Improved Weighted k-Nearest Neighbor Based on PSO for Wind Power System State Recognition
Open Access
- 21 October 2020
- Vol. 13 (20), 5520
- https://doi.org/10.3390/en13205520
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.Funding Information
- Ministry of Science and Technology, Taiwan (MOST 108-2221-E-033-022-MY2)
This publication has 29 references indexed in Scilit:
- Current-Aided Order Tracking of Vibration Signals for Bearing Fault Diagnosis of Direct-Drive Wind TurbinesIEEE Transactions on Industrial Electronics, 2016
- Building an Intrusion Detection System Using a Filter-Based Feature Selection AlgorithmInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016
- Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current’s FFTIEEE Transactions on Instrumentation and Measurement, 2015
- Current-Based Mechanical Fault Detection for Direct-Drive Wind Turbines via Synchronous Sampling and Impulse DetectionIEEE Transactions on Industrial Electronics, 2014
- Particle swarm optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networksMechanical Systems and Signal Processing, 2011
- Wavelet Basis Function Neural Networks for Sequential LearningIEEE Transactions on Neural Networks, 2008
- Review of condition monitoring of rotating electrical machinesIET Electric Power Applications, 2008
- Deterministic Convergence of an Online Gradient Method for BP Neural NetworksIEEE Transactions on Neural Networks, 2005
- A theory for multiresolution signal decomposition: the wavelet representationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1989