Improved Weighted k -Nearest Neighbor Based on PSO for Wind Power System State Recognition

Chun-Yao Lee, Kuan-Yu Huang, Yi-Xing Shen and Yao-Chen Lee
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Chun-Yao Lee: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan
Kuan-Yu Huang: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan
Yi-Xing Shen: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan
Yao-Chen Lee: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan

Energies, 2020, vol. 13, issue 20, 1-16

Abstract: In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k -nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the distance judgment strategy to figure out the identical probability of multi-label classification. The PSO optimizes the weight and parameter k of PWKNN. This testing is based on four classified conditions of the 300 W wind generator which include healthy, loss of lubrication in the gearbox, angular misaligned rotor, and bearing fault. Current signals are used to measure the conditions. This testing tends to establish a feature database that makes up or trains classifiers through feature extraction. Not lowering the classification accuracy, the correlation coefficient of feature selection is applied to eliminate irrelevant features and to diminish the runtime of classifiers. A comparison with other traditional classifiers, i.e., backpropagation neural network (BPNN), k -nearest neighbor ( k -NN), and radial basis function network (RBFN) shows that PWKNN has a higher classification accuracy. The feature selection can diminish the average features from 16 to 2.8 and can reduce the runtime by 61%. This testing can classify these four conditions accurately without being affected by noise and it can reach an accuracy of 83% in the condition of signal-to-noise ratio (SNR) is 20dB. The results show that the PWKNN approach is capable of diagnosing the failure of a wind power system.

Keywords: particle swarm optimization (PSO); k -nearest neighbors ( k -NN); classification; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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