Modeling Maximum Power Control for Wind Turbines Using Particle Swarm Optimization Algorithm

The longer the power transmission line, the more power is lost during transmission. To address this issue, this paper proposes a Particle Swarm Optimization (PSO) algorithm to determine the maximum power point for wind turbines, thereby maximizing the transmission power of the wind turbine system. The model used is a grid-integrated Doubly-Fed Induction Generator (DFIG) wind power system with parameters including 60 individuals and 2 control variables: the wind turbine's power coefficient <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(C_{p})$</tex> and the tip speed ratio <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\lambda)$</tex>, for a total of 1000 iterations. The PSO algorithm's objective function is continuously updated based on the input tip speed and the position of individuals, factoring in parameters such as air density, pitch angle, and propeller radius. Simulation results indicate that the power coefficient corresponds to the optimal input tip speed, allowing the wind turbine to achieve maximum power output. This demonstrates that the PSO algorithm is highly effective in enhancing the generating power of the wind turbine system.