A Hybrid ANN‐Based Model Predictive Control For PWM‐Based Variable Speed Wind Energy Conversion System On Smart Grid

作者：S. Karthikeyan, C. Ramakrishnan, S. Karthik · 发表于：International Transactions on Electrical Energy Systems · 年份：2025 · DOI：10.1155/etep/3791152 · 被引用次数：1

One renewable energy (RE) source that shows promise for producing electrical energy is wind energy (WE). The coordination between the grid and WE conversion systems has become necessary due to high wind power penetration into the grid and varying wind speeds (VWSs). When incorporated into the grid, wind systems encounter challenging scenarios, including voltage fluctuations, power loss, and the troublesome dynamics of RE sources. Conventional PI control systems and fuzzy logic controllers (FLCs) face difficulties in resolving these problems. Applying hybrid artificial neural networks (ANN) will enhance the efficiency of the VWS system. The suggested controller can facilitate uninterrupted power transmission between generators and the grid, enabling a seamless connection with the grid. Here, it can all be facilitated by the constant voltage and power source supplied by a suggested controller. The training of hybridized ANNs with model predictive control (MPC) can minimize computing demands and device version errors. For ANN‐MPC, the WE systems for DC microgrids are optimal. Simulink simulations in MATLAB/Simulink are conducted using the suggested hybrid ANN controller. The proposed ANN can consistently achieve better voltage balance and accuracy across various loading cases compared to conventional FLC and PID controllers. The outcomes demonstrate this. The outcomes of these simulations verify the efficiency of the ANN‐based strategy. With an accuracy rate of 92.6% and a perfo...