MODIFIED PARTICLESWARM OPTIMISATION ALGORITHM FOR PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING UNDER PARTIAL SHADING CONDITIONS
The common problem associated with the PV systems is partial shading of the PV modules due to clouds or blocking objects, such as buildings or tree leaves. For a PV system to be effective, maximum power point tracking (MPPT) algorithm that can track the global maximum power point is necessary, since causes the output power of a PV system to reduce drastically. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Particle swarm optimisation (PSO) algorithm is a new optimisation algorithm based on MHA. Is use to solve optimisation problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original PSO can be further improved for various partial shading conditions (PSCs). Therefore, a proposed modified PSO, named modified particle swarm optimisation is use for this study. The proposed method adds the weighting average, the swooping behaviour, and nonlinear convergence factor in the PSO. The maximum power tracked by the MPSO method during PS1, PS2, PS3, and PS4 were 500.502 W, 399.6840 W, 285.0341 W and 180.8662 W respectively while for the PSO algorithm values were 463.0465 W, 398.4245 W,195.7191 W and 176.4084 W respectively, The results showed that the MPSO algorithm was able to achieve better tracking accuracy, faster dynamic response and more robust under the partial shaded conditions than the conventional PSO technique. The MPSO has reduced the tracking time up to 45.5% of the PSO and the dynamic tracking efficiency is improved by more than 2%, compared to the PSO. The findings can be applied to improve real-world photovoltaic systems. By implementing the modified PSO approach, solar energy producers can enhance their profitability and sustainability, ultimately contributing to a greener future.
Authors : Abdulrazak, M., Shodiya, S., Dagala, V.M., Muhammad, A.B. and Bukar, Y.M.
Category : Open Access Volume (Issue) : 10(1) Date Uploaded : 5th January 2025