Abstract

Integrating an interline power flow controller (IPFC) with an artificial neural network (ANN)-based control system is an effective approach to enhancing power system efficiency and performance. This study presents a comprehensive investigation into optimal power flow (OPF) regulation in power systems through the integration of IPFC and ANN-based control techniques. The primary objective is to ensure the robustness and reliability of the power system. The proposed system optimizes power flow using advanced ANN-based control strategies. The ANN control system dynamically adjusts IPFC parameters in real time, enabling adaptive and efficient power flow management. Leveraging the capabilities of ANN, the system effectively handles fluctuating loads, network disturbances, and operational uncertainties, thereby improving overall system performance. Simulation studies are conducted using MATLAB 2016b to evaluate the efficiency of the proposed IPFC-ANN control system. The simulation framework facilitates the analysis of complex power system scenarios, including multiple interconnected nodes, varying load demands, and diverse operating conditions. Key performance metrics, such as system efficiency, voltage stability, and power loss reduction, are used to validate the effectiveness of the proposed control method. The results demonstrate that the integrated IPFC-ANN control system consistently achieves an efficiency of over 92%, outperforming conventional control methods.

Keywords

Optimal power flow, Interline power flow controller, Artificial neural network, Power system stability, Adaptive control strategies, Voltage regulation and loss reduction.

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