Optimization of wind-turbine control using the hybrid ANFISPID method based on ant colony optimization

The Permanent Magnet Synchronous Generator (PMS) be coupled with a wind turbine to produce electricity. The PMSG has very little efficiency to produce electrical power. This characteristic is influenced by wind speed, pitch angle, and others. Therefore, wind turbines need to be controlled to produce optimal electrical power. In this paper, a combination of Adaptive Neural Fuzzy Inference System (ANFIS) and Proportional Integral Derivatives (PID) was combined with the artificial intelligence of Ant Colony Optimization (ACO) to control the pitch angle. The combining ANFIS, PID, and ACO will be compared to control the pitch angle which to produce the optimal PMSG output power. The simulation results show that the three models tested have been covered for the ANFIS-PID-ACO model while the best model performed. The ANFIS-PID-ACO model was the best model whit the highest maximum active power obtained at wind speed t1= 3.7075 Watts, t2 = 2.188 Watts, t3 = 3.9199 Watts, t4 = 2.6086 Watts, and t5 = 5.0338 Watts. The NFISPID- ACO method is proven to be able to optimize wind energy better than the previous method. However, this research will be developed using other methods to obtain the best optimization method.