Real-Time Transient Stability Prediction Based on Relevance Vector Learning Mechanism for Large-Scale Power System

2007 ◽  
Author(s):  
Niu Lin ◽  
Du Zhi-gang ◽  
Zhao Jian-guo
Keyword(s):  
Power System ◽  
Real Time ◽  
Large Scale ◽  
Transient Stability ◽  
Stability Prediction ◽  
Learning Mechanism

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

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yanjun Zhang ◽  
Tie Li ◽  
Guangyu Na ◽  
Guoqing Li ◽  
Yang Li
Keyword(s):  
Power Systems ◽  
Power System ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Transient Stability ◽  
Stability Prediction ◽  
Learning Machine ◽  
Elm Classifier ◽  
Ipso Algorithm ◽  
Synchronized Measurements

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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