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Low-Voltage Ride-Through Based on Neuro-Fuzzy for Grid-Connected Photovoltaic System

N. Jaalam 1,2, L. V. Tan 1, N. H. Ramly 1, L. N. Muhammad 1, N. L. Ramli1, and N. L. Ismail 3
1. Faculty of Electrical & Electronics Engineering Technology, University of Malaysia Pahang, 26600 Pekan, Malaysia
2. UMPEDAC, University of Malaya, 59100 Kuala Lumpur, Malaysia
3. Faculty of Engineering, National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia

Abstract—The increasing capacity of grid-connected photovoltaic (PV) over electrical power system might lead to voltage sags which affected the consumers and industries. To improve this situation, a simple control strategy of reactive power control using neuro-fuzzy is proposed in this paper to enable voltage regulation in a single-stage grid-connected PV system. An Artificial Neural Network (ANN) model is trained until a satisfactory result is obtained. After that, the trained neural network is combined with fuzzy logic. During the abnormal condition, the reactive current is controlled to inject reactive power for grid support and voltage recovery purpose. The dynamic behaviour of the system will be analyzed under a three-phase fault condition via MATLAB/Simulink. The simulation result shows that the proposed control strategy using neuro-fuzzy controller is effective in compensating desired reactive power during such faults. The voltage profile of the system has shown at least 9% of increment in all case studies. A swift recovery on the voltage can be achieved as well since the voltage returns to steady-state immediately when the fault is cleared. 
Index Terms—Low-voltage ride-through, grid-connected photovoltaic, neuro-fuzzy

Cite: N. Jaalam, L. V. Tan, N. H. Ramly, L. N. Muhammad, N. L. Ramli, and N. L. Ismail, "Low-Voltage Ride-Through Based on Neuro-Fuzzy for Grid-Connected Photovoltaic System," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 9, No. 4, pp. 260-267, July 2020. Doi: 10.18178/ijeetc.9.4.260-267

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.