Cost‐effective wind turbine (WT) diagnosis using supervisory control and data acquisition (SCADA) data is a promising technology for future intelligent wind farm operation and management. This study presents a thermophysics‐based method for WT drivetrain fault diagnosis. A synthesised thermal model is formed by incorporating thermal mechanisms of the drivetrain into a WT system model. Applications of the model are demonstrated in case studies of the gearbox and generator comparing simulation results and SCADA data analysis. The results show non‐linearity of the gearbox oil temperature rise with wind speed/output power that can effectively indicate gearbox efficiency degradation, which may be attributed to gear transmission problems such as gear teeth wear. Electrical generator faults such as ventilation failure and winding voltage unbalance will cause changes to heat transfer and result in temperature changes that can be used for diagnosis purposes. This is shown by different patterns of stator winding temperature associated with power generation, while the simulation reveals the thermal mechanism. The method can be applied to diagnose some failure modes which are hard to identify from vibration analysis. The developed thermal model can play a central role for the purpose of fault diagnosis, by deriving relationships between various SCADA signals and revealing changes in the thermophysics of WT operation.

Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.

Although Permanent Magnet Synchronous Generator (PMSG) wind turbines (WTs) mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

This paper proposes a new SVPWM based fault tolerant control approach for a three-phase two-level AC-DC converter. Voltage vectors deviation and affected voltage space sectors under faulty conditions are firstly analyzed. Then a twelve-sector division based fault tolerant control approach is proposed. With the division scheme, sectors affected by faulty switches can be effectively separated from unaffected ones and SVPWM strategies are implemented to compensate distorted reference voltage vector in each sector. The scheme to determine the compensation ratio of vectors is also provided to maximize fault-tolerant performance. Six types of defined failure modes, including single and multiple switch faults, are analyzed and tested in experiments. Evaluations on results of current averaged THDs, fluctuation of generator speed, torque oscillations and stability of DC-link voltage verify its capability to achieve maximum system recovery. The approach proposed is a SVPWM based method for AC-DC converters that requires no additional hardware equipment and provides an effective & low cost fault-tolerant solution.