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.
Highlights
With the emergence of green energy, wind power plays a principle role in energy
This paper uses the PWKNN as a classifier of malfunction diagnosis and compares it with the back propagation neural network (BPNN), the k-nearest neighbor (k-NN), and the radial basis function network (RBFN)
This paper proposes the PWKNN to recognize the operational conditions of power generators
Summary
With the emergence of green energy, wind power plays a principle role in energy. wind power is a form of intermittent energy without a stable output. The automatic identification system includes the following three parts: the signal analysis, feature extraction, and condition classification. The signal analysis has been carefully researched in the past, in which the feature extraction and condition classification are crucial factors [7,8]. Features extracted from signals in automatic classification can reduce the input number of classifiers and computing time. The proposed method is used to recognize the operation of the wind power system. This paper is organized as follows: In Section 2, we introduce the signal analysis and feature extraction approach; in Section 3, we present the procedure and structure of improved weight k-NN based on particle swarm optimization (PWKNN); in Section 4, we discuss the types of malfunctions of a wind power system, the feature selection; in Section 5, we present the simulation results; and, we provide the conclusions This paper is organized as follows: In Section 2, we introduce the signal analysis and feature extraction approach; in Section 3, we present the procedure and structure of improved weight k-NN based on particle swarm optimization (PWKNN); in Section 4, we discuss the types of malfunctions of a wind power system, the feature selection; in Section 5, we present the simulation results; and in Section 6, we provide the conclusions
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