The integration of renewable energy sources, such as wind power, into the electrical grid is essential for the development of sustainable energy systems. Doubly fed induction generators (DFIGs) have been significantly utilized in wind energy conversion systems (WECSs) because of their efficient power generation and variable speed operation. However, optimizing wind power extraction at variable wind speeds remains a major challenge. To address this, an artificial neural network (ANN) is adopted to predict the optimal shaft speed, ensuring maximum power point tracking (MPPT) for a wind energy-driven DFIG connected to a matrix converter (MC). The DFIG is controlled via field-oriented control (FOC), which allows independent power output regulation and separately controls the stator active and reactive power components. Through its compact design, bidirectional power flow, and enhanced harmonic performance, the MC, which is controlled by the simplified Venturini modulation technique, improves the efficiency and dependability of the system. Simulation outcomes confirm that the ANN-based MPPT enhances the power extraction efficiency and improves the system performance. This study shows how wind energy systems can be optimized for smart grids by integrating advanced control techniques like FOC and simplified Venturini modulation with intelligent algorithms like ANN.

In this study, a control method is created to drive two parallel-connected permanent magnet synchronous motors fed by a single converter. Matrix converter used in this work provides ac-ac conversion in one stage. The matrix converter...

Renewable energy systems, including solar, wind and hydro, have gained significant importance in recent years. Although these systems have many advantages, such as being clean and sustainable, their key disadvantage is low efficiency comparing to the energy systems based on fossil fuels. Moreover, more land area and resources are required by renewable energy sources to produce equal amount of energy compared to conventional sources. The efficiency of renewable energy systems can be improved using some selected techniques, which extract maximum power under all possible conditions. In this paper, a comparative analysis of eight different algorithms, including simple and artificial maximum power tracking techniques, has been conducted for wind and solar energy systems with integrated load and storage components. The comparison was based on factors such as efficiency, settling time, oscillations at maximum power point, and complexity of the algorithm. By combining the benefits of artificial intelligence and conventional methods, an adaptive hybrid algorithm that produced superior results is proposed for renewable energy systems.