Modified Fuzzy-Q-Learning (MFQL)-Based Mechanical Fault Diagnosis for Direct-Drive Wind Turbines Using Electrical Signals

Abstract

In this paper, a self-learning multi-class intelligent model for wind turbine fault diagnosis is proposed by using MFQL (Modified-Fuzzy-Q-Learning) technique. The MFQL is adaptive in nature and extension of fuzzy-Q-learning method where look-up table of Q-learning is conquered by fuzzy based approximation strategy to reduce the curse of dimensionality of the Q-learning. The proposed MFQL classifier diagnoses the mechanical and imbalance faults without using mechanical sensors. Proposed methodology is addressed with relying on PMSG (Permanent Magnet Synchronous Generator) stator current signals, which is already being used by protection system of wind turbines. According to the aforementioned description, non-stationary current signals of PMSG have been pre-processed to extract the input features by empirical mode decomposition followed with J48 algorithm based most relevant input feature selection. For the one-step ahead performance demonstration of the proposed MFQL approach, results have been compared with neural network, support vector machines, fuzzy logic, and conventional Fuzzy-Q-Learning techniques. Demonstrated results outperform the capability of proposed MFQL approach. Moreover, MFQL is developed first time to implement in the area of WTGS fault diagnosis in the literature.