Adaptive Fuzzy Sugeno Large of Maxima Optimization for Gas Turbine Biofuel Speed Controllers.

Modern day gas turbines are important sources of electrical power generation, and are regarded as the prime movers in aerospace, marine propulsion. The depletion of fossil fuel resources has paved the way for the usage of biofuel and renewable energy. The challenge to this is the fact that biofuel has been considered as an alternative to fossil fuel for power generation to ensure the reliability and dependability of renewable fuels in a complex multi-domain systems such as gas turbines. The main objective of this study is to optimize the fuel control system parameters of the Rowen gas turbine model and the turbine speed controllers. Furthermore, to render the Rowen model simulation relevant for biofuel applications, real-time experimental data on speed and loads were obtained. Thus, this paper proposes micro turbines that run on biofuel-based tuned Fuzzy Sugeno Large of Maxima (FSLOM). Four controllers were used to track the speed and load data. The experimental results show that, fuzzy controllers with online gain tuning, result in the best speed controllers performance (2.0- 1.05 Sec) for the biofuel operation compared to other controllers.