Transmission line vulnerability assessment and online monitoring based on multi-factor dynamic interaction diagram

Abstract

A kind of transmission line vulnerability assessment and online monitoring based on multi-factor dynamic interaction diagram, successively contains the generation of generalized fault chain data, the construction strategy of multi-factor interaction diagram, the dynamization of interaction diagram and vulnerability assessment and online monitoring;Specifically, the concept of generalized fault chain is proposed, the number range of initial lines that can trigger cascading failure is increased, so that the interaction diagram constructed based on fault chain theory can be applied to power systems under any N-K criterion, through principle analysis, the key parameters that can affect the accuracy of transmission line vulnerability assessment are introduced into the interaction diagram, the strategy of real-time updating parameters is proposed, which improves the accuracy and effectiveness of vulnerability assessment using interaction diagram, the principle of using uninterrupted data stream and sliding window to replace in order is proposed, which solves the difficult problem of dynamic interaction diagram of static interaction diagram, so that it can meet the accuracy requirements of transmission line vulnerability assessment under high penetration rate, and realizes the function of online monitoring.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of new energy power transmission line safety operation evaluation and protection, and relates to a power transmission line vulnerability evaluation and online monitoring based on a multi-factor dynamic interaction graph. BACKGROUND

[0002] The cascading failure of a new energy power system refers to a phenomenon of triggering large-scale power supply interruption by one or a group of initial disturbances. Once the cascading failure occurs, it will cause a series of serious consequences, especially the current hacker attack on the vulnerable components of the power system for the purpose of triggering cascading failure, which further increases the risk and consequences. In the power system, the interruption probability of the transmission line is one order of magnitude larger than that of the substation, and with the continuous expansion of the utility network, the transmission line is more frequently close to its limit operation, so it is very helpful to prevent the occurrence of cascading failure by evaluating the vulnerable transmission line.

[0003] Since the use of graphical methods can systematically describe the propagation behavior of cascading failure and the correlation between lines, and solve the limitations of complex and difficult to summarize the internal rules of ordinary data, therefore, different from the starting point of exploring the principle of cascading failure, researchers consider building a graph that can represent the above content to conduct vulnerability evaluation by means of it, and this graph model is called interaction graph.

[0004] The initial construction idea is to evaluate according to the complex network theory combined with the actual physical topology of the power system. However, relying only on the physical topology structure is not enough to solve the problem, because research has proved that it actually lacks strong connection with cascading failure. Subsequent researchers began to try to build various different interaction graphs to more fully excavate the characteristics of vulnerable transmission lines and their complex and hidden correlations in the propagation. With data-driven as the core, the construction idea of using the interruption sequence generated by the cascading failure model is outstanding in effect and rich in achievements. Especially considering the influence between the whole fault lines, the way of representing the interruption sequence by fault chain and finally combining all fault chains to form an interaction graph is not only simple in principle but also effective, but they all do not consider the case when the power system is in N-K criterion.

[0005] In addition, the current interaction graph is obtained by using the cascading failure model under fixed parameters, so the constructed interaction graph is called static interaction graph. However, in fact, the topological structure, running environment, and output of renewable energy and other key factors closely related to cascading failure are dynamically changing. These will change the final obtained outage sequence, and further affect the final vulnerability assessment result, especially in the case of increasing penetration of renewable energy. Because the vulnerability of the power system is very sensitive to the change of renewable energy generation. The static interaction graph can only reflect the vulnerability of the transmission line at a certain time, and once the key parameters affecting the cascading failure change, the final vulnerability is not necessarily reliable.

[0006] The existing method adopts the strategy of re-establishing a static interaction graph according to new parameter conditions at a fixed time to solve the problem, but there are two problems: first, the vulnerability state of the line in the updated time interval is unknown, and it cannot be monitored online and its development trend cannot be obtained; second, once the cascading failure occurs within the time interval, the lagging vulnerability information cannot support real-time response decision-making. These two points are very important for better preventing cascading failure and taking measures to intervene in the further deterioration after the occurrence of cascading failure. Therefore, the static characteristics also limit the further application range of the interaction graph. How to construct a dynamic interaction graph that can change according to the change of key influencing parameters is the key to solving the above problems. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a power transmission line vulnerability assessment and online monitoring based on a multi-factor dynamic interaction graph. The concept of generalized fault chain is proposed, and the dynamic interaction graph is realized by using uninterrupted data flow and sliding window, and more accurate power transmission line vulnerability assessment and classification online monitoring are realized by using the same.

[0008] To solve the above technical problems, the technical scheme adopted by the present application is as follows: a power transmission line vulnerability assessment and online monitoring based on a multi-factor dynamic interaction graph, which comprises the following steps:

[0009] S1, generation of generalized fault chain data, the concept of generalized fault chain is proposed by extending the fault chain theory, the number range of initial lines that can trigger cascading failure is increased, and the interaction graph constructed based on the fault chain theory can be applied to the power system under any N-K criterion;

[0010] S2, construction strategy of multi-factor interaction graph, the key parameters that can affect the accuracy of power transmission line vulnerability assessment are introduced into the interaction graph through principle analysis, and a real-time parameter updating strategy is proposed to improve the accuracy and effectiveness of vulnerability assessment using the interaction graph;

[0011] S3, the dynamicization of the interaction graph, proposes to use uninterrupted data flow and sliding window to replace the principle of order to realize the dynamicization of the static interaction graph, which can ensure the accuracy of the vulnerability assessment of the transmission line under high penetration rate; at the same time, in order to improve the generation rate of fault chain data to meet the dynamicization requirements, a parallel distributed computing framework is constructed;

[0012] S4, vulnerability assessment and online monitoring, in the process of dynamicization of the interaction graph, the related evaluation index parameters are monitored, the online monitoring of the vulnerability of the transmission line is realized, and the practical application range of the interaction graph is expanded; and according to the new features brought by the generalized fault chain, the scheme of primary and secondary vulnerable line classification monitoring and prevention is proposed.

[0013] In S1, the concept of generalized fault chain is to use the cascading failure simulation model, get the cascading failure sequence by selecting an initial trigger line, and select a line from each generation to form the final fault chain; when the power system is N-K criterion, an initial trigger line cannot meet the requirements, then the number of initial trigger lines at the first end is K+1, and the rest is unchanged.

[0014] In S2, the principle of constructing an interaction graph based on a fault chain is to take data-driven as the core, to mine the vulnerable lines in the transmission line through the generated fault chain and then to evaluate; through principle analysis, it is proposed to regard the topological structure, new load, random output of renewable energy and operating environment temperature as the key dynamic parameters, and adopt the real-time updating strategy, that is, in the process of fault chain data generation, once the change of the above parameters triggers the set threshold, the system parameters of the evaluation target are updated immediately to ensure that the obtained data has new vulnerability information.

[0015] In S3, the construction method of the fault chain interaction graph is to integrate the fault chains obtained from the cascading failure simulation model in the form of a graph structure; once the key parameters of the power system to be evaluated change, the vulnerability information contained in the original fault chain will be unreliable, and the uninterrupted data flow is used, and the newly generated fault chain is output after the key parameters are updated, and finally a data stream containing new information is formed. Then use the sliding window to get new fault chains from it and replace the old fault chains in the original interaction graph one by one in order to realize the dynamic change of the static interaction graph, and the new vulnerability information is naturally contained; a distributed computing framework is constructed between different computers, communication is carried out by using local area network, and the fault chain generation algorithm is changed to parallel implementation to improve the generation rate.

[0016] In S4, it can be found through the generalized fault chain that the line of the initial trigger fault and the line of the fault in the process of the cascading fault have different characteristics in vulnerability, the vulnerability of the former is embodied in easier to trigger the cascading fault, and the vulnerability of the latter is embodied in easier to propagate the cascading fault, the vulnerable lines are classified into primary vulnerable lines and secondary vulnerable lines, and the primary vulnerable lines and the secondary vulnerable lines are monitored respectively, and the occurrence and propagation of the cascading fault are prevented.

[0017] The main beneficial effects of the present application are:

[0018] Firstly, the concept of the generalized fault chain is defined, so that the interaction diagram based on the fault chain can be applied to the power system of a wider N-K criterion.

[0019] Considering the influence of the key parameters affecting the cascading fault, a dynamic interaction diagram construction method based on uninterrupted data flow is proposed, which solves the influence of the constantly changing parameters on the accuracy of the vulnerability evaluation result, realizes the dynamicization of the interaction diagram and the online monitoring of the vulnerable lines, and lays a foundation for the real-time decision of the interaction diagram for the cascading fault and the expansion of the application range. In addition, the construction method is simple and accurate in principle, and has good expansibility, and more actual factors can be introduced in the actual engineering application in the later period; in order to improve the generation speed of data, a parallel distributed computing framework is also built to meet the real-time requirements.

[0020] Through the characteristics of the generalized fault chain, different initial lines have great differences in the number of times of triggering the cascading fault, and this characteristic cannot be well reflected in the vulnerability evaluation index of the interaction diagram. Because the lines triggering the cascading fault and affecting the propagation of the cascading fault reflect different vulnerabilities. Therefore, it is proposed to classify the vulnerable lines into primary and secondary types for classification monitoring, which is conducive to taking corresponding measures to ensure the safe operation of the power grid according to different scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0021] The present application will be further described below in conjunction with the drawings and embodiments:

[0022] Figure 1 It is a structural form diagram of the generalized fault chain of the present application.

[0023] Figure 2 It is a diagram generated by the generalized fault chain algorithm of the present application.

[0024] Figure 3 It is an interaction diagram corresponding to the IEEE118 node system of the present application.

[0025] Figure 4 It is the evaluation result of the vulnerability of the power transmission line of the present application.

[0026] Figure 5The principle diagram of the dynamicization of the interactive graph of the present application.

[0027] Figure 6 The transmission line vulnerability change when the present application is implemented for online monitoring. DETAILED DESCRIPTION

[0028] As Figures 1-6 In the present application, a transmission line vulnerability assessment and online monitoring based on a multi-factor dynamic interactive graph, which comprises the following steps:

[0029] S1, generation of generalized fault chain data, the fault chain theory is extended, the concept of generalized fault chain is proposed, the number range of initial lines capable of triggering cascading faults is increased, and the interactive graph constructed based on the fault chain theory can be applied to the power system under any N-K criterion;

[0030] S2, construction strategy of multi-factor interactive graph, through principle analysis, the key parameters capable of affecting the accuracy of transmission line vulnerability assessment are introduced into the interactive graph, and the real-time updating parameter strategy is proposed to improve the accuracy and effectiveness of vulnerability assessment using the interactive graph;

[0031] S3, dynamicization of the interactive graph, the principle of sequential replacement of uninterrupted data flow and sliding window is proposed to realize the dynamicization of the static interactive graph, which can ensure the accuracy of transmission line vulnerability assessment under high penetration rate; at the same time, in order to improve the generation rate of fault chain data to meet the dynamicization requirement, a parallel distributed computing framework is constructed;

[0032] S4, vulnerability assessment and online monitoring, the related evaluation index parameters are monitored in the process of dynamicization of the interactive graph, the online monitoring of transmission line vulnerability is realized, the practical application range of the interactive graph is expanded; and according to the new features brought by the generalized fault chain, the classification monitoring and prevention scheme of primary vulnerable lines and secondary vulnerable lines is proposed.

[0033] In the preferred scheme, in S1, the concept of generalized fault chain is obtained by selecting an initial triggering line to get the cascading failure sequence through the cascading failure simulation model, and a line is selected from each generation to form the final fault chain; when the power system is under N-K criterion, the initial triggering line cannot meet the requirement, then the number of initial triggering lines at the first end is K+1, and the rest is unchanged.

[0034] In the preferred scheme, in S2, the principle of constructing the interaction graph based on the fault chain is to take data-driven as the core, to mine the vulnerable lines in the transmission line through the generated fault chain and then to evaluate; through principle analysis, it is proposed to take the topological structure, new type of load, random output of renewable energy and operating environment temperature as the key dynamic parameters, and to adopt the strategy of real-time updating, that is, in the process of generating the fault chain data, once the change of the above parameters triggers the set threshold, the system parameters of the evaluation target are immediately updated to ensure that the obtained data has new vulnerability information. The purpose of this step is to overcome the fact that most of the fault chain data obtained at present is obtained from the cascading failure simulation model according to the fixed model parameters, and with the increasing penetration of renewable energy in the power system, the fluctuation characteristics brought by itself will change the generated fault chain and affect the accuracy of vulnerability evaluation.

[0035] In the preferred scheme, in S3, the construction method of the fault chain interaction graph is to integrate the fault chains obtained from the cascading failure simulation model in the form of a graph structure; once the key parameters of the power system to be evaluated change, the vulnerability information contained in the original fault chain is unreliable, and the uninterrupted data stream is adopted, and the newly generated fault chain is output after the key parameters are updated, and finally a data stream containing new information is formed; then the new fault chain sequence is obtained from the sliding window and replaces the old fault chain in the original interaction graph one by one, realizing the dynamic change of the static interaction graph, and the new vulnerability information is naturally contained; a distributed computing framework is formed between different computers, communication is carried out by using a local area network, and the fault chain generation algorithm is changed to parallel implementation to improve the generation rate.

[0036] In a preferred solution, in S4, it can be found through the generalized fault chain that the lines that initially triggered the fault and the lines that caused the fault in the process of cascading failure are not the same in terms of vulnerability, the vulnerability of the former is reflected in the easier triggering of cascading failure, and the vulnerability of the latter is reflected in the easier propagation of cascading failure, the vulnerable lines are classified into primary vulnerable lines and secondary vulnerable lines for monitoring, respectively, to prevent the occurrence and propagation of cascading failure. The purpose of this step is that the former is determined by the number of times the lines appear in the initial triggering line set, and the latter is determined by the out-degree index in the interaction graph; the classification monitoring is conducive to better preventing the occurrence and propagation of cascading failure, and the vulnerability of the primary vulnerable line lies in being able to trigger cascading failure as an initial triggering line. Because different initial lines have different numbers of times of triggering cascading failure, the risk of cascading failure caused by them also differs in reality. By monitoring the primary vulnerable lines online and combining the historical failure rate of the lines and the actual operating environment for key maintenance, cascading failure can be more effectively prevented from being triggered. The vulnerability of the secondary vulnerable line mainly lies in the propagation process of cascading failure, which can determine the scale and impact of subsequent cascading failure. By monitoring them online, we can timely combine the power flow scheduling and other mitigation measures to avoid further deterioration of the fault in the process of cascading failure development. Which index is used for vulnerability evaluation depends on the ultimate purpose. In addition, if the goal is to avoid causing the maximum load loss rate to the power grid, an attack that simultaneously contains both types of vulnerable lines can cause greater damage because it contains the vulnerability of the lines in two different aspects. Therefore, by comparing different mixed sorting methods, a group of vulnerable lines that can cause the maximum load loss rate can be selected as another online monitoring object.

[0037] Embodiments

[0038] S1: IEEE 118-node system is adopted to show the research content of the application. The system contains 118 nodes, 54 power generators, and 195 transmission lines. Taking the N-1 criterion as an example, because the standard data of the system does not meet the N-1 standard, we increase the flow limit of part of the branches, so that the flow in the line is lower than 95% of the long-term limit (rateA), and the interruption of a single line cannot cause cascading failure.

[0039] The method of the application can be applied to any power system under the N-K security criterion, and any change in the number of nodes of the power system or the security criterion is within the scope of the claims of the application.

[0040] S2: The enumeration method is used to arrange and combine the initial triggering lines, and then the DCSIMSEP cascading failure simulation model is used to obtain the generalized fault chain, the definition of which is shown in the accompanying Figure 1 algorithm process is shown in the accompanyingFigure 2 The total number of programs to be executed is 18915. The patent uses two computers with Intel dual-core CPU (at 3.20GHz), 4GB RAM and Intel 6-core CPU (at 2.60GHz), 16GB RAM to build a parallel distributed computing system, which is composed of a client, a job manager and 8 workers, and uses Ethernet communication mode. The final results of parallel distributed computing of all programs are shown in Table 1, and the parallel distributed computing system can greatly reduce the data generation time. At the same time, the results show that a total of 328 initial trigger line combinations can cause cascading failure, so it can be seen that the probability of different initial lines in triggering cascading failure is different under the N-K criterion.

[0041] Table 1 Comparison of program calculation time

[0042]

[0043]

[0044] The method of the application determines the initial trigger line set, and uses the classical cascading failure simulation model to obtain the generalized fault chain data required for constructing the interaction graph; at the same time, a parallel distributed computing framework is constructed to improve the data generation rate. Any change in the initial trigger line set, any change in the cascading failure simulation model, and any change in the parallel distributed computing framework parameters all belong to the scope of the claims of the application.

[0045] S3: Using all the obtained generalized fault chains to construct the interaction graph of the IEEE118 node system, as shown in the accompanying drawings Figure 3 The results are shown in Table 2. The results show that (1) the vulnerability nodes obtained from the interaction graph are effective (2) among the indexes of the secondary vulnerable lines, the out-degree has the best evaluation effect. (3) The primary vulnerable line proposed by the application is also effective. As shown in Table 2, the numbers in the vulnerability line ranking evaluated by out-degree and trigger frequency are not exactly the same, which shows that the two reflect different characteristics, so it is necessary to divide the lines into primary and secondary vulnerable nodes. (4) Mixing the two types of nodes for attack can achieve better attack effect. Only changing the number of attacks belongs to the scope of the claims of the application. Figure 4 The results are shown in Table 2. The results show that (1) the vulnerability nodes obtained from the interaction graph are effective (2) among the indexes of the secondary vulnerable lines, the out-degree has the best evaluation effect. (3) The primary vulnerable line proposed by the application is also effective. As shown in Table 2, the numbers in the vulnerability line ranking evaluated by out-degree and trigger frequency are not exactly the same, which shows that the two reflect different characteristics, so it is necessary to divide the lines into primary and secondary vulnerable nodes. (4) Mixing the two types of nodes for attack can achieve better attack effect. Only changing the number of attacks belongs to the scope of the claims of the application.

[0046] Table 2 The vulnerability ranking of primary and secondary nodes

[0047]

[0048] S4: The node 49 in the original system is changed to a wind farm, and the total installed capacity of the wind farm is 300 MW. The renewable energy penetration rate of the system is 6.4%. The maximum change of the output power of the wind farm per minute is limited to not more than 30 MW, and the maximum change per 10 minutes is limited to not more than 100 MW, so as to avoid affecting the stability of the power grid. The output power of the wind farm at the initial time is 238.066 MW. After the first sampling, the output power of the wind farm at this time is 278.815 MW. The method proposed in this paper is used to update the related parameters, and the generated generalized fault chain data after updating is merged into the original data stream. The original interaction diagram is replaced by a sliding window one by one to realize the dynamic of the interaction diagram. The principle process is shown in the accompanying Figure 5 Figure 6 The online monitoring process of the ranking of the part of the secondary vulnerable lines using the out-degree evaluation index. It can be seen that the vulnerability of the 58 line which is originally ranked at the back quickly rises under the new parameters. The vulnerability ranking of the system after the dynamic change is shown in Table 3. The ranking of the overall line vulnerability has changed significantly, which also shows the necessity of constructing a dynamic interaction diagram.

[0049] Table 3 The line vulnerability ranking before and after the parameter change

[0050]

[0051] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as a limitation of the present application. The embodiments in the application and the features in the embodiments can be combined with each other without conflict. The protection scope of the present application should be based on the technical solutions claimed in the claims, including the equivalent replacement solutions of the technical features claimed in the claims. That is, the equivalent replacement improvement within this range is also within the protection scope of the present application.​

Claims

1. A method for vulnerability assessment and online monitoring of transmission lines based on multi-factor dynamic interaction graphs, characterized in that, It includes the following steps: S1, the generation of generalized fault chain data, extends the fault chain theory, proposes the concept of generalized fault chain, increases the range of the number of initial lines that can trigger cascading faults, and makes the interaction diagram constructed based on fault chain theory applicable to power systems under any NK criterion. S2, the construction strategy of multi-factor interaction graph, adopts data-driven approach as the core of constructing static interaction graph. Through principle analysis, key parameters that can affect the accuracy of transmission line vulnerability assessment are introduced into the interaction graph. A real-time parameter update strategy is proposed to expand the interaction graph into a multi-factor interaction graph, thereby improving the accuracy and effectiveness of vulnerability assessment using interaction graph. S3, Dynamic Interaction Graph: This paper proposes to use uninterrupted data streams and sliding windows to sequentially replace static interaction graphs, thereby ensuring the accuracy of transmission line vulnerability assessment under high penetration rates. At the same time, to improve the generation rate of fault chain data to meet the dynamic requirements, a parallel distributed computing framework is constructed. S4, Vulnerability Assessment and Online Monitoring: During the dynamic transformation of the interactive graph, assessment index parameters, including topology, new loads, random output of renewable energy, and operating environment temperature, are monitored to achieve online monitoring of transmission line vulnerability and expand the practical application scope of the interactive graph; a classification monitoring and prevention scheme for primary and secondary vulnerable lines is proposed. In S1, the concept of a generalized fault chain is to use a cascaded fault simulation model to obtain a cascaded fault sequence by selecting an initial triggering line, and then select one line from each generation to form the final fault chain. When the power system follows the NK criterion, one initial triggering line cannot meet the requirements, so the number of initial triggering lines at the beginning is K+1, and the rest remain unchanged. In S3, the method for constructing a fault chain interaction graph integrates the fault chains obtained from the cascaded fault simulation model in a graph structure. Once the key parameters of the power system to be evaluated change, the vulnerability information contained in the original fault chains becomes unreliable. A continuous data stream is used, and each time the key parameters are updated, the newly generated fault chains are output, ultimately forming a data stream that continuously contains new information. Then, a sliding window is used to obtain the new fault chain sequence from it and replace the old fault chains in the original interaction graph one by one, realizing the dynamism of the static interaction graph, and the new vulnerability information is naturally included in it. By constructing a distributed computing framework among different computers, using a local area network for communication, and changing the fault chain generation algorithm to a parallel implementation, the generation rate of generalized fault chain data is improved.

2. The method for vulnerability assessment and online monitoring of transmission lines based on multi-factor dynamic interaction graphs according to claim 1, characterized in that: In S2, the principle of using the interaction graph constructed based on fault chains is data-driven, and the generated fault chains are used to discover vulnerable lines in the transmission line and then evaluate them. Based on principle analysis, this paper proposes to regard four factors as key dynamic parameters: topology, new loads, random output of renewable energy, and operating environment temperature. A real-time update strategy is adopted, that is, in the process of generating fault chain data, once the change of the above parameters is detected and triggers the set threshold, the system parameters of the evaluation target are immediately updated to ensure that the acquired data contains new vulnerability information.

3. The method for vulnerability assessment and online monitoring of transmission lines based on multi-factor dynamic interaction graphs according to claim 1, characterized in that: In S4, through the generalized fault chain, it was found that the vulnerability characteristics of the line that initially triggers the fault and the line that fails during the cascading fault are different. The former is more vulnerable in that it is easier to trigger cascading faults, while the latter is more vulnerable in that it is easier to propagate cascading faults. Vulnerable lines are classified into primary vulnerable lines and secondary vulnerable lines for separate monitoring to prevent the occurrence and propagation of cascading faults.

Images