The increasing penetration of renewable energy in the power system grid makes it one of the most important topics in electricity generation, now and into the future. Tidal current energy is one of the most rapidly growing technologies for generating electric energy. Within that frame, tidal current energy is surging to the fore. Forecasting is the first step in dealing with future generations of the tidal current power systems. The doubly-fed induction generator (DFIG) and the direct drive permanent magnet synchronous generator (DDPMSG) are the most commonly used generators associated with tidal current turbines. The aim of the present work is to propose a forecasting technique for tidal current speed and direction and to develop dedicated control strategies for the most commonly used generators, enabling the turbines to act as an active component in the power system.

This thesis is divided into two parts. The first part proposes a hybrid model of an artificial neural network (ANN) and a Fourier series model based on the least squares method (FLSM) for monthly forecasting of tidal current speed magnitude and direction. The proposed hybrid model is highly accurate and outperforms both the ANN and the FLSM alone. The model is validated and shown to perform better than other models currently in use. This study was done using data collected from the Bay of Fundy, Nova Scotia, Canada, in 2008.

The second part of the thesis describes the overall dynamic models of the tidal current turbine driving either a DFIG or a DDPMSG connected to a single machine infinite bus system, including controllers used to improve system stability. Two models are tested and validated, and two proportional integral (PI) controllers are proposed for each machine to control the output power of the tidal current turbine. The controllers are tested using a small signal stability analysis method for the models, and prove the robustness of the tidal current turbine using two different types of generators over those without controllers. The controller gain ranges are also investigated to establish zones of stability. Overall results show the advantages of using a DDPMSG over a DFIG.