An Improved Sliding Mode Controller for MPP Tracking of Photovoltaics

Abstract

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power.

Keywords

• backstepping sliding mode controller (BSMC)
• fuzzy logic
• maximum power point tracking (MPPT)
• particle swarm optimization (PSO) algorithm
• photovoltaic (PV) system