A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H‐bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery‐based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases.

In this paper, a two-stage grid-connected photovoltaic system is presented. The optimal performance of solar panels at varying temperatures and irradiance levels is achieved by the application of a fuzzy logic-based maximum power point tracking method. Space vector modulation is used to control the inverter; to achieve capacitor voltage balance, redundant switching states of a three level inverter are used. The grid side control regulates the power and the current injected to the grid, the conventional PI controllers are replaced by fuzzy PI. Simulations have been made using Simulink environment on MATLAB to validatethe control of the proposed system.

This paper aimed to evaluate the performance of a three-phase double-stage photovoltaic water pumping system. Ours is composed of several components. These include a photovoltaic array; a boost DC-DC converter with a maximum power point tracking algorithm based on the Perturb and Observe technique; a three-level neutral-point-clamped inverter, which is controlled using simplified space vector pulse width modulation; and a moto-pump group based on an induction motor and tank. The induction motor is distinguished by its rigidness, reliability and relatively low cost. However, the difficulty of controlling the induction motor is related to the fact that its mathematical model in Park configuration is nonlinear and highly coupled. The regulation of the speed, rotor flux and d-q axis currents of the Field-Oriented Control is achieved through the use of sliding-mode-control-based regulators. To evaluate the performance of the proposed control strategy, simulations were conducted using the Matlab/Simulink platform while considering varying levels of irradiation and temperature. The simulation outcomes conclusively demonstrate that the proposed system control has good performance with regard to dynamic responses, control robustness and power quality, thus affirming its efficacy. A solar water pumping system that uses photovoltaic energy has the potential to be a dependable and effective way to pump water while using sustainable energy sources.