This chapter addresses the optimization and validation of switched reluctance generator (SRG) models, focusing on energy performance and precise control of operational parameters. It begins with the construction and validation of a computational model based on experimental data, exploring different inductance representations, including parametric regression and Fourier series. Methods for calculating efficiency and generated power are presented, considering variations in the turn-off angle \(\theta _{OFF}\) and the excitation voltage \(V_1\) . Optimization involves hybrid algorithms and sensitivity analysis techniques to adjust \(\theta _{OFF}\) to maximize SRG efficiency under various conditions. In addition, a wind turbine model coupled to the generator is introduced, which details the integration of maximum power point tracking (MPPT) control for varying wind speed profiles. Simulations with real-world data demonstrate system performance, achieving efficiencies exceeding 60% under all conditions, underscoring the applicability of SRGs in renewable energy generation with low operational costs and high reliability.