Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

被引：47
|
作者：
Zhang, Yanjun [1 ]
Li, Tie [1 ]
Na, Guangyu [1 ]
Li, Guoqing [2 ]
Li, Yang [2 ]
机构：
[1] State Grid Liaoning Elect Power Supply Co Ltd, Shenyang 110006, Peoples R China
[2] Northeast Dianli Univ, Sch Elect Engn, Chuanying 132012, Jilin, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2015年 / 2015卷
关键词：
DYNAMIC SECURITY ASSESSMENT; FEATURE-SELECTION;
D O I：
10.1155/2015/529724
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.
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页数：8
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