A novel flicker detection method for vertical axis wind turbine using two-dimensional discrete wavelet transform

Background Pattern recognition applications have increased dramatically over the past four decades. In power system, pattern recognition can be used in fields such as power quality. One of the important power quality aspects is flicker, which is caused by torque fluctuations in wind turbines. Aims In this paper, the various mechanical faults including single and double faults that cause flicker in vertical axis wind turbines (VAWTs) are modeled; then flicker caused by single and double faults are analyzed. Then, a novel method for pattern recognition is presented which results in more accuracy in flicker detection. Materials and Methods In this paper, features of voltage and current signals are extracted by the 2D-DWT. The 2D-DWT is capable of producing valuable features than the discrete wavelet transform (DWT) at a lower level of decomposition. The 2D-DWT is even capable of compressing data that can be suitable for smart grids, which high volume data is one of their problems. In order to reduce the redundant data, dimension of features vector, and computational burden, a new method is presented for feature selection. The backbone of the presented feature selector is the used classifier in the corresponding pattern system; so it is compatible with the classifier. Results Results show the high accuracy of the proposed method in identifying the factors that cause flicker in VAWTs. Conclusion In this paper, it was shown that the features extracted from the current and voltage signal by the 2D-DWT were superior to the features extracted from the only voltage signal by the DWT. Also, the advantage of the proposed feature selection over the Relieff method was shown.