Temporary stability result tracing method and system for large power grid

Abstract

The invention discloses a transient stability result tracing method and system for a large power grid, and belongs to the technical field. The method comprises the following steps: establishing a hierarchical list of key influence factors influencing a transient stability result based on historical user behavior data of a large power grid; on the basis of the key influence factor hierarchical list, constructing a transient stability result tracing factor knowledge graph; based on the transient stability result traceability knowledge graph, constructing a transient stability result traceability analysis framework; and on the basis of the transient stability result factor tracing analysis framework, factor tracing is carried out on the transient stability result of the large power grid according to the user behavior data of the large power grid. The transient stability tracing analysis method is applied to the field of power grid simulation analysis, transient stability tracing analysis based on simulation calculation can be achieved, and a certain application basis is provided for power grid simulation analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of large power grid operation analysis, and more specifically, to a method and system for tracing temporary stability results of a large power grid. Background Art

[0002] With the rapid development and construction of new power systems, new energy, pumped storage, energy storage and other equipment are connected to the grid on a large scale, making the operation mode of the power grid flexible and changeable, and the operation characteristics and dispatching control of the power grid more complex. At the same time, the lean requirements for power grid safety and stability analysis are gradually increasing, and the intensity and workload of simulation analysis are increasing day by day.

[0003] Since large power grid simulation calculations involve many parameters and the analysis of factors affecting the results is complex, the traditional reliance on operators to quickly adjust the calculation operation mode and quickly identify transient results is difficult to achieve. Transient calculation is an important calculation for mode operation analysis. In the entire power system, transient calculation has the characteristics of large amount of calculation and complex analysis. It is heavily dependent on the experience and level of experts and is time-consuming and labor-intensive. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method for tracing the cause of temporary stability results of a large power grid, comprising:

[0005] Based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established;

[0006] Based on the hierarchical list of key influencing factors, a knowledge graph for tracing the cause of temporary stable results is constructed;

[0007] Based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed;

[0008] Based on the temporary stability result tracing analysis framework, the temporary stability result of the large power grid is traced according to the user behavior data of the large power grid.

[0009] Optionally, based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including:

[0010] Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

[0011] Optional, historical user behavior data, including:

[0012] Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

[0013] Optional preprocessing includes cleaning and filtering historical user behavior data.

[0014] Optionally, construct a knowledge graph for tracing temporary stable results, including:

[0015] Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

[0016] Optionally, a triple consists of a node, a relationship, and a node.

[0017] Optional, nodes, including:

[0018] State nodes, feature nodes, function nodes, weight nodes, and description nodes.

[0019] Optional, relations, including:

[0020] A direct logical relationship between two nodes.

[0021] Optionally, based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed, including:

[0022] Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships;

[0023] The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

[0024] Optionally, tracing the cause of the temporary stability result of the large power grid includes:

[0025] Find the cause from the result;

[0026] For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found;

[0027] The method of finding the cause from the result includes:

[0028] Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results;

[0029] The step of determining the weight of the key influencing factors corresponding to the found causes includes:

[0030] Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action;

[0031] The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

[0032] On the other hand, the present invention also proposes a temporary stability result tracing system for a large power grid, comprising:

[0033] A list building unit is used to build a hierarchical list of key influencing factors affecting temporary stability results based on historical user behavior data of the large power grid;

[0034] A knowledge graph construction unit, used to construct a knowledge graph for tracing the cause of a temporary stable result based on the hierarchical list of key influencing factors;

[0035] An analysis framework building unit, used to build a tentatively stable result tracing analysis framework based on the tentatively stable result tracing knowledge graph;

[0036] The analysis unit is used to trace the cause of the temporary stability result of the large power grid based on the temporary stability result tracing analysis framework and according to the user behavior data of the large power grid.

[0037] Optionally, based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including:

[0038] Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

[0039] Optional, historical user behavior data, including:

[0040] Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

[0041] Optional preprocessing includes cleaning and filtering historical user behavior data.

[0042] Optionally, construct a knowledge graph for tracing temporary stable results, including:

[0043] Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

[0044] Optionally, a triple consists of a node, a relationship, and a node.

[0045] Optional, nodes, including:

[0046] State nodes, feature nodes, function nodes, weight nodes, and description nodes.

[0047] Optional, relations, including:

[0048] A direct logical relationship between two nodes.

[0049] Optionally, based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed, including:

[0050] Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships;

[0051] The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

[0052] Optionally, tracing the cause of the temporary stability result of the large power grid includes:

[0053] Find the cause from the result;

[0054] For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found;

[0055] The method of finding the cause from the result includes:

[0056] Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results;

[0057] The step of determining the weight of the key influencing factors corresponding to the found causes includes:

[0058] Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action;

[0059] The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

[0060] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0061] a processor for executing one or more programs;

[0062] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0063] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention provides a method for tracing the cause of temporary stability results of a large power grid, comprising: establishing a hierarchical list of key influencing factors affecting temporary stability results based on historical user behavior data of the large power grid; constructing a knowledge graph for tracing the cause of temporary stability results based on the hierarchical list of key influencing factors; constructing a framework for tracing the cause of temporary stability results based on the knowledge graph for tracing the cause of temporary stability results; tracing the cause of temporary stability results of the large power grid based on the framework for tracing the cause of temporary stability results according to the user behavior data of the large power grid. The present invention can establish a framework for tracing the cause of temporary stability results by establishing a knowledge graph, and the analysis framework can effectively trace the cause of temporary stability results of the large power grid, with small amount of calculation and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flow chart of the method of the present invention;

[0067] Figure 2 is a flow chart of an embodiment of the method of the present invention;

[0068] Figure 3 A fishbone analysis diagram of an embodiment of the method of the present invention;

[0069] Figure 4 A knowledge description diagram of simulation data modification and calculation in an embodiment of the method of the present invention;

[0070] Figure 5 It is a causal reasoning analysis diagram of a temporary stable result based on a knowledge graph in an embodiment of the method of the present invention;

[0071] Figure 6 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0072] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0073] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0074] Embodiment 1:

[0075] The present invention proposes a method for tracing the cause of temporary stability results of a large power grid, such as Figure 1 As shown, including:

[0076] Step 1: Based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established;

[0077] Step 2: Based on the hierarchical list of key influencing factors, a knowledge graph of tracing the causes of temporary stable results is constructed;

[0078] Step 3: Based on the temporary stable result tracing knowledge graph, a temporary stable result tracing analysis framework is constructed;

[0079] Step 4: Based on the temporary stability result tracing analysis framework, trace the temporary stability result of the large power grid according to the user behavior data of the large power grid.

[0080] Among them, based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including:

[0081] Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

[0082] Among them, historical user behavior data includes:

[0083] Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

[0084] Among them, preprocessing includes: cleaning and screening historical user behavior data.

[0085] Among them, the construction of the knowledge graph of temporary stable result tracing includes:

[0086] Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

[0087] Among them, a triple is composed of a node, a relationship and a node.

[0088] The nodes include:

[0089] State nodes, feature nodes, function nodes, weight nodes, and description nodes.

[0090] Among them, the relationship includes:

[0091] A direct logical relationship between two nodes.

[0092] Among them, based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed, including:

[0093] Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships;

[0094] The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

[0095] The causes of the temporary stability of the large power grid include:

[0096] Find the cause from the result;

[0097] For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found;

[0098] The method of finding the cause from the result includes:

[0099] Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results;

[0100] The step of determining the weight of the key influencing factors corresponding to the found causes includes:

[0101] Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action;

[0102] The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

[0103] The present invention will be further described below in conjunction with the embodiments of the present invention:

[0104] Implementation steps: Figure 2 As shown, including:

[0105] Organize a hierarchical list of key influencing factors,

[0106] Construction of knowledge graph for tracing temporary stable calculation results,

[0107] The automatic reasoning framework for tracing the results of temporary stable calculations is constructed.

[0108] Intelligent tracing analysis system for temporary stability calculation results.

[0109] First, the historically accumulated user behavior data is obtained through the database, including basic data parameters, steady-state data, dynamic data, calculation procedures, adjustment measures, and adjustment behavior logs. After data cleaning and screening, the user's temporary stability calculation data is obtained. Through previous historical calculation examples, the scenes of undisturbed anomalies and temporary stability instability are collected, and the factors of different levels that affect the temporary stability results are sorted out to form a hierarchical list of key influencing factors.

[0110] Secondly, based on the hierarchical list of key influencing factors mentioned above, a method for constructing a knowledge graph for causal analysis of temporary stable results is proposed;

[0111] Secondly, based on the temporary stable traceability knowledge graph, a temporary stable result traceability analysis framework is constructed;

[0112] Finally, based on the research on the tracing analysis framework of temporary stability results and the verification of the comprehensive stability program, an intelligent tracing analysis system for temporary stability calculation results was developed.

[0113] The analysis result file is used as part of the safety and stability control strategy for reference by operators. The knowledge graph of the temporary stability result traceability analysis is modeled as follows: Figure 3 As shown,

[0114] First, the knowledge of temporary stable result attribution is organized according to the triple grammar; secondly, the logic and process of attribution analysis are decomposed into multiple states, each state corresponds to multiple operations, and the execution order of different operations is determined according to the weight. Finally, the temporary stable attribution knowledge triples are imported into the neo4j graph database to form a temporary stable attribution knowledge base, and neo4j is used for knowledge storage and management. Among them, the framework of temporary stable result attribution analysis is as follows Figure 4 , Figure 5 As shown in the figure, first, the current analysis state is determined through the initial temporary stability calculation; secondly, the knowledge based on the knowledge graph can intelligently guide the machine to realize the automatic execution or automatic adjustment of the machine in a specific scenario (such as power grid flow adjustment), and finally, the temporary stability calculation is performed again to determine the latest analysis state and determine whether new adjustments are needed. Through the above research, for the purpose of automatic reasoning analysis, the task execution relationship such as the knowledge scenario in the knowledge graph and the corresponding state of the scenario, state transition conditions, and actions that can be taken under the state are modeled, and corresponding to the data modification process, the temporary stability result traceability analysis framework based on task flow and knowledge graph is completed.

[0115] The knowledge graph of temporary and stable causal analysis is constructed, including:

[0116] The key influencing factors, rules and regulations to be followed, and effective control measures in the analysis process are expressed in the form of triples as a data structure that is easy for computers to store and use. The triple is composed of <node, relationship, node>. Nodes include: state nodes, feature nodes, function nodes, weight nodes, description nodes, etc.; Relationships: connect two nodes in the knowledge graph to indicate the direct logical relationship between the two nodes. Including: has features, operation, is, call, has attributes, has weight, etc.

[0117] Taking temporary stability calculation analysis as an example, the temporary stability analysis knowledge triples are imported into the neo4j graph database to form a temporary stability analysis knowledge base, which is displayed in the form of a knowledge graph.

[0118] Abductive analysis construction methods include:

[0119] Abductive analysis can be described as two basic steps: finding the cause from the result and determining the weight of each possible cause.

[0120] Step 1: Find the cause from the result (find the set of influencing factors)

[0121] According to the three elements of causal analysis, causal analysis can be divided into three categories: inferring results from causes (also known as causal inference), finding causes from results, and inferring each other between causes and results.

[0122] The second category is the causal analysis of the key influencing factors of the temporary stability results. It is a causal analysis method that finds the cause from the result. For example, if the temporary stability curve result is abnormal, find the cause of the abnormality. The typical method is the fishbone diagram analysis method.

[0123] The fishbone diagram analysis method is to list all influencing factors of a problem by category and exhaustively for further analysis. The fish head is the result (problem), the big fishbone is the category of cause, and the small fishbone is the specific cause. Fishbone diagram analysis is suitable for finding multiple possible causes.

[0124] Step 2: Determine the weight (significance) of each influencing factor

[0125] The task of this step of causal analysis is to evaluate the impact of a certain strategy or action on a certain outcome indicator we are concerned about, and then to infer which strategies or actions trigger certain key changes in the outcome indicator we are concerned about.

[0126] In this process, we can only infer conclusions from the observed data, but the observed data itself is biased. We cannot know whether the observed data has specific strategy influences that only belong to the observed part, so we need to use methods to eliminate this bias.

[0127] The present invention combines the actual business scenarios of transient stability calculation of large power grids, and provides an automatic reasoning and analysis method for tracing the causes of transient stability, including uneven transient stability undisturbed calculation curve results caused by parameter filling, abnormal transient stability calculation caused by unreasonable fault settings, abnormal changes in power grid state quantities during transient stability calculation due to static or dynamic parameter problems, and transient stability results that are inconsistent with expectations or cause system instability due to multi-level factors such as equipment parameters, controller action events, and system stability characteristics.

[0128] Embodiment 2:

[0129] The present invention also proposes a temporary stability result tracing system 200 for a large power grid, such as Figure 6 As shown, including:

[0130] A list building unit 201 is used to build a hierarchical list of key influencing factors affecting temporary stability results based on historical user behavior data of the large power grid;

[0131] A knowledge graph construction unit 202, configured to construct a knowledge graph for tracing the cause of a temporary stable result based on the hierarchical list of key influencing factors;

[0132] An analysis framework building unit 203 is used to build a tentatively stable result tracing analysis framework based on the tentatively stable result tracing knowledge graph;

[0133] The analysis unit 204 is used to trace the cause of the temporary stability result of the large power grid based on the temporary stability result tracing analysis framework and according to the user behavior data of the large power grid.

[0134] Among them, based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including:

[0135] Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

[0136] Among them, historical user behavior data includes:

[0137] Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

[0138] Among them, preprocessing includes: cleaning and screening historical user behavior data.

[0139] Among them, the construction of the knowledge graph of temporary stable result tracing includes:

[0140] Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

[0141] Among them, a triple is composed of a node, a relationship and a node.

[0142] The nodes include:

[0143] State nodes, feature nodes, function nodes, weight nodes, and description nodes.

[0144] Among them, the relationship includes:

[0145] A direct logical relationship between two nodes.

[0146] Among them, based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed, including:

[0147] Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships;

[0148] The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

[0149] The causes of the temporary stability of the large power grid include:

[0150] Find the cause from the result;

[0151] For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found;

[0152] The method of finding the cause from the result includes:

[0153] Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results;

[0154] The step of determining the weight of the key influencing factors corresponding to the found causes includes:

[0155] Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action;

[0156] The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

[0157] The present invention is applied in the field of power grid simulation analysis and can realize temporary stability tracing analysis based on simulation calculation, and has a certain application basis in power grid simulation analysis.

[0158] Embodiment 3:

[0159] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0160] Embodiment 4:

[0161] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0162] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0167] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for tracing the cause of temporary stability results of a large power grid, characterized in that: include: Based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established; Based on the hierarchical list of key influencing factors, a knowledge graph for tracing the cause of temporary stable results is constructed; Based on the tentatively stable result tracing knowledge graph, a tentatively stable result tracing analysis framework is constructed; Based on the temporary stability result tracing analysis framework, the temporary stability result of the large power grid is traced according to the user behavior data of the large power grid.

2. The method for tracing the cause of a temporary stable result according to claim 1, characterized in that: Based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including: Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

3. The method for tracing the cause of a temporary stable result according to claim 2, characterized in that: The historical user behavior data includes: Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

4. The method for tracing the cause of a temporary stable result according to claim 2, characterized in that: The preprocessing includes: cleaning and screening the historical user behavior data.

5. The method for tracing the cause of a temporary stable result according to claim 1, characterized in that: The construction of the temporary stable result tracing knowledge graph includes: Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

6. The method for tracing the cause of a temporary stable result according to claim 5, characterized in that: The triple is composed of a node, a relationship and a node.

7. The method for tracing the cause of a temporary stable result according to claim 6, characterized in that: The node comprises: State nodes, feature nodes, function nodes, weight nodes, and description nodes.

8. The method for tracing the cause of a temporary stable result according to claim 6, characterized in that: The relationship includes: A direct logical relationship between two nodes.

9. The method according to claim 1, characterized in that: The construction of a tentatively stable result tracing analysis framework based on the tentatively stable result tracing knowledge graph includes: Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships; The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

10. The method for tracing the cause of a temporary stable result according to claim 1, characterized in that: The tracing of the temporary stability result of the large power grid includes: Find the cause from the result; For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found; The method of finding the cause from the result includes: Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results; The step of determining the weight of the key influencing factors corresponding to the found causes includes: Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action; The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

11. A system for tracing the cause of temporary stability results in a large power grid, characterized in that: include: A list building unit is used to build a hierarchical list of key influencing factors affecting temporary stability results based on historical user behavior data of the large power grid; A knowledge graph construction unit, used to construct a knowledge graph for tracing the cause of a temporary stable result based on the hierarchical list of key influencing factors; An analysis framework building unit, used to build a tentatively stable result tracing analysis framework based on the tentatively stable result tracing knowledge graph; The analysis unit is used to trace the cause of the temporary stability result of the large power grid based on the temporary stability result tracing analysis framework and according to the user behavior data of the large power grid.

12. The temporary stable result tracing system according to claim 11, characterized in that: Based on the historical user behavior data of the large power grid, a hierarchical list of key factors affecting the temporary stability results is established, including: Obtain historical user behavior data of the large power grid, preprocess the historical user behavior data to obtain user temporary stability calculation data, obtain historical calculation examples of the large power grid, determine the key influencing factors of different levels that affect temporary stability results in the scenarios of no disturbance anomaly and temporary stability and instability based on the historical calculation examples, associate the temporary stability calculation data with the key influencing factors, and generate a hierarchical list of key influencing factors.

13. The temporary stable result tracing system according to claim 12, characterized in that: The historical user behavior data includes: Basic parameter data, steady-state data, dynamic data, calculation program data, adjustment measure data and adjustment behavior log data.

14. The temporary stable result tracing system according to claim 12, characterized in that: The preprocessing includes: cleaning and screening the historical user behavior data.

15. The temporary stable result tracing system according to claim 11, characterized in that: The construction of the temporary stable result tracing knowledge graph includes: Based on the hierarchical list of key influencing factors, the key influencing factors, the rules and regulations to be followed, and the control measures are represented in the form of triples in the form of data structures that can be stored and utilized by computers to generate a knowledge graph for tracing temporary stable results.

16. The temporary stable result tracing system according to claim 15, characterized in that: The triple is composed of a node, a relationship and a node.

17. The temporary stable result tracing system according to claim 16, characterized in that: The node comprises: State nodes, feature nodes, function nodes, weight nodes, and description nodes.

18. The temporary stable result tracing system according to claim 16, characterized in that: The relationship includes: A direct logical relationship between two nodes.

19. The system according to claim 11, characterized in that The construction of a tentatively stable result tracing analysis framework based on the tentatively stable result tracing knowledge graph includes: Determine the knowledge scenarios in the temporary stable result tracing knowledge graph and the task execution relationships corresponding to the knowledge scenarios, and construct a temporary stable result tracing analysis framework based on the knowledge scenarios and the corresponding task execution relationships; The task execution relationship corresponding to the knowledge scenario includes: large power grid state, state transition conditions and actions taken under the state.

20. The temporary stable result tracing system according to claim 11, characterized in that: The tracing of the temporary stability result of the large power grid includes: Find the cause from the result; For the reasons found, determine the weights of the key influencing factors corresponding to the reasons found; The method of finding the cause from the result includes: Based on the fishbone analysis method, the results are categorized and all influencing factors are exhaustively listed, and all the influencing factors are used as the causes corresponding to the results; The step of determining the weight of the key influencing factors corresponding to the found causes includes: Determine the impact of the strategy or action executed by the large power grid on the result indicator through the key influencing factors, determine the key strategy or action corresponding to the key change of the result indicator based on the impact, and determine the weight of the key influencing factor corresponding to the cause through the determined key strategy or action; The weights of the key influencing factors corresponding to the reasons found are used to complete the tracing of the temporary stability results of the large power grid.

21. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 10 is implemented.

22. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 10 is implemented.

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