Abstract：As wind power generation is a complicated nonlinear time-varying system, it's hard to extract effective fault feature. A novel algorithm that combined the modified local discriminant basis (LDB)algorithm and SOM-BP network is proposed to fault diagnosis and isolation. Extracting primal fault feature by improved LDB algorithm, then map this incipient fault feature into a new feature space with high class separability via self-organizing feature map (SOM) nonlinearly transform, finally BP is used as the nonlinear classifier to implement fault diagnosis and isolation.
[1] Karki R, Hu P, Billinton. A simplified wind power generation model for reliability evaluation[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 533-540. [2] Cusido J, Jornet A, Romral L, et al. Wavelet and PSD as fault detection techniques[C]. IEEE Proceedings of the Technology Conference on Instrumentation and Measurement, 2006: 1397-1400. [3] Liu B. Selection of wavelet packet basis for rotating machinery fault diagnosis[J]. Journal of Sound and Vibration, 2005, 284: 567-582. [4] Liu Chaochun, Dai Daoqing, Yan Hong. Local discriminant wavelet packet coordinates for face recognition[J]. The Journal of Machine Learning Research, 2007(8): 1165-1195. [5] Umapathy K, Krishnan S. Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(3): 517-523. [6] Umapathy K, Krishnan S. Audio signal feature extraction and classification using local discriminant bases [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(4): 1236-1246. [7] Umapathy K, Krishnan S. Modified local discriminant bases and its application in signal classification[C]. IEEE Proceedings of the Conference on Acoustics, Speech, and Signal Processing, 2004, 2: 745-748. [8] Chu J U, Moon I, Mun M S. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(11): 2232-2239. [9] Berglund E, Sitte J. The parameterless self-organizing map algorithm[J]. IEEE Transactions on Neural, 2006, 17(2): 305-316. [10] Noriega G. Self-organizing maps as a model of brain mechanisms potentially linked to autism[J]. IEEE Transactions on Rehabilitation Engineering, 2007, 15(2): 217-226. [11] Malhi A, Gao R X. PCA-based feature selection scheme for machine defect classification[J]. IEEE Transactions on Instrumentation and Measurement, 2004, 53(6): 1517-1525.
2009, Vol. 24 
