Advanced Control Technique for Optimizing Stability and Fault Recovery in Grid-Connected Solar Systems

Dai Van Le, Huynh Hoang Bao Nghia

Abstract


The primary purpose of this study is to enhance the operational stability and fault recovery capabilities of grid-connected photovoltaic (PV) systems, which frequently encounter challenges related to environmental variability and severe grid faults. To address these issues, the paper proposes an advanced maximum power point tracking (MPPT) controller utilizing an artificial neural network (ANN) trained with the Levenberg-Marquardt (LM) algorithm. The research procedure involves modelling a detailed grid-connected system comprising a DC-DC boost converter and a three-level neutral-point-clamped (NPC) inverter within the MATLAB/Simulink environment. The proposed control strategy is rigorously evaluated and compared with a conventional perturb-and-observe (P&O) method with dynamic step-size tuning under two distinct scenarios: normal operation with varying irradiance and severe three-phase-to-ground (LLLG) fault conditions. The main results demonstrate the superior performance of the proposed ANN-LM technique. Under normal conditions, the proposed method provides greater stability, with a total percentage overshoot of only 1.292%, which is significantly lower than the 1.496% observed with the conventional method. In the event of an LLLG fault, the proposed controller exhibits exceptional resilience, achieving a rapid voltage recovery time of 0.539 seconds compared to 0.713 seconds for the benchmark. Furthermore, the proposed technique improves power quality by limiting the inverter voltage total harmonic distortion (THD) to 122.37% during faults, whereas the conventional method reaches 147.31%. These findings confirm that the ANN-LM controller effectively optimizes energy extraction and ensures robust system operation in the face of grid disturbances.

Keywords


Artificial Neural Networks; Levenberg-Marquardt; Three-Phase Ground Faults; Grid-Connected Solar Energy Systems; Renewable Energy; Stability

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References


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DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v10i1.533.g420

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