0115]

[0116] Cut off the lines in the alternative tripping set; after the lines trip, the system topology is updated again, and it is necessary to re-determine the islanding and perform cascading failure simulation;

[0117] S105. When there are no new overloaded tripping lines in the system, perform a dispatching simulation on the power system to obtain the load loss of each node in the system under the action of the dispatching strategy; calculate the cascading failure risk according to the load loss situation and the line state transition probability during the cascading failure propagation process;

[0118] The calculation formula for the cascading failure risk is:

[0119]

[0120] In the formula, DL is the load loss of the system after the cascading failure propagation ends, which is obtained according to the system state after the dispatching strategy is executed, and Pr is the probability that the system transfers from one state to another under the action of line tripping;

[0121] The load loss DL of the system after the cascading failure propagation ends depends on the execution situation of the dispatching strategy, and its calculation formula is:

[0122]

[0123] In the formula, is the load power of each node in the power system before the initial fault occurs, and P i is the load power of each node after the cascading failure propagation ends;

[0124] After the chain fault propagation ends, the calculation formula for the load power of each node is as follows:

[0125] minP G,i ;

[0126]

[0127] P G,i +P REG,k =P L,j ;

[0128] In the formula, P G,i is the power generation power of the traditional power generation node i, is the maximum value of the power generation power of the traditional power generation node, is the minimum value of the power generation power of the traditional power generation node, P REG,k is the power generation power of the new energy power generation node k, is the maximum value of the power generation power of the new energy power generation node, is the minimum value of the power generation power of the new energy power generation node, P L,j is the load node power, is the maximum value of the load node power; in the dispatching strategy, the constraints from top to bottom are the power constraint of the traditional power generation node, the power constraint of the new energy power generation node, the power consumption constraint of the load node, the line power flow constraint, and the system supply-demand balance constraint.

[0129] The above content is only to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation, characterized in that: The following steps are involved: Collect power system data information, standardize the data information, and obtain standard information; Establish a cascading failure propagation model based on standard information; Determine the initial fault information of the power system, and perform probabilistic power flow calculation based on the system state after the initial fault according to the cascading fault propagation model to obtain the system power flow redistribution and relay protection execution status after the fault; Based on the redistribution of system power flow and the execution of relay protection after the fault, the depth-first algorithm is used to quickly simulate the risk of cascading failures and obtain the simulation results; Combine the obtained simulation results to perform scheduling simulation and risk calculation, and output a cascading failure risk assessment report.

2. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 1, characterized in that: The power system data information includes topological structure, node injection power, node injection power probability distribution, node parameters, line parameters and node type.

3. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 2, characterized in that: The node injection power, node parameters and line parameters are all converted into per-unit values ​​for calculation.

4. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 1, characterized in that: When establishing the cascading fault propagation model based on standard information, the uncertainty of renewable energy power generation is combined with the power flow redistribution characteristics and the response of the relay protection system.

5. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 4, characterized in that: The power flow redistribution characteristic is calculated based on the semi-invariant method, and the formula used is as follows: Among them, Y is the power flow variable output by the force system; X i are multiple input random variables, α i is the weight factor, indicating that X i Contribution to Y; k n (Y) is an n-order semi-invariant, which describes the probability distribution characteristics of the random variable; n is the order of the semi-invariant; in, is the probability density function of the standard normal distribution, γ1 is the standardized skewness, γ2 is the standardized kurtosis, H n (y) is a Hermitian polynomial of order n; f Y (y) is the probability density function of the system power flow.

6. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 4, characterized in that: The formula used to combine the uncertainty of renewable energy power generation with the response of the relay protection system is as follows: in, is the lower bound of the power flow distribution of line ij, is the upper bound of the tidal distribution, δ ij is the lower bound of the overload severity, is the upper bound of the overload severity, P ij,th is the maximum transmission capacity of line ij.

7. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 4, characterized in that: Based on the system state after the initial fault, the probability power flow calculation is performed according to the cascading fault propagation model. The calculation formula used to obtain the system power flow redistribution and relay protection execution status after the fault is as follows: Where: Pr is the line ij when the power flow distribution is f(P ij ) is the action probability when .

8. The method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 1, characterized in that: When the depth-first algorithm is used to perform rapid simulation of cascading failure risk, the estimated overload risk value and island risk value are used as approximate values ​​of the overall risk; The calculation formula of the estimated overload risk value is: Where Pr is the state transition probability of the system under the action of line ij relay protection, |P ij | is the absolute value of the line flow index; The calculation formula of the estimated island risk value is: Among them, PTDF k It is the sensitivity coefficient of the power flow change of line k after line ij is taken out of operation.

9. A method for assessing the risk of cascading failures in a power system considering the uncertainty of renewable energy generation according to claim 1, characterized in that: The steps of scheduling simulation and risk calculation based on the obtained simulation results are as follows: When there is no new overload tripping line in the power system, the power system is dispatched and simulated to obtain the load loss of each node in the system under the dispatch strategy, and the cascading failure risk is calculated based on the load loss and the line state transition probability during the cascading failure propagation process; the calculation formula for cascading failure risk is: Among them, DL is the load loss of the system after the cascading fault propagation ends, which is obtained according to the system state after the dispatch strategy is executed, Pr is the probability of the system transferring from one state to another under the action of line tripping, is the set of states of the system, the superscript represents the stage of chain propagation, and the subscript represents the mth specific state within the stage; The calculation formula of DL is: In the formula, is the load power of each node in the power system before the initial fault occurs, P i It is the load power of each node after the cascading fault propagation ends.

10. A power system cascading failure risk assessment system considering the uncertainty of renewable energy generation, characterized in that: include: Data acquisition and modeling module: used to collect power system data information, standardize the data information, and obtain standard information; Establish a cascading failure propagation model based on standard information; Initial fault setting and probabilistic power flow calculation module: used to determine the initial fault information of the power system, and perform probabilistic power flow calculation based on the system state after the initial fault according to the cascading fault propagation model to obtain the system power flow redistribution and relay protection execution status after the fault; Dispatching simulation and risk calculation module: used to quickly simulate the risk of cascading failures using a depth-first algorithm based on the redistribution of system power flow and the execution of relay protection after a fault, and obtain simulation results; Combine the obtained simulation results to perform scheduling simulation and risk calculation, and output a cascading failure risk assessment report.

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