论文标题
使用人工智能在现代电力系统中识别变电站配置
Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence
论文作者
论文摘要
电力系统传输网络拓扑用于能源管理系统应用。变电站配置是传输网络拓扑处理的基础。由可再生能源组成的现代电力系统由于风能和太阳能发电厂的可变性质，需要可靠且快速的网络拓扑处理。目前使用的传输网络拓扑处理基于通过SCADA传达的继电器信号不是高度可靠或高度准确的。研究了针对不同变电站布置（包括主和转移总线布置（MTBA），环形总线布置（RBA）和单一总线布置（SBA）在内的不同变电站布置（SCI）的变电站配置识别（SCI）。本文提出了基于人工智能（AI）方法的功能布置（FA）的基于同步测量的SCI。此方法改善了监视FA。提出了MTBA，RBA和SBA变电站配置识别的典型结果。在实时数字模拟器上模拟了一个经过修改的两个面积四机电源系统模型，该模型由MTBA，RBA和SBA组成的两个网格连接的太阳能PV植物模拟。显示基于AI的SCI可以准确识别在任何工作条件下的三个变电站布置的所有可能的FA。
Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition.
