In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fasttracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex-MSX-60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck-boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast-tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10 C

In this paper, a battery charging model is developed for solar PV system applications. As a means of photovoltaic power controlling system, buck-boost converter with a Maximum Power Point Tracking (MPPT) mechanism is developed in this paper for maximum efficiency. This paper proposed a novel combined technique of hybrid Particle Swarm Optimisation (PSO) and Salp Swarm Optimization (SSO) models to perform Maximum Power Point Tracking mechanisms and obtain a higher efficiency for battery charging. In order to retrieve the maximum power from the PV array, the Maximum Power Point Tracking mechanism is observed which reaches the maximum efficiency and the maximum power is fed through the buck-boost converter into the load. The buck-boost converter steps up the voltage to essential magnitude. The energy drawn from the PV array is used for the battery charging by means of an isolated buck converter since the buck-boost converter is not directly connected to the battery. The Fractional Order Proportional Integral Derivative (FOPID) controller handles the isolated buck converter and battery to enhance the efficiency obtained through the Maximum Power Point Tracking mechanism. The simulation results show higher steady efficiency by using the hybrid PSOSSO algorithm in all stages. The battery is charged without losing the efficiency obtained from the hybrid PSOSSO algorithm-based Maximum Power Point Tracking mechanism. The higher efficiency was obtained as 99.99% at Standard Test Conditions (STC) and 99.52% at PV partial shading conditions (PSCs) by using the new hybrid algorithm.

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This paper deals with the means of transferring energy from the input to the output. The buck boost converter is considered as a maximum power point tracker or power equilibrium device used between the photovoltaic solar system and the battery by supplying the desired power for the stand-alone system requirements. The system energy is assigned by SLP190S-24 High Efficiency Monocrystalline PV module based Perturb and Observe (P&O) MPPT algorithm with a selected lead acid battery bank of 24 Volts. In order to achieve this energy transfer with minor energy losses, Buck-Boost converter with the switching frequency of 25Khz is designed for charging the lead acid battery applied in standalone system. The MATLAB SIMULINK is used to validate the accuracy and effectiveness of the designed Buck-Boost converter simulation results. The result clings to the value of 99.72% for the combined Tracking and conversion efficiencies.

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