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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

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.

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