Transient stability improvement of a transmission system through the application of an artificial neural network-based high-voltage direct current system
Year: 2025
Type: article
Abstract: 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 improvement compared to the 3-ms critical clearing time achieved using conventional method. Additionally, the ANN-based controller enhances voltage stability, achieving a minimum voltage magnitude of 0.98 pu, representing a 27.8% improvement over the conventional approach. The results confirm that the proposed ANN-based HVDC system offers superior transient stability performance by dynamically adjusting the system response, ensuring better fault ride-through capability and improved voltage profile. The findings highlight the potential of ANN-based controllers in improving transient stability in modern power systems.
Institution: University of Nigeria
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W4408558493