Low Voltage Ride-Through Improvement of a Grid-Connected PV Power System Using a Machine Learning Control System

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Abstract
The insufficient durability of solar energy systems is an important problem in low-voltage situations in the electrical grid. This problem can cause PV systems to become difficult to operate during periods of low voltage and may disconnect PV systems from electrical grids. In this study, a hybrid protection system combining a DC chopper and a capacitive bridge fault current limiter (CBFCL) and based on a machine learning (ML) approach is proposed as a protection strategy to improve the low voltage ride-through (LVRT) capability of a grid-connected PV power plant (PVPP) system. To forecast the best control parameters using real time, including both the fault and normal operation conditions of the grid-connected PVPP system, the ML approach is trained on historical data. Among 20 classifier algorithms, the Coarse Tree classifier and Medium Gaussian SVM classifier have the best accuracy and F1-score for the DC chopper and DC chopper + CBFCL protection systems. The Medium Gaussian SVM classifier has the highest accuracy (98.37%) and F1-score (99.17%) for the DC chopper and CBFCL protection method among the 20 classifier methods. In comparison to another protection system, the simulation results show that a proposed hybrid protection system using SVM offers optimum protection for the grid-connected PVPP system.

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