Hybrid SSA-PSO Based Intelligent Direct Sliding-Mode Control for Extracting Maximum Photovoltaic Output Power and Regulating the DC-bus Voltage

Photovoltaic (PV) energy has become a considerably more attractive source of power, with costs continually declining. Along with the implementation of this sustainable energy system, the power supply stability should be extensively investigated, particularly in off-grid systems where electricity storage becomes a limitation. The performance of the standalone photovoltaic battery system (SPVBS) is mainly reliant on the DC-bus voltage and its capacitor. The shortcomings of utilizing a small film DC-bus capacitor in SPVBS include the instability and low-frequency ripple of DC-bus voltage and the system's dynamic MPPT techniques performance. Therefore, this study proposes a new technique of regulating the various system components that stabilizes the DC-bus voltage, extracts stable output power despite harsh sudden load and radiation fluctuations, and maintains the system's functionality with or without the battery storage system (BSS). The proposed approach uses a hybrid salp swarm algorithm and particle swarm optimization (SSA-PSO) combined with an intelligent direct sliding mode controller (DSMC) to eliminate the instability and ripples of DC-bus voltages. Moreover, it employs DSMC for bidirectional DC-DC converter and boost converters to enhance the system's dynamic MPPT techniques capabilities. Four different modes are considered according to the energy availability, demand, and battery's state of charge (SOC). Additionally, this easy-to-use technique keeps the DC-bus voltage stable even when the battery storage system is not available. Simulation and experimental results revealed the resilience performance of the proposed intelligent SSA-PSO-DSMC technique in terms of DC-bus regulation, output power extracted, and current harmonics reduction compared to PSO, SSA, cuckoo search optimization (CSO), and grey wolf optimization (GWO).