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Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

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Drivetrain failures may cause severe damage to wind turbines. In the previous work, detection of failures in generator bearing and gearbox gears using electrical signature analysis (ESA) has been investigated. However, the detection of defects of bearings in the gearboxes has been a major gap. Bearing defects in gearboxes are believed to be one of the root causes of wind drivetrain failures. In this paper, a novel ESA-based monitoring technique is proposed for monitoring gearbox bearing defects in wind turbines, which is the first ESA technique reported that is capable of detecting bearing defects in gearboxes. A novel electrical signature tool, i.e., electrical multiphase imbalance separation technique, has been used to improve the signal-to-noise ratio in ESA. The principle of gearbox bearing defect detection is presented in detail. The proposed approach is validated by experimental results obtained from a 25-hp wind drivetrain simulator, which is designed to simulate 1.5-MW wind turbines as well as in the field on 1.5-MW wind turbines. The experimental results show that the proposed approach is capable of providing accurate detection of gearbox bearing failures at an early stage. The proposed approach is cost-effective, with reliable detection of defects compared to existing techniques.

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