中国电机工程学报2013，Vol.33Issue(10)：90-97,前插12,9.
基于双阶段并行隐马尔科夫模型的电力系统暂态稳定评估
Power System Transient Stability Assessment Based on Two-stage Parallel Hidden Markov Model
摘要
Abstract
The transient stability assessment (TSA) of power system based on the artificial intelligence and machine learning method has become more popular. This paper proposed a precise pattern recognition method for TSA based on a two-stage parallel hidden Markov model (TS-PHMM). In the first stage, the sensitive feature subset was selected from the original feature set based on the relative sensitivity principle; in the second stage, the principal component analysis (PCA) method was used to decrease the subset dimension to obtain an optimized feature set. Then the optimized subset was adopted to train PHMM with a serial weight factors for TSA. Finally, in the CEPRI 8-generator 36-bus test system and a real large power system, the simulation results proved the validity and effectiveness of the feature selection approach and PHMM pattern recognition. Meanwhile, this new method needs less training samples compared with some of the common methods such as SVM and ANN to reach an equivalent accurate rate.关键词
暂态稳定评估/机器学习/双阶段并行隐马尔科夫/模式识别Key words
transient stability assessment (TSA)/ machine learning method/ two stage hidden Markov model (TS-PHMM)/ pattern recognition分类
信息技术与安全科学引用本文复制引用
唐飞,王波,查晓明,马志昊,邵雅宁..基于双阶段并行隐马尔科夫模型的电力系统暂态稳定评估[J].中国电机工程学报,2013,33(10):90-97,前插12,9.基金项目
国家高技术研究发展计划(863计划)(2011AA05A119) （863计划）
国家电网公司大电网重大专项资助项目课题(SGCC-MPLG029-2012). （SGCC-MPLG029-2012）
