基于广域测量系统的快速暂态稳定预测方法  

Fast Learning Algorithm for Transient Stability Prediction Based on Wide-area Measurement System

作　　者：刘兆燕[1] 江全元[1] 曹一家[1] 

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027

出　　处：《电力系统自动化》2007年第21期1-4,共4页Automation of Electric Power Systems

基　　金：国家自然科学基金重大项目(50595414);国家自然科学基金资助项目(50507018);教育部科学技术研究重大项目(305008)~~

摘　　要：在运用相量测量单元实测系统故障时轨线的基础上,采用一种在机器人领域广泛应用的抓球算法,对电力系统故障后预判失稳的发电机转子角进行预测。该方法分为跟踪和预测2个阶段:首先应用粒子群优化算法对跟踪阶段进行多参数优化,加快了跟踪的过程;然后利用泰勒级数展开法预测发电机的转子角。该方法无需知道系统结构的先验知识,可以提前0.5s判断各发电机的同步性,得到足够的"可用反应时间"用于在线失稳预警或就地控制。在10机39节点的新英格兰测试系统和50机145节点测试系统上的仿真结果表明了该方法的有效性。A fast learning method to predict the rotor angles of the generators to be considered as losing synchronism is proposed. The method adopts a widely used robotic ball-catching algorithm based on a continual stream of accurate generator rotor angle data measured by phasor measurement unit （PMU） on line. The method is divided into two parts. The tracking process is improved by the use Of particle swarm optimization （PSO） to perform multi-parameter optimization. Meanwhiie, the prediction process uses the Taylor series expansion to predict the generator rotor angle. The algorithm does not require prior knowledge of the system configuration and is able to predict the stability of the generators 500 milliseconds into the future and to save enough “action time” for on-line instability alarm and local control. The simulation results on the 10-generator, 39-bus New England Test System and 50-machine, 145-bus test system demonstrate the effectiveness of the proposed method for transient stability prediction.

关 键 词：暂态稳定预测 广域测量系统 机器人抓球算法 粒子群优化算法 

分 类 号：TM764[电气工程—电力系统及自动化] TM712