Maximization of generated power from wind energy conversion system using a new evolutionary algorithm

In this paper, a grid-connected Doubly Fed Induction Generator controlled by a Sliding Mode Controller (SMC) is used to maximize the Wind Energy Conversion System (WECS) output power. A SMC is implemented using a PID controller that is tuned using a new algorithm based on hybrid Differential Evolution with a Linearized Biogeography-Based Optimization (LBBO-DE). Biogeography-Based Optimization (BBO) is an evolutionary optimization algorithm based on a mathematical model of organism distribution. BBO permits a recombination of the solutions features by migration. A new migration model based on the sigmoid function is proposed. An analysis of the LBBO-DE is conducted using six different models, including the sigmoid model. Their performance were tested with 23 benchmark functions. The comparison reveals that the sigmoid model has the best performance. Therefore, the LBBO-DE with a sigmoid model is used to optimize the controller parameters to maximize the WECS output power. The LBBO-DE with the sigmoid model is compared with the Tyreus-Luyben tuning method, Genetic Algorithm (GA) and Linearized BBO (LBBO). The results showed that the LBBO-DE has the best performance. The proposed algorithm is verified using an experimental setup for the maximization of the generated power from the WECS and reducing power loss.