Low-voltage ride-through based on neuro-fuzzy for grid-connected photovoltaic system - N., Jaalam; L. V., Tan; N. H., Ramly; L. N., Muhammad; N. L., Ramli; N. L., Ismail (IJEETC, 2020)
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 gridconnected 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. 