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Improving offshore wind turbine reliability is a key industry goal to improve the availability of this renewable energy generation source. The semiconductor devices in the wind turbine power converter (WTPC) are traditionally considered as the most sensitive and important components to achieve this, and managing their thermo-mechanical stressing is vital, since this is one of their principal long-term aging mechanisms. Conventional deterministic reliability prediction methods used in industrial applications are not suitable for wind turbine applications, due to the stochastic wind speed. This paper develops an electro-thermal model of the power devices which is integrated with a wind turbine system model for the investigation of power converter thermal cycling under various operating conditions. The model has been developed to eliminate the problems of PWM switching, substantially reducing simulation time.The model is used to improve the current controller tuning method to reduce thermal stresses suffered by the converter during a grid fault. The model is finally used to design a control method to alleviate a key problem of the doubly-fed induction generator (DFIG) -severe thermal cycling caused during operation near synchronous speed.

When the installed capacity of wind power becomes high, the power generated by wind farms can no longer simply be that dictated by the wind speed.With sufficiently high penetration, it will be necessary for wind farms to provide assistance with supply-demand matching. The work presented here introduces a wind farm controller that regulates the power generated by the wind farm to match the grid requirements by causing the power generated by each turbine to be adjusted. Further benefits include fast response to reach the wind farm power demanded, flexibility, little fluctuation in the wind farm power output and provision of synthetic inertia

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Model predictive and linear quadratic Gaussian controllers are designed for a 5MW variable-speed pitch-regulated wind turbine for three operating points – below rated wind speed, just above rated wind speed, and above rated wind speed. The controllers are designed based on two different linear dynamic models (at each operating point) of the same wind turbine to study the effect of utilising different control design models (i.e. the model used for designing a model-based controller) on the control performance. The performance of the LQG controller is enhanced by improving the robustness, achieved by replacing the Kalman filter with a modified Luenberger observer, whose gain is obtained to minimise the effect of uncertainty and disturbance

Anomalies in the wind field and structural anomalies can cause unbalanced loads on the components and structure of a wind turbine. For example, large unbalanced rotor loads could arise from blades sweeping through low level jets resulting in wind shear, which is an example of anomaly. The lifespan of the blades could be increased if wind shear can be detected and appropriately compensated. The work presented in this paper proposes a novel anomaly detection and compensation scheme based on the Extended Kalman Filter. Simulation results are presented demonstrating that it can successfully be used to facilitate the early detection of various anomalous conditions, including wind shear, mass imbalance, aerodynamic imbalance and extreme gusts, and also that the wind turbine controllers can subsequently be modified to take appropriate diagnostic action to compensate for such anomalous conditions.

The power converter is one of the most vulnerable components of a wind turbine. When the converter of an offshore wind turbine malfunctions, it could be difficult to resolve due to poor accessibility. A turbine generally has a dedicated controller that regulates its operation. In this paper, a collective control approach that allows a cluster of turbines to share a single converter, hence a single controller, that could be placed in a more accessible location. The resulting simplified turbines are constant-speed stall-regulated with standard asynchronous generators. Each cluster is connected by a mini-AC network, whose frequency can be varied through a centralised AC-DC-AC power converter. Potential benefits include improved reliability of each turbine due to simplification of the turbines and enhanced profit owing to improved accessibility. A cluster of 5 turbines is assessed compared to the situation with each turbine having its own converter. A collective control strategy that acts in response to the poorest control is proposed, as opposed to acting in response to the average control. The strategy is applied to a cluster model, and simulation results demonstrate that the control strategy could be more cost-effective than each turbine having its own converter, especially with optimal rotor design

Optimal controllers, namely Model Predictive Control (MPC), H∞ Control (H∞), and Linear Quadratic Gaussian control (LQG), are designed for a 5 MW horizontal-axis variable-speed wind turbine. The control design models required as part of the optimal control design are obtained by using a high fidelity aeroelastic model (i.e., DNV Bladed). The optimal controllers are eventually designed in three operating modes: below-rated, just below-rated, and above rated-wind speeds, based on linearized control design models. The linearized models are reduced by using a model reduction technique to facilitate the design of optimal controllers. The controllers are analyzed not only in the time domain but also in the frequency domain and on the torque/speed plane. Simulation results demonstrated that optimal controllers perform better than the standard proportional-integral-derivative (PID) controller, particularly for removing oscillation due to the drive-train mode without incorporating a drive-train damper.