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| Power System Transient Stability Assessment Based on Boosting Bayesian Classifier |
| Lu Jinling, Zhu Yongli, Zhao Hongshan, Liu Yan |
| North China Electric Power University Baoding 071003 China |
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Abstract Transient stability can be rapidly assessed using the artificial intelligence technology. The Bayesian classifier is one of the methods of artificial intelligence. Due to its linear training computational complexity, it is also one of the most practical and effective way for classification. Naive Bayesian classifier-bound as a Bayesian network is built on the characteristics of the property relative to the type of condition attributes of an independent assumption. Therefore, there is the possibility of misuse of classified. In this paper Adaptive Boost algorithm is used to boost the Naive Bayesian classifier which effectively reduces the rate misuse of classified. And the boosting Bayesian classifier is first put forward to be used for power system transient stability assessment. In this paper, the characteristics which can rapid reflect power system transient are selected. They are looked as the attribute variables of Bayesian classifier and stability or instability of the system are looked as class variables. The numerical simulation algorithm is used to produce a large amount of samples. And the attributes variables are processed to discrete data, the boosting Bayesian classifier is construct for transient stability assessment. At last, simulation results of the New England 10 machine 39-bus system verify that the power system transient stability assessment based on boosting Bayesian classifier can effectively reduce machine learning complexity and improve classifier precision.
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Received: 17 December 2008
Published: 14 February 2014
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2009, Vol. 24 
