This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.

Increasing the efficiency of photovoltaic (PV) systems is a pressing issue, and several studies have focused on the Maximum Power Point Tracking (MPPT) techniques to extract the maximum PV output power. Many MPPT techniques have been discussed in the last decade, and optimization-based MPPT techniques have shown better performance than other MPPT techniques. In this study, two optimization techniques, the cuckoo search algorithm and particle swa9rm optimization with changing inertia weight techniques are discussed and applied to a PV system to track the maximum power point. The MSX-60 PV module and boost DC-DC converter are used in this paper to simulate and model the MPPT system using MATLAB/Simulink to show which technique has the best performance under various solar irradiation scenarios. In addition, different structures of PV arrays such as series-parallel, bridge link, and total cross-tied PV structures are simulated to analyze their effect on the efficiency of MPPT processes.

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