Large power grid multi-time scale maintenance plan risk identification method and device

Abstract

The invention discloses a large-power-grid multi-time-scale maintenance plan risk identification method and a large-power-grid multi-time-scale maintenance plan risk identification device. The method comprises the following steps: on the basis of all files of a large power grid, superposing a maintenance plan, a power generation plan and bus load prediction of the large power grid to form a group of future state operation modes; the method comprises the following steps: adding a hybrid connection relationship between elements of a calculation model and a physical model on a physical / calculation integrated model of a large power grid, and constructing a hybrid topology model of each future state operation mode; adopting a risk identification algorithm to carry out fault topology analysis on the hybrid topology model of each future state operation mode, and determining a fault risk key index of each fault of each future state operation mode; and determining a risk identification result of each future state operation mode according to the fault risk key index of each fault of each future state operation mode.

Description

Technical Field

[0001] The present invention relates to the technical field of large power grid safety control, and more specifically, to a method and device for risk identification of multi-time scale maintenance plan of large power grid. Background Art

[0002] Grid dispatching and operation are responsible for the important tasks of ensuring the safety of large power grids, fully absorbing new energy, operating the spot market, and coordinating the control of "source, grid, load, and storage". The hierarchical control of grid operation risks combined with maintenance plans is an important part of grid dispatching and operation, and is the main way to ensure the strength of the grid structure and the reliability of power supply. This requires that the formulation of maintenance plans must accurately match the actual operation needs of the grid. The decentralization of grid operation modes will increase rapidly with the increase in the penetration rate of renewable energy, and the consistency of the coupling laws between operation modes and seasons will gradually weaken. The traditional maintenance plan preparation method based on typical operation modes is difficult to adapt to this rapid change. Therefore, it is necessary to use various plans and forecast data to use automated means to construct future state operation modes, and to quickly predict and evaluate the operation of the power system through online analysis and calculation.

[0003] Risk identification is an analysis method that targets a certain power grid operation mode, takes power plants and stations as basic objects, conducts topological analysis on expected faults one by one, and finally identifies power grid risks according to risk control regulations. Risk identification of power grid maintenance plans is crucial for safe power grid control. Currently, risk identification of maintenance plans is carried out manually, and the formation of operation mode risk warning notices is one of the important tasks of the control center system. By identifying the structural risks in the power grid maintenance operation mode, a comprehensive risk analysis and assessment of the operation mode is provided to control personnel, helping power grid control personnel to formulate control measures and requirements in advance to deal with risks, improve the ability to respond to fault situations, and help ensure the safe operation of the power grid and improve the reliability of the power grid. This risk identification method, which is highly dependent on manual work, has the following problems: 1) Lack of actual data fusion, failure to effectively integrate the actual operation plan of the power grid, accurate forecast data and the current power grid connection method, which may cause a large deviation between the identification results and the actual situation; 2) Insufficient analysis of equipment maintenance correlation. When analyzing the maintenance of specific equipment, the superposition effect of other maintenance equipment in the same period is not fully considered, and the potential mutual influence and correlation between equipment may be ignored; 3) Highly dependent on manual experience, this method relies on the professional knowledge and experience of analysts, increasing the risk of human negligence. 4) Inefficiency and high cost. The entire risk identification process takes days, with a long cycle and high resource consumption, which makes it difficult to adapt to the frequent adjustment and optimization needs of the maintenance plan of the new power system power grid. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and device for risk identification of multi-time scale maintenance plans for large power grids.

[0005] According to one aspect of the present invention, a method for risk identification of multi-time scale maintenance plan of a large power grid is provided, comprising:

[0006] Based on all the files of the large power grid, the maintenance plan, power generation plan and bus load forecast of the large power grid are superimposed to form a set of future operation modes;

[0007] Add hybrid connection relationships between computing model and physical model elements to the physical / computing integrated model of the large power grid, and build a hybrid topology model for each future operation mode;

[0008] A risk identification algorithm is used to perform fault topology analysis on the hybrid topology model of each future operation mode, and key fault risk indicators of each fault in each future operation mode are determined;

[0009] According to the key indicators of the failure risk of each failure in each future operation mode, the risk identification result of each future operation mode is determined.

[0010] Optionally, the entire file includes power grid model data; the maintenance plan mainly includes the name, type and maintenance time period of the power outage equipment; the power generation plan includes the power generation output plan curves of thermal power and large hydropower units; the bus load forecast includes the substation bus offline power forecast curve.

[0011] Optionally, a hybrid connection relationship between the computing model and the physical model elements is added to the physical / computing integrated model of the large power grid to construct a hybrid topology model for each future state operation mode, including:

[0012] Equivalently simplify the physical model of the knife switch in the physical model of the large power grid to obtain a simplified physical model;

[0013] Aggregate the computational models in the large power grid according to the interconnected components within the plant and station, and establish virtual topological nodes to obtain a simplified computational model;

[0014] Map and mix the elements of the simplified physical model and the simplified computing model to build a hybrid topology model for each future operating mode.

[0015] Optionally, a risk identification algorithm is used to perform fault topology analysis on the hybrid topology model of each future operation mode to determine the key fault risk indicators of each fault in each future operation mode, including:

[0016] Based on the hybrid topology model of the future operation mode, a risk identification algorithm is used to consider M kinds of expected failure situations for risk identification and determine the number of topology analysis;

[0017] According to the number of topological analysis, topological analysis is performed on the hybrid topological model to determine the key indicators of fault risk of each fault in each future operation mode.

[0018] Optionally, according to the number of topology analyses, a topology analysis is performed on the hybrid topology model to determine the key indicators of the fault risk of each fault in each future state operation mode, including:

[0019] Step 1: According to the graph characteristics of the expected fault conditions of the large power grid, identify and mark the set of failed edges that fail due to the expected fault conditions;

[0020] Step 2: Based on the set of failed edges, construct a set of connected components of the hybrid topology model;

[0021] Step 3: Select any unvisited node in the hybrid topology model as the starting node;

[0022] Step 4: Mark the starting node as visited and add it to the connected component set;

[0023] Step 5: Select all unvisited adjacent nodes of the starting node;

[0024] Step 6: Repeat steps 3 and 4 until all adjacent nodes are marked as visited;

[0025] Step 7: Repeat steps 1 to 5 until all nodes are marked as visited;

[0026] Step 8: Determine the key indicators of fault risk based on the content and quantity of the connected component set.

[0027] Optionally, based on the content and quantity of the connected component set, key indicators of fault risk are determined, including:

[0028] If the number of connected component sets is 1, it indicates that there is no risk of power outage in the large power grid; if the number of connected component sets is greater than 1, it is determined that there is a risk of power outage in the large power grid, and in the case of a power outage risk, the physical devices associated with the nodes in each connected component set are analyzed to determine the power outage devices.

[0029] According to another aspect of the present invention, a large power grid multi-time scale maintenance plan risk identification device is provided, comprising:

[0030] A formation module is used to form a set of future operation modes based on all the files of the large power grid, superimposing the maintenance plan, power generation plan, and bus load forecast of the large power grid;

[0031] A construction module is used to add hybrid connection relationships between computing model and physical model elements to the physical / computing integrated model of the large power grid, and to construct a hybrid topology model for each future state operation mode;

[0032] An analysis module, used to perform fault topology analysis on the hybrid topology model of each future operation mode by using a risk identification algorithm, and determine the key fault risk indicators of each fault in each future operation mode;

[0033] The determination module is used to determine the risk identification result of each future state operation mode according to the key indicators of the failure risk of each failure in each future state operation mode.

[0034] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0035] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0036] Therefore, based on graph theory and the topological characteristics of power grid equipment failures, the present invention proposes a new topological analysis model suitable for rapid risk identification of power grid operation modes, namely a hybrid topological model, which aims to significantly improve the efficiency of topological analysis without sacrificing analysis accuracy, and meet the requirements of online calculation of risk identification of multi-time-scale maintenance plans for large power grids. The multi-time-scale maintenance plan risk identification method for large power grids proposed in the present invention has significant advantages in accuracy and timeliness, and can continuously adjust and verify the risks of multi-time-scale power outage plans for large power grids, and finally obtain a power outage plan that meets the requirements of safe operation of the power grid and fits the actual operation of the power grid, supporting the overall improvement of the intelligent level of power grid maintenance plan compilation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0038] Figure 1 It is a flow chart of a method for risk identification of multi-time scale maintenance plan of a large power grid provided by an exemplary embodiment of the present invention;

[0039] Figure 2a It is a schematic diagram of plant and station physical equipment connection provided by an exemplary embodiment of the present invention;

[0040] Figure 2bIt is a schematic diagram of topology construction for plant external fault risk identification provided by an exemplary embodiment of the present invention;

[0041] Figure 2c It is a schematic diagram of topological construction of internal fault risk identification of plant 1 provided by an exemplary embodiment of the present invention;

[0042] Figure 3 is a risk identification flow chart based on a hybrid topology model provided by an exemplary embodiment of the present invention;

[0043] Figure 4 It is a schematic diagram of generating a future-day operation mode provided by an exemplary embodiment of the present invention;

[0044] Figure 5 It is a schematic diagram of parallel calculation of maintenance mode risk identification provided by an exemplary embodiment of the present invention;

[0045] Figure 6 It is a structural schematic diagram of a large power grid multi-time scale maintenance plan risk identification device provided by an exemplary embodiment of the present invention;

[0046] Figure 7 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0047] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0048] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0049] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0050] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0051] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0052] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0053] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0054] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0055] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0056] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0057] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0058] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0059] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0060] Exemplary Methods

[0061] Figure 1 FIG. 1 is a flow chart of a method for identifying risks of multi-time scale maintenance plans for a large power grid provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the large power grid multi-time scale maintenance plan risk identification method 100 includes the following steps:

[0062] Step 101, based on all the files of the large power grid, the maintenance plan, power generation plan, and bus load forecast of the large power grid are superimposed to form a set of future state operation modes;

[0063] Step 102, adding a hybrid connection relationship between the computing model and the physical model elements to the physical / computing integrated model of the large power grid, and constructing a hybrid topology model of each future state operation mode;

[0064] Step 103, using a risk identification algorithm to perform fault topology analysis on the hybrid topology model of each future operation mode, and determining the key fault risk indicators of each fault in each future operation mode;

[0065] Step 104 , determining a risk identification result of each future operating mode according to the key failure risk indicators of each failure in each future operating mode.

[0066] Specifically, based on graph theory and the topological characteristics of power grid equipment failures, the present invention proposes a new topological analysis model suitable for rapid risk identification of power grid operation modes, and describes the implementation of a risk identification topological analysis algorithm based on the model, aiming to significantly improve the efficiency of topological analysis without sacrificing analysis accuracy, and meet the requirements for online calculation of risk identification of multi-time-scale maintenance plans for large power grids. The multi-time-scale maintenance plan risk identification method for large power grids proposed in the present invention has significant advantages in terms of accuracy and timeliness, and can continuously adjust and verify the risks of multi-time-scale power outage plans for large power grids, and finally obtain a power outage plan that meets the requirements for safe operation of the power grid and fits the actual operation of the power grid, supporting the overall improvement of the intelligent level of power grid maintenance plan compilation.

[0067] Based on graph theory and topological characteristics of power grid equipment failures, this paper proposes a new hybrid topological model suitable for rapid risk identification of power grid operation modes, and introduces a risk identification topological analysis algorithm based on this model. On this basis, by integrating various types of planning data to construct a set of future power grid operation modes, and using parallel computing methods for acceleration, rapid risk identification of maintenance plans with multi-time scales and second-level response is achieved.

[0068] (1) Hybrid topology model

[0069] Breadth-First Search (BFS) and Depth-First Search (DFS) are the core algorithms for exploring graph connectivity in graph theory. Their algorithm complexity is expressed as O(V+E), where V represents the number of vertices in the graph and E represents the number of edges. In order to improve the flexibility and response speed of power grid maintenance plan preparation and realize the rolling deduction of 96-point maintenance plan preparation with a second-level response, improving the efficiency of risk identification calculation is one of the keys. The most direct and effective strategy to improve efficiency is to optimize the representation of the risk identification graph and minimize the number of vertices and edges in the topology graph. Based on the introduction in the previous article, the construction of the topology model is the key to solving the problem. Power grid topology models are mainly divided into two categories: 1) Physical model (node / switch topology), which maps the physical connection relationship of power grid equipment in detail and can fully reflect the system structure, but the number of vertices and edges is too large when constructed. 2) Computational model (busbar / branch topology): In order to alleviate the computational burden brought by the physical model, the computational model constructs the graph by equivalently simplifying components such as switches and switches, effectively reducing the number of vertices and edges in the graph. However, although this simplification improves the computational efficiency, it also sacrifices the ability to identify the risk of faults related to physical devices (such as switches and physical buses). In view of the limitations of the above two models, a simplified knife switch topology model is adopted instead of a simplified switch, which can reduce the number of points and edges and identify the risks of switches and physical buses. This topology model can improve the computational efficiency to a certain extent while ensuring the comprehensiveness of risk identification. This model still basically retains the physical topology characteristics, so it is called a simplified knife switch physical model here.

[0070] Based on an in-depth study of the risk failure characteristics of power grids, the present invention proposes a new hybrid topology model. The hybrid topology model is established through model expansion on the basis of the physical / computational integration model, maintaining the mapping relationship between the physical model and the computational model elements, and adding a hybrid connection relationship between the computational model and the physical model elements for risk identification topology analysis. During the anticipated fault topology analysis, for the plant where the fault is located, the physical model of the plant is adopted in order to retain the physical topological characteristics; for other plants, only the connectivity characteristics between plants need to be retained, so the computational model is adopted. Therefore, if the anticipated fault is inside the plant, the computational model and the physical model of the plant are connected by the "edge" represented by the AC line, which is called a hybrid connection; if the AC line fault is outside the plant, the computational model can be used directly for topological analysis.

[0071] The invention patent simplifies the computational model and the physical model equivalently: 1) The plant station of the physical model adopts a simplified knife switch physical model, aggregates the physical nodes connected by the knife switch, and establishes virtual topological nodes. This method can simplify the model and retain the physical topology; 2) The computational model plant station aggregates the internal connected components of the plant station to establish virtual topological nodes. This method can simplify the model to the greatest extent without changing the topological connectivity of the plant station. The object-oriented topological modeling method based on graph computing provides a solid implementation technical support for the construction of hybrid topological models due to its high readability and powerful abstraction ability.

[0072] In addition, the present invention simplifies the computational model and the physical model equivalently: 1) The plant station of the physical model adopts a simplified knife switch physical model, aggregates the physical nodes connected by the knife switch, and establishes a virtual topology node. This method can simplify the model and retain the physical topology; 2) The computational model plant station aggregates the internal connected components of the plant station to establish a virtual topology node. This method can simplify the model to the greatest extent without changing the topological connectivity of the plant station. The object-oriented topological modeling method based on graph computing provides a solid implementation technical support for building a hybrid topology model due to its high readability and powerful abstraction ability.

[0073] As shown in Figure 2, Figure 2a This is a schematic diagram of the connection of physical equipment in the power plant and substation. The 500 kV busbar of power plant 1 operates in a combined manner, the 500 kV and 220 kV busbars of substation 1 operate in a combined manner, and the 220 kV busbar of substation 2 operates in a divided manner. Figure 2b Schematic diagram of topology construction considering the risk of external faults in power plants. Substation 1 constructs one virtual topology node based on internal connectivity, substation 2 constructs two virtual topology nodes based on internal connectivity, and power plant 1 constructs one virtual topology node based on internal connectivity. Figure 2cIn order to consider the topological construction diagram of the internal fault of power plant 1, power plant 1 is replaced with a simplified physical model to form 7 topological nodes. The design of this topological model takes into account the fault type characteristics of risk identification, that is, the failure set triggered by the internal fault of the plant in risk identification will not cross the plant, so the connectivity inside the non-faulty plant will not change. It is only necessary to use the internal connected components of the plant as topological nodes to construct the topological analysis diagram. This processing method can greatly reduce the complexity of connectivity analysis and greatly improve the calculation efficiency.

[0074] Taking the online data (QS file) of Hunan Power Grid and State Grid on August 21, 2024 as an example, the comparison of the scales of different topological models is shown in Table 1. In the provincial power grid model, the number of vertices of the topological graph constructed by the hybrid topological model is 9‰ of that constructed by the physical model; while in the State Grid power grid model, the number of vertices of the topological graph constructed by the hybrid topological model is 12‰ of that constructed by the physical model.

[0075] Table 1 Comparison of scales of different topological models

[0076]

[0077] (2) Topological analysis algorithm

[0078] The risk identification topology analysis algorithm used in the present invention is based on a hybrid topology model. Its core principle is the same as other topology analysis algorithms. However, when analyzing internal faults in a plant, it is necessary to perform a hybrid connection topology analysis based on the physical equipment wiring conditions of the plant to support all types of connectivity analysis. The present invention uses a depth-first search algorithm to implement risk identification based on a hybrid topology model. The flowchart is as follows: Figure 3 As shown in the figure: 1) According to the graph characteristics of power grid faults, first identify and mark the set of "edges" that have failed due to faults; 2) Create a new connected component set; 3) Select any unvisited node in the graph as the starting node; 4) Mark it as visited and add it to the connected component set; 5) Select all unvisited adjacent nodes of the node; 6) Repeat steps 3 and 4 until all adjacent nodes are marked as visited; 7) Repeat steps 1 to 5 until all nodes are marked as visited; 8) Perform risk judgment based on the number of connected component sets. If the number of connected components is 1, it indicates that there is no power outage risk in the power grid; if the number is greater than 1, it means that there is a power outage risk. At this time, it is necessary to further analyze the physical devices associated with the nodes in each connected component set to determine the specific power outage equipment.

[0079] (3) Generation of future operation modes

[0080] like Figure 4As shown in the figure, based on the full file (QS file), the maintenance plan, power generation plan, and bus load forecast are superimposed to form a set of future state operation modes. The specific data composition is as follows: 1) The QS file contains the power grid model data; 2) The plan for equipment power outage maintenance mainly includes the name, type, and maintenance time period of the power outage equipment; 3) The power generation plan includes the power output plan curves of thermal power and large hydropower units; 4) The bus load forecast includes the offline power forecast curve of the substation bus. Taking the generation of the future state operation mode of the day ahead as an example, the power generation plan and load forecast curve are 96 points at intervals of 15 minutes. Based on these data, 96 power grid operation modes can be constructed.

[0081] (4) Implementation of risk identification algorithm

[0082] The implementation process of multi-time scale maintenance plan risk identification is as follows: Figure 4 As shown. The risk identification of maintenance plans at different time scales is consistent in principle. The present invention takes the risk identification of the operation mode of the day-ahead maintenance plan as an example to illustrate: although the load forecast and power generation plan curves cover a total of 96 different operation scenarios at a time interval of 15 minutes, according to the maintenance interval defined by the actual maintenance plan, usually only 2 to 10 (represented as N) unique grid topologies will be generated. Therefore, it is only necessary to perform anticipated fault analysis on these different topologies, and then combine the power generation plan with the load forecast data to calculate the risk index. This method can significantly reduce the overall computational complexity and amount.

[0083] For each power grid topology, M types of expected fault conditions need to be considered for risk identification, which means that in order to comprehensively and accurately assess the risk, N times M topological analyses need to be performed. In order to improve the efficiency of fault analysis, the fault topological analysis outside the plant is first performed, and then the topological analysis of the internal faults of each plant is performed one by one through hybrid connections. Through topological analysis, the connected components after the fault can be determined, and the information of each connected component can be counted, so as to obtain the key indicators of fault risk of the future operation mode at 96 time points. Finally, the risks are rated according to these key indicators to complete the comprehensive risk identification work.

[0084] (5) Parallel Computing

[0085] The design of parallel algorithms has a crucial impact on the efficiency of parallel computing. Parallel algorithms should minimize data dependencies and communication overhead. Figure 5As shown in the figure, N independent topological graphs are constructed by N maintenance operation modes, and M topological analyses are performed based on each topological graph. The data relied on by the topological analyses carried out on different topological graphs are independent of each other. At the same time, the topological graph objects are guaranteed to be immutable or read-only in the implementation of the topological analysis algorithm. The basic computational complexity of each topological analysis is the same, and it naturally has the characteristics of load balancing. The current multi-core and multi-CPU hardware architecture can be fully utilized through parallel computing technology to minimize the communication overhead in the parallel computing process and achieve efficient acceleration of the computing process. Taking the risk identification of Hunan Power Grid on March 15, 2024 as an example, there are 8 maintenance operation modes and 2681 expected faults. A total of 21448 topological analyses are performed on a test laptop (CPU model is Intel Core i7, main frequency is 2.70GHz, number of cores is 14). The parallel computing method is used, and it takes a total of 0.14 seconds, realizing the rapid risk identification of the day-ahead maintenance plan with second-level response, meeting the actual production and application needs of the provincial power grid.

[0086] The method proposed in the present invention greatly reduces the complexity of connectivity analysis in risk identification, and then accelerates it through parallel computing technology, realizing rapid risk identification of maintenance plans with multi-time scales and second-level response. The performance test of the maintenance plan risk identification method proposed in the present invention is carried out based on the online data of Hunan Power Grid, as follows:

[0087] Test environment: a laptop computer with an Intel Core i7 CPU, a main frequency of 2.70 GHz, and 14 cores.

[0088] Test data: The power grid model is the online model of Hunan Power Grid, which includes about 1,100 power plants and stations. The power generation plan, load forecast, and maintenance plan are the planned data for a certain day in advance.

[0089] The process of risk identification testing includes: constructing all the grid topologies required for the next day based on the maintenance plan of the previous day; then conducting anticipated fault topology analysis for each topology, a total of 2,681 anticipated fault analyses, including 713 line faults, 517 transformer faults, 615 pole-to-pole faults, and 836 busbar faults; finally, calculating risk indicators in combination with power generation plans and load forecast data.

[0090] Table 2 shows the performance test results of the day-ahead risk identification of provincial power grids. For the risk identification of 2681 expected fault analyses of provincial-scale power grids, the risk identification based on the physical topology model takes 108.7 seconds using the serial calculation method, while the risk identification of the hybrid topology model proposed in the present invention only takes 1.1 seconds, which is about 100 times faster. This shows that the method proposed in the present invention can greatly reduce the complexity of the connectivity analysis of large power grids. The parallel computing technology is then used to accelerate the total time consumption of 0.14 seconds. Under the condition of 14-core hardware, the parallel-serial acceleration ratio is about 8, which can effectively solve the computational performance problem of the risk identification of the maintenance mode of large power grids. The performance advantage of the method proposed in the present invention can effectively support the online real-time risk identification of the maintenance plan, can quickly identify the potential risks of the maintenance plan, and provide a basis for the formulation of preventive measures, thereby reducing the risk in the implementation of the power grid maintenance plan. Compared with traditional manual analysis methods, this method can not only reduce the investment of human resources, but also use the real-time data of the power grid operation to make its identification results more in line with the actual operation of the power grid. Therefore, the future development trend will be to gradually shift to the use of fast-response, highly automated risk identification tools to ensure that the power grid can operate safely and stably in a complex and changing environment.

[0091] Table 2 Risk identification performance test results

[0092]

[0093] The method for verifying the correctness of risk identification is to select representative cases based on the results of manual risk identification within a specific time period in 2024, and directly compare and analyze them with the identification results of the method proposed in the present invention. Table 3 shows the correctness verification results of the risk identification of the provincial power grid on the day before. As can be seen from Table 3, the method proposed in the present invention can correctly identify both inherent and planned risks, and the risk identification accuracy is the same as that of the risk identification based on the physical topology model. In addition, the present invention also tests and verifies structural risks other than the above-mentioned representative risks, and the results show that they can all be accurately identified.

[0094] Table 3 Comparison between risk identification based on hybrid topological model and manual identification

[0095]

[0096] Therefore, based on graph theory and the topological characteristics of power grid equipment failures, the present invention proposes a new topological analysis model suitable for rapid risk identification of power grid operation modes, namely a hybrid topological model, which aims to significantly improve the efficiency of topological analysis without sacrificing analysis accuracy, and meet the requirements of online calculation of risk identification of multi-time-scale maintenance plans for large power grids. The multi-time-scale maintenance plan risk identification method for large power grids proposed in the present invention has significant advantages in accuracy and timeliness, and can continuously adjust and verify the risks of multi-time-scale power outage plans for large power grids, and finally obtain a power outage plan that meets the requirements of safe operation of the power grid and fits the actual operation of the power grid, supporting the overall improvement of the intelligent level of power grid maintenance plan compilation.

[0097] Exemplary Devices

[0098] Figure 6 FIG. 1 is a schematic diagram of a large power grid multi-time scale maintenance plan risk identification device provided by an exemplary embodiment of the present invention. Figure 6 As shown, the device 600 includes:

[0099] A forming module 610 is used to form a set of future state operation modes based on all files of the large power grid and superimpose the maintenance plan, power generation plan, and bus load forecast of the large power grid;

[0100] A construction module 620 is used to add a hybrid connection relationship between the computing model and the physical model elements to the physical / computing integrated model of the large power grid to construct a hybrid topology model of each future state operation mode;

[0101] An analysis module 630 is used to perform a fault topology analysis on the hybrid topology model of each future operation mode by using a risk identification algorithm to determine a key fault risk indicator of each fault in each future operation mode;

[0102] The determination module 640 is used to determine the risk identification result of each future state operation mode according to the key fault risk indicators of each fault in each future state operation mode.

[0103] Optionally, the full file includes power grid model data; the maintenance plan mainly includes the name, type and maintenance time period of the power outage equipment; the power generation plan includes the power generation output plan curves of thermal power and large hydropower units; the bus load forecast includes the substation bus offline power forecast curve.

[0104] Optionally, the building block 620 includes:

[0105] A simplification submodule, used to perform equivalent simplification on the physical model of the knife switch in the physical model of the large power grid to obtain a simplified physical model;

[0106] An aggregation submodule is used to aggregate the computing model in the large power grid according to the internal connected components of the plant and station, establish a virtual topology node to obtain a simplified computing model;

[0107] The construction submodule is used to map and hybridly connect the elements of the simplified physical model and the simplified computing model, and construct the hybrid topology model of each future state operation mode.

[0108] Optionally, the analysis module 630 includes:

[0109] An identification submodule is used for performing risk identification based on a hybrid topology model of a future-state operation mode by using the risk identification algorithm to consider M types of anticipated failure situations and determine the number of topology analyses;

[0110] The analysis submodule is used to perform topology analysis on the hybrid topology model according to the number of topology analyses, and determine the key indicators of the fault risk of each fault in each future operation mode.

[0111] Optionally, the determination module 640 includes:

[0112] Step 1: According to the graph characteristics of the expected fault conditions of the large power grid, identify and mark the set of failed edges that fail due to the expected fault conditions;

[0113] Step 2: Based on the set of failed edges, construct a set of connected components of the hybrid topology model;

[0114] Step 3: Select any unvisited node in the hybrid topology model as the starting node;

[0115] Step 4: Mark the starting node as visited and add it to the connected component set;

[0116] Step 5: Select all unvisited adjacent nodes of the starting node;

[0117] Step 6: Repeat steps 3 and 4 until all adjacent nodes are marked as visited;

[0118] Step 7: Repeat steps 1 to 5 until all nodes are marked as visited;

[0119] Step 8: Determine the key indicators of fault risk based on the content and quantity of the connected component set.

[0120] Optionally, based on the content and quantity of the connected component set, key indicators of fault risk are determined, including:

[0121] If the number of connected component sets is 1, it indicates that there is no risk of power outage in the large power grid; if the number of connected component sets is greater than 1, it is determined that there is a risk of power outage in the large power grid, and in the case of a power outage risk, the physical devices associated with the nodes in each connected component set are analyzed to determine the power outage devices.

[0122] Exemplary Electronic Devices

[0123] Figure 7 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 7 As shown, the electronic device 70 includes one or more processors 71 and a memory 72 .

[0124] The processor 71 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0125] The memory 72 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 71 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 73 and an output device 74, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0126] In addition, the input device 73 may also include, for example, a keyboard, a mouse, and the like.

[0127] The output device 74 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0128] Of course, to simplify, Figure 7 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0129] Exemplary computer program products and computer-readable storage media

[0130] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0131] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0132] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0133] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0134] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0135] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0136] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0137] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0138] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0139] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for risk identification of multi-time scale maintenance plan for large power grid, characterized in that: include: Based on all the files of the large power grid, a set of future operation modes are formed by superimposing the maintenance plan, power generation plan, and bus load forecast of the large power grid; Adding a hybrid connection relationship between the computing model and the physical model elements to the physical / computing integrated model of the large power grid to construct a hybrid topology model for each future state operation mode; A risk identification algorithm is used to perform fault topology analysis on the hybrid topology model of each future operation mode, and key fault risk indicators of each fault in each future operation mode are determined; According to the key fault risk indicators of each fault in each future operating mode, a risk identification result of each future operating mode is determined.

2. The method according to claim 1, characterized in that The complete file includes power grid model data; the maintenance plan mainly includes the name, type and maintenance time period of the power outage equipment; the power generation plan includes the power generation output plan curves of thermal power and large hydropower units; the bus load forecast includes the substation bus offline power forecast curve.

3. The method according to claim 1, characterized in that Adding a hybrid connection relationship between the computing model and the physical model elements to the physical / computing integrated model of the large power grid, and constructing a hybrid topology model for each future state operation mode, including: Equivalently simplifying the physical model of the knife switch in the physical model of the large power grid to obtain a simplified physical model; Aggregating the computing model in the large power grid according to the internal connected components of the plant and station, establishing a virtual topology node to obtain a simplified computing model; Mapping and hybrid connection are performed between elements of the simplified physical model and the simplified computing model to construct the hybrid topology model of each future state operation mode.

4. The method according to claim 1, characterized in that The risk identification algorithm is used to perform fault topology analysis on the hybrid topology model of each future operation mode, and the key indicators of fault risk of each fault in each future operation mode are determined, including: Based on the hybrid topology model of the future operation mode, the risk identification algorithm is used to consider M kinds of expected failure situations for risk identification, and the number of topology analysis is determined; According to the number of topology analyses, a topology analysis is performed on the hybrid topology model to determine a key indicator of the fault risk of each fault in each future operation mode.

5. The method according to claim 4, characterized in that According to the number of topological analyses, a topological analysis is performed on the hybrid topological model to determine the key indicators of the fault risk of each fault in each future operation mode, including: Step 1: According to the graph characteristics of the expected fault conditions of the large power grid, identify and mark the set of failed edges that fail due to the expected fault conditions; Step 2: constructing a connected component set of the hybrid topology model according to the failed edge set; Step 3: Select any unvisited node in the hybrid topology model as the starting node; Step 4: Mark the starting node as visited and add it to the connected component set; Step 5: Select all unvisited adjacent nodes of the starting node; Step 6: Repeat steps 3 and 4 until all adjacent nodes are marked as visited; Step 7: Repeat steps 1 to 5 until all nodes are marked as visited; Step 8: Determine the key indicators of fault risk based on the content and quantity of the connected component set.

6. The method according to claim 5, characterized in that According to the content and quantity of the connected component set, key indicators of fault risk are determined, including: If the number of the connected component sets is 1, it indicates that the large power grid has no power outage risk; if the number of the connected component sets is greater than 1, it is determined that the large power grid has a power outage risk, and when there is a power outage risk, the physical devices associated with the nodes in each connected component set are analyzed to determine the power outage devices.

7. A large power grid multi-time scale maintenance plan risk identification device, characterized in that: include: A formation module is used to form a set of future state operation modes based on all files of the large power grid and superimpose the maintenance plan, power generation plan, and bus load forecast of the large power grid; A construction module is used to add a hybrid connection relationship between the computing model and the physical model elements to the physical / computing integrated model of the large power grid, and to construct a hybrid topology model of each future state operation mode; An analysis module, used to perform fault topology analysis on the hybrid topology model of each future operation mode by using a risk identification algorithm, and determine the key fault risk indicators of each fault in each future operation mode; The determination module is used to determine the risk identification result of each future operation mode according to the key fault risk indicators of each fault in each future operation mode.

8. The device according to claim 7, characterized in that The complete file includes power grid model data; the maintenance plan mainly includes the name, type and maintenance time period of the power outage equipment; the power generation plan includes the power generation output plan curves of thermal power and large hydropower units; the bus load forecast includes the substation bus offline power forecast curve.

9. The device according to claim 7, characterized in that Building blocks, including: A simplification submodule, used to perform equivalent simplification on the physical model of the knife switch in the physical model of the large power grid to obtain a simplified physical model; An aggregation submodule is used to aggregate the computing model in the large power grid according to the internal connected components of the plant and station, establish a virtual topology node to obtain a simplified computing model; The construction submodule is used to map and hybridly connect the elements of the simplified physical model and the simplified computing model, and construct the hybrid topology model of each future state operation mode.

10. The device according to claim 7, characterized in that Analysis modules, including: An identification submodule is used for performing risk identification based on a hybrid topology model of a future-state operation mode by using the risk identification algorithm to consider M types of anticipated failure situations and determine the number of topology analyses; The analysis submodule is used to perform topology analysis on the hybrid topology model according to the number of topology analyses, and determine the key indicators of the fault risk of each fault in each future operation mode.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

12. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 6.

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