State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

Xiaoyi Qian,Mohammed Gendeel,Yuxian Zhang

doi:10.3390/en12112046

Xiaoyi Qian, Mohammed Gendeel + Show 1 more

Open Access

https://doi.org/10.3390/en12112046

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Journal: Energies	Publication Date: May 28, 2019
Citations: 4	License type: CC BY 4.0

Affiliation: Shenyang University of Technology

Abstract

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.

Highlights

In recent years, research into renewable energy has attracted considerable attention owing to energy shortages and increasingly serious environmental problems [1]
Existing research divides the methods for fault detection and identification (FDI) for wind turbines (WTs) into model-based and data-based methods [4]
The present study investigates a probabilistic fault identification scheme on the basis of the fuzzy rule-based classification system (FRBCS) to improve the accuracy of fault identification and enhance the interpretability of identification results for offshore WTs

Summary

Introduction

Research into renewable energy has attracted considerable attention owing to energy shortages and increasingly serious environmental problems [1]. The timely detection and accurate location of common faults are crucial to enhance the efficiency of offshore WTs. Existing research divides the methods for fault detection and identification (FDI) for WTs into model-based and data-based methods [4]. Laouti et al [8] proposed a combination scheme of observer and support vector machines (SVMs) for fault detection of WTs. Laouti et al [8] proposed a combination scheme of observer and support vector machines (SVMs) for fault detection of WTs This scheme utilizes structural risk minimization to enhance generalization with a small training data set, and it allows for process nonlinearity by using flexible kernels. Cho et al [9] proposed a model-based method for fault detection of blade pitch systems, designed a Kalman filter to estimate blade pitch angle. As the building of system models is dependent on expert knowledge [10], data acquisition and Energies 2019, 12, 2046; doi:10.3390/en12112046 www.mdpi.com/journal/energies

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