Implementation of Automatic Failure Diagnosis for Wind Turbine Monitoring System Based on Neural Network
The global action began to resolve the problem of global warming. Thus, the wind power has been emerged as an alternative energy of existing fossil fuel energy. The existing wind power has limitation of location requirements and noise problems. In case of Korea, the existing wind power has difficulties on limitation of location requirements and the noise problems. The wind power turbine requires bigger capacity to ensure affordability in the market. Therefore, expansion into sea is necessary. But due to the constrained access environment by locating sea, the additional costs are occurred by secondary damage. In this paper, we suggest automatic fault diagnosis system based on CMS (Condition Monitoring System) using neural network and wavelet transform to ensure reliability. In this experiment, the stator current of induction motor was used as the input signal. Because there was constraint about signal analysis of large wind turbine. And failure of the wind turbine is determined through signal analysis based wavelet transform. Also, we propose improved automatic monitoring system through neural network of classified normal and error signal.
KeywordsWavelet transform Neural network Automatic failure diagnosis CMS Wind turbine monitoring system
This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and planning (KETEP) grant funded by the Ministry of knowledge Economy, Republic of Korea (No. 20114010203060).
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