摘要
对基于扩展卡尔曼滤波(EKF)原理的动态状态估计理论进行了深入的分析,并指出其存在的问题的此基础上,提出具有自适应能力的动态状态估计模型和算法。该模型和算法的新意主要体现在:在预测环节中,建立系统节点注入功率制约作用和系统状态自身预测融合的加权优化综合预测模型,提高了状态预估的精度;在滤波环节中,基于最小二乘支持向量机技术,建立了自适应的限定记忆动态滤波器,提高了模型的估计能力和计算速度。对山东500kV电网进行的实际分析,充分表明了该方法的有效性。
This paper further analyzes dynamic state estimation theory based on the extend Kalman filter （EKF） and points out two existent problems. Then model and algorithm for self-adapting dynamic estimator is presented here. Their new ideas embody two aspects. In forecasting model, considering control action of nodal power to system states and self-regulation of states, integrated model for system states is used to increase prediction accuracy. In filtering model, using least square support vector machines （LSSVM） technology, self-adapting dynamic filter is formed with limited memory to increase estimation capability and computing speed. It makes a satisfying result in actual application for power system control center of Shandong province.
出处
《电工技术学报》
EI
CSCD
北大核心
2008年第8期107-113,共7页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(50377021
50677036)
关键词
电力系统
动态状态估计
支持向量机
卡尔曼滤波
自适应滤波
Power systems, dynamic state estimation, support vector machines, kalman filtering, adaptive filters
