A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

In this paper, a review of Metaheuristic Optimization Techniques (MOT) which are currently in use for optimization in a vast range of problems, is presented. MOT are known for their simplicity and stochastic nature and successfully applied to solve complex engineering problems. Although there exist various categories of MOT, the techniques from swarm intelligence is reviewed in this paper. An explanation of the theoretical foundation upon which each algorithm is based is provided, along with the relevant mathematical models that explain how an algorithm attempts to obtain the best solution to a problem. The paper also reviews the applications of swarm-based MOT to the control of the doubly fed induction generator (DFIG). Particular attention is given to control of the DFIG for wind energy applications. Control of the DFIG is generally realized via the use of PI controllers. While various PI controller tuning methods are well established (such as the Ziegler-Nichols and Cohen-Coon methods), these methods produce satisfactory results, and often fail to meet the stringent levels of control presently required. Due to this fact, as well as the current success of MOT in engineering, the application of MOT to the control of the DFIG could be promising area of research. The results of the study show that although the various swarm-based MOT differ from each other in terms of aspects such as complexity and advantages, they are all based on the concept of randomness, and always attempt to produce the best possible solution. It was also observed that various swarm-based MOT displays the demerit of getting easily trapped in the local optimum, however various advancements have been proposed to correct such an issue. Based on the results of the application of these techniques to other engineering problems, their application to the DFIG could yield exceptional results.