Stability analysis of the pitch angle control of large wind turbines using different controller strategies

Abstract

Reducing the environmental impact necessitates a boost in renewable energy conversion systems. Wind energy is regarded as one of the most essential energy sources. For this purpose, the high wind variations in the energy conversion chain require robust and reliable control. This research aims to implement a regulation based on artificial intelligence toward a blade orientation mechanism to improve the stability of energy conversion. On the other hand, an energy maximization technique called Maximum Power Point Tracking (MPPT) is integrated into the control system. A developed program in MATLAB estimates the turbine performance with two different strategies, namely the MPPT technique and the Pitch control mechanism. For the best control and more stability of energy conversion, three artificial intelligence controllers, which are Neuronal Network (PI-ANN), Fuzzy Logic (PI-FLC), and Neuro-Fuzzy (PI-NFLC), were employed. They are compared with the conventional controller (PI-C). This comparison is made to distinguish the most robust regulator against wind speed variations. The different performance indices showed that the controller PI-NFLC has an excellent response, with an Integral Time Absolute Error (ITAE) of 375.28, whereas the Integral Absolute Error (IAE) and Integral Time Square Error (ITSE) equal 13.87 and 406.59, respectively.