The optimization of energy production in renewable energy systems is crucial to improve energy efficiency. In this context, the aim of this study focuses on maximizing the efficiency of a switched reluctance generator. This paper presents a novel approach to enhance the electrical power and efficiency of a switched reluctance generator by determining the optimal operating parameters based on the mechanical input power of the system. The proposed strategy consists of the following steps: First, an algorithm was developed that provides machine data for different power modes based on control parameters, including electrical and mechanical powers such as speed, torque, and turn-on and turn-off angles. In the next step, the obtained data were analyzed to identify the optimum points corresponding to the states with maximum power and efficiency for various scenarios. An algorithm for maximum power point tracking was also developed to determine the optimal parameters as a function of mechanical energy. Finally, the data and algorithms were integrated into the switched reluctance generator control system. Simulations were conducted to compare the proposed MPPT technique with other techniques. This comparison is essential to validate the effectiveness of the proposed strategy in achieving enhanced electrical power generation efficiency.

This study proposes a dynamic filtering based framework aimed at improving the performance and reliability of power grid control systems. To achieve this, several distortion identification techniques are first examined to determine the most efficient method for real-time operation. The selected dynamic filter is subsequently incorporated into a photovoltaic generator (PVG) to support the delivery of high quality electrical energy and to optimize power flow supervision. The obtained results confirm that integrating renewable generation with intelligent energy management significantly enhances grid stability, control accuracy, and overall system efficiency.