Performance Evaluation of Artificial Neural Network, Perturb and Observe, and Incremental Conductance MPPT Controllers for Wind Energy Conversion Systems

Reliable maximum power point tracking (MPPT) methods are essential in dynamic wind conditions to obtain maximum efficiency in wind energy conversion systems (WECSs). Conventional methods like incremental conductance (INC) and perturb and observe (P&O) are simple and robust but have drawbacks in terms of convergence and oscillations around the maximum power point (MPP) under dynamic conditions. In contrast, intelligent control methods such as artificial neural networks (ANNs) adapt more effectively. This paper presents a comparative analysis of ANN, P&O, and INC methods to obtain MPP for a WECS. A permanent magnet synchronous generator (PMSG) was coupled with a DC–DC boost converter to study the performance of the three MPPT methods under two different wind profiles. The ANN was trained with Bayesian regularization (BR) to estimate wind speed using rotor speed and computed mechanical power as inputs. The INC method achieved MPP using real-time power–voltage curves, while the P&O method perturbs the control variable, observes its results in output power, and adjusts the control variable accordingly. The three MPPT methods were compared in terms of power extraction, voltage stability, robustness, and dynamic response. The ANN achieved faster response, smoother output power and voltage, and reduced oscillation to dynamic conditions with higher output power compared to P&O and INC. On the other hand, the P&O and INC methods are less computationally intensive and do not require offline training.