Résumé: The proliferation of grid-connected photovoltaic systems (GCPVs) has created significant challenges in maintaining power quality standards, particularly with respect to total harmonic distortion (THD). This research is concerned with evaluating three well-known pulse width modulation (PWM) techniques - sine PWM (SPWM), third harmonic injection PWM (THIPWM) and space vector PWM (SVPWM) for their effectiveness in mitigating THD in three-phase single stage GCPVs. Through extensive simulations performed in MATLAB/Simulink, a comprehensive comparative analysis is presented that reveals the strengths and limitations of each PWM strategy. The results show that SVPWM is the most effective technique for THD mitigation and outperforms its counterparts. THIPWM proves to be a promising second-best option, while SPWM lags behind in terms of harmonic suppression capabilities. This research not only quantifies the THD reduction achieved by each PWM technique but also delves into the underlying mechanisms and theoretical underpinnings that contribute to their distinct performances. The results are further supported by detailed harmonic spectrum analyses, providing valuable insights into the harmonic profiles associated with each modulation strategy.

Résumé: This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (P&O), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.

Résumé: The kWh price of renewable energy power plants is still very costly, so any malfunction or weak yield is prejudicial to guarantee the investment payback. Therefore, in this case, sustainability re-assessment of the whole system is required. In general, faults are difficult to detect, and very quickly they evolve rapidly and exponentially. Keeping the faulty part in operation has a negative influence on the other healthy parts, through additional electrical constraints, which increasingly weaken them (risk of cascading faults). So, it is important to detect faults early in order to eliminate or correct them. Early detection implies in-depth diagnosis, for this purpose, several detection processes require the planned disconnection of photovoltaic power plant production. This technique has negative economic repercussions. Our proposal is based on continuous monitoring which automatically launches the detection process as soon as a tiny anomaly appears, without stopping production, unlike other techniques. It is also a real time faults parameters observation. The sensitivity of the developed algorithm is based on the interaction between the electrical characteristics of the panels. The behavior of the photovoltaic panel under different levels of degradation is highlighted and discussed. Therefore, a new diagnosis process is developed and achieved successfully based on specified points gathered from new proposed characteristics very sensitive to any variation from healthy case. This sensitivity allows to an earlier fault detection.