Work

Enhancing fault ride-through capability of grid-connected solar PV systems using an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based dynamic voltage restorer in compliance with the Ethiopian grid code

Year: 2026

Type: article

Abstract: As global interest in renewable energy grows, the deployment of grid-connected solar photovoltaic (PV) systems is rapidly increasing. However, these systems face significant challenges during grid faults, such as increased grid current, voltage drops at the point of common coupling (PCC), and overshoots in the DC link voltage. To protect power converters, solar PV systems typically disconnect from the grid when voltage falls below a nominal threshold, which can lead to reliability and stability issues. This paper addresses the enhancement of fault ride-through (FRT) capability of grid-connected PV (GCPV) systems, enabling them to remain operational during grid disturbances. An adaptive neuro-fuzzy inference system (ANFIS)-based dynamic voltage restorer (DVR) controller is proposed to improve FRT performance. The proposed method is evaluated on the Bahir Dar distribution network (Feeder R5–02) using MATLAB R2021a. The particle swarm optimization (PSO) algorithm determines the optimal size (2.2 MW) and location (bus 19) of the PV system, aimed at minimizing total losses. A synchronous reference frame (SRF) phase-locked loop (PLL) is introduced for effective synchronization of the PV system with the grid. Before implementing the ANFIS-based DVR controller, PSO-based PI tuning is performed to optimize gain values. Training data is extracted from the Simulink model to train the ANFIS controller, which is then evaluated against various fault scenarios. The results demonstrate the controller's effectiveness in maintaining PCC voltage during disturbances, meeting Ethiopian grid code requirements. Simulation results show that the ANFIS-based DVR maintains the PCC RMS voltage at 0.957 pu. across all fault scenarios, compared to 0.81 pu. with the conventional PI-based DVR. Thus, the ANFIS-based DVR significantly enhances FRT capability during grid faults and fulfils the grid code requirements of renewable energy integration in Ethiopia.

Source: Scientific African

DOI: 10.1016/j.sciaf.2026.e03319

Authors: Yosef Birara Wubet, Yaschalew Ayele Gizachew

Institutions: University of Gondar, Bahir Dar University

Language: English

Topic: Microgrid Control and Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $590