Improving Power Harvesting Ability of Variable Speed Wind Turbine using Intelligent Soft Computing Technique

Abstract

This paper presents a study carried out on maximizing energy harvesting of wind turbines. One way of improving the output power of the wind turbines is by optimizing the power conversion coefficient. The power conversion coefficient factor is expressed as a function of the wind turbine blade tip speed ratio and the turbine blade pitch angle. Optimization of the wind turbine generator output power is done by considering the effects of variations of wind speed, blade tip speed ratio, and pitch angle. An intelligent soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy logic controller for blade pitch actuator was applied to optimize the generator output power. The simulation result showed that the power conversion coefficient of 0.513 is achieved. The study was verified by using real-time wind speed data of Adama II wind farm in Ethiopia and specifications of the Gamesa G80 horizontal axis wind turbine generator unit by MATLAB software. Accordingly, a promising and satisfying improvement in power harvesting capacity is obtained. The output power of this generator is improved by 9.47% which is by far better result as compared to the existing literature.