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Rotor Poles Abnormality Detection of Wind Generator Based on ACO Combined with BPNN
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Abstract
This paper proposes an ABPNN approach, which utilizes ant colony optimization (ACO) to obtain optimal weight of back propagation network (BPNN). And various abnormalities of wind generator rotor can be accurately recognized. In the paper, four tailor-made types of wind generators, namely one broken rotor pole, two adjacent broken rotor poles, fractured rotor pole and burnt rotor pole, are operated to obtain the measured current signals of generators under various load and speed conditions. The spectrums and resolution levels of the current signals can be illustrated by using S-transform (ST) and multi-resolution analysis (MRA) respectively, and features can be extracted from the spectrums and resolution levels. Using the proposed ABPNN approach, the significant optimal weight of BPNN can be calculated based on the extracted features. Finally, the results show that the recognition accuracy of various abnormal generator rotor poles can be improved, even in environments with noise interference
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