WAVELET NEURAL NETWORKS BASED RECOGNITION OF SWING AND FAULT IN POWER SYSTEM
Mao Peng 1, Zhang Zhaoning 2, Lin Xiangning 1, Sun Yaming 3 (1. Group of Dongfang Electronic Information Industry Inc, Yantai 264001, China) (2. Civil Aviation College of China, Tianjin 300300, China) (3. Tianjin University, Tianjin 300072, China)
All of the existing power swing blocking elements would cause, at different extent, delayed and blind elimination during the power swing. This paper presents a new type of wavelet neural networks (WNN) model with the integration of the outstanding characteristics of Wavelet transform (WT) and Neural Networks (NN), and its corresponding algorithm. Based on the WNN, a new principle for power swing block using transient signal could be designed in the distance protection devices. Theoretical analysis and lots of EMTP simulation results show that WNN after enough learning can quickly and correctly recognize the fault during power swing. Even under the unfavorable conditions, satisfactory results can be achieved. And the method has many advantages such as fast computation and response, high reliability etc.