| Summary: | Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme.
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