Transient stability improvement of a transmission system through the application of an artificial neural network-based high-voltage direct current system

作者：C. Ohanu, Uche Chinweoke Ogbuefi, E. Ejiogu · 发表于：Energy Exploration & Exploitation · 年份：2025 · DOI：10.1177/01445987251327693 · 被引用次数：4

The increasing disturbances in power system networks present significant challenges to electrical power engineers, often leading to a loss of synchronism in grid-tied generators. It is important to ensure voltage, angle, and frequency stability in power system for efficient grid operation and a sustainable power supply. This paper investigates transient stability enhancement in multi-generator system using an artificial neural network (ANN)-based control technique. The conventional high-voltage direct current (HVDC) systems are based on a fixed proportional integral controller parameters to function efficiently, but the proposed ANN-based technique dynamically adjusts the thyristor firing angle in real-time to improve system stability. This intelligent control mechanism enhances transient stability by optimizing power system responses based on real-time operational data. The effectiveness of the proposed method is tested on a real 330-kV, 40-bus Nigeria transmission network, modeled in Power System Analysis Toolbox. The Newton–Raphson power flow method is employed to determine the base-case characteristics of the network. To achieve stable system operation, the voltage magnitude of a transmission system must fall within the statutory limit of 0.95–1.05 per unit (pu). However, power flow studies indicate a significant low-voltage profile of 0.70 pu on the network. Implementing the ANN-based HVDC system, three-phase faults are cleared within 2 ms, demonstrating a significant im...