Smart Sag Detection and Reactive Current Injection Control for a PV Microgrid under Voltage Faults

Muhammad Affan Khan and Jaehong Kim ()
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Muhammad Affan Khan: Department of Electrical Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea
Jaehong Kim: Department of Electrical Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea

Energies, 2023, vol. 16, issue 19, 1-21

Abstract: Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.

Keywords: photovoltaic (PV) power generation; microgrid; low-voltage ride through; voltage source inverter (VSI); neural network (NN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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