[0014] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation modes and specific operating procedures are given, but the scope of protection of the present invention is not limited to the following Mentioned examples.
[0015] Such as figure 1 As shown, the system of this embodiment uses a microcomputer and a high-performance computer as a platform, and a computing resource monitoring terminal is introduced between the server and the client to perform computing resource monitoring and computing task scheduling, including: server, client and computing resources On the monitoring side, the server side obtains real-time power system data from the energy management system and stores it in the database server. The data resources of the database server are sent out in two ways, one is sent to the computing engine server, and the other is sent to the computing engine client. Data mapping for offline calculation Module, server-side computing engine server computing resource data is directly input to the computing resource monitoring terminal. On the one hand, the client terminal is connected to the server terminal for two-way data exchange, and on the other hand, it is connected to the computing resource monitoring terminal to realize two-way data transmission and computing resource monitoring. The end is connected with the server end and the client end respectively for two-way data interaction.
[0016] The following is an explanation of the above-mentioned components:
[0017] 1. Server side
[0018] The server side includes three sub-modules: a database server, a parallel computing engine server and a web server. The database server obtains real-time power system data from the energy management system, and the processed data is connected to the parallel computing engine server for two-way data exchange, and the calculation results of the parallel computing engine are used as the input of the web server.
[0019] The parallel computing engine server is composed of several computing engine servers. Each computing engine server includes six sub-modules: network invulnerability, network availability, network carrying capacity, node availability, line availability and unit availability. Among them, the three sub-modules of network survivability, network availability and network carrying capacity analyze the vulnerability of the power system from the network topology layer, while node availability, line availability and unit availability analyze the vulnerability of the power system from the infrastructure layer.
[0020] 2. Client side
[0021]The client terminal includes two sub-modules: a parallel computing engine client and an auxiliary service function module. In the online computing mode, the parallel computing engine client obtains the information of the computing engine server through the connected computing resource monitoring terminal; in the offline computing mode, the parallel computing engine client obtains the computing engine client information through the connected computing resource monitoring terminal . The auxiliary service function module mainly completes some other auxiliary computing services.
[0022] The parallel computing engine client is composed of several computing engine clients, and each computing engine client is divided into two working modes: online computing and offline computing. In the online computing mode, the computing engine client includes three sub-modules: task submission, status monitoring and result viewing. The task submission module provides a remote task submission portal for online calculations. The task submission module sends the calculation tasks to the server and computing resource monitoring ends, and connects to the status monitoring module to monitor the status of the calculation tasks. The result viewing module displays the results according to the status monitoring. . In offline computing mode, the computing engine client includes three sub-computing modules: data mapping, offline analysis, and result publishing. The data mapping module obtains offline analysis data from the server and maps it to the local computing engine client. The mapped data is used as the input of the offline analysis module. The offline analysis module performs offline analysis based on the results of the data mapping module, and the results are directly output to the result release Module, the result release module will release the results in time through the computer network according to the calculation results of the offline analysis module. The auxiliary service function module mainly completes some other auxiliary computing services.
[0023] 3. Computing resource monitoring terminal,
[0024] The computing resource monitoring terminal includes four sub-modules: computing resource monitoring, computing resource database, computing resource performance prediction, and computing task scheduling. The computing resource monitoring module obtains computing engine server information from the server on the one hand, and computing engine client information from the client on the other hand. The computing resource monitoring results are stored in the computing resource database, and the computing resource database processes the data as calculations. The input signal of resource performance prediction. The computing resource performance prediction module inputs the prediction results into the computing task scheduling module. The computing task scheduling module performs computing task scheduling according to the computing resource performance prediction results, and the results are sent to the parallel computing engine server module and parallelization. Computing engine client module.
[0025] The server side, the client side, and the computing resource monitoring terminal described in this embodiment are in a distributed environment, and can be interconnected through a local area network or a wide area network, and follow a unified communication protocol, and can communicate with each other and exchange information. When the power system dynamic safety assessment and early warning simulation starts, the server is first started to obtain real-time power system data from the energy management system, and the working mode is selected through the client. In the online computing mode, the computing engine client submits online computing tasks to the computing resource monitoring terminal. The computing resource monitoring terminal performs computing task scheduling according to the status of the computing engine server and the characteristics of the computing tasks, and the task scheduling results are returned to each computing engine server for execution. The calculation engine server performs online calculations based on the database server data and task scheduling results, and returns the real-time calculation status to the state monitoring module of the calculation engine client. After the calculation is completed, the calculation engine server submits the results to the web server for publication for the client side results The viewing module views the calculation results through the computer network. In the offline computing mode, the computing engine client obtains offline computing data from the database server on the server side and submits offline computing tasks to the computing resource monitoring terminal. The computing resource monitoring terminal performs computing task scheduling based on the status of the computing engine client and the characteristics of the computing tasks. The task scheduling results are returned to each computing engine client for execution, and each computing engine client uses offline analysis tools to analyze the vulnerability of the power system according to the data of the data mapping module and the task scheduling results, and submits the offline analysis results to the web server through the web page.
[0026] In this embodiment, a modular approach is adopted to perform dynamic security assessment and early warning of the power system from the network topology layer and the infrastructure layer. At the network topology layer, the indicators for evaluating the vulnerability of the power system include three aspects: network survivability, network availability, and network carrying capacity. Among them, network invulnerability refers to the ability of the network to maintain connectivity when the network is damaged by external attacks in a network with a completely determined topology. Commonly used indicators are network connectivity and network cohesion; network availability is Describe the ability of the network to perform the required energy flow function at any time within the specified time under the condition that external resources are available, that is, the availability of the network. This indicator is quantitatively measured by the availability of the network; it is a measure of the network carrying capacity The indicators mainly include: the maximum flow capacity between node pairs, the line impedance and flow rate with the greatest availability between node pairs, and the shortest path impedance and flow rate between node pairs. These indicators can be reflected in the emergency state, the power system The maximum energy flow that can be withstood, the safest transmission route for emergency energy flow, and the smallest transmission cost. At the infrastructure layer, the indicators for evaluating the vulnerability of the power system include three aspects: node availability, line availability, and unit availability. Among them, node availability indicators mainly include safe completion of energy flow rate, maximum throughput and saturation; line availability indicators mainly include the maximum theoretical transmission capacity of the line, line integrity, availability, impedance, etc.; as the generation, transformation and consumption of energy flow The availability indicators of other unit equipment mainly include maximum carrying capacity and safety performance.
[0027] This embodiment proposes an extensible computing resource monitoring and computing task scheduling model. With this model as the core, a distributed system is used to monitor the status of the computing engine server and computing engine client, and to optimize the scheduling of tasks based on the characteristics of computing tasks. . In this model, by adding a computing resource monitoring terminal between the server side and the client side, the computing resources are monitored and performance predicted, so as to realize the communication between different computing engine servers and computing engine clients in a distributed computing environment. And optimized scheduling of computing tasks. The monitoring of computing resources can use an independent computing unit, or can be concurrently performed by a server-side or client-side computing unit, which has greater flexibility. The computing resource performance prediction module performs regression analysis based on the historical data of the computing resource monitoring module, and predicts the performance status of the computing resource in the future, as a reference for the computing task scheduling module. The computing task scheduling module performs task scheduling according to the status information of the current computing resource performance prediction module and the characteristics of a given computing task, so as to fully utilize the computing potential of the computing resource.
[0028] This embodiment proposes a flexible and customizable simulation framework, which selects suitable working modes and simulation modules for different simulation environments and application requirements characteristics to perform dynamic safety assessment and early warning of the power system. In the online computing mode, the client side provides a remote task submission portal. The computing task is handled by the server-side parallel computing engine server with strong computing power, while the client-side parallel computing engine with weak computing power only needs to monitor the calculation. In offline computing mode, in addition to providing user interaction interfaces on the client side, its parallel computing engine client is directly responsible for the execution of computing tasks, and the computing results are published in time through the web page. The client side also provides an auxiliary service function module as an extensible interface for users to customize other working modes or computing service types. Server-side computing engine The core computing module of the server and the client-side computing engine offline analysis module of the client are all modular structures. At the network topology layer, you can select network survivability, network availability and network load capacity indicators for vulnerability analysis ; At the infrastructure layer, you can select evaluation indicators such as node availability, line availability, and unit availability.
[0029] In summary, this embodiment provides a scalable distributed system that supports dynamic security assessment and early warning of the power system, realizes the analysis of various vulnerability indicators of the power system, improves the efficiency of power system security assessment, and guarantees power The safe and stable operation of the system has important practical significance.
