Frequency Stability of the Nigerian 330kV Transmission Network using ANN Controller

Subject Area: Electrical Engineering

The Nigerian power sector faces frequent frequency instability issues that disrupt the balance between electricity supply and demand, causing power outages. This study investigates the enhancement of frequency stability in the Nigerian 330kV transmission network using Artificial Neural Network (ANN) controllers. A load flow analysis of New Haven, Enugu’s transmission network was conducted using the Newton-Raphson method, analyzing 11 buses. Results revealed weak buses with voltages below the stability range, indicating frequency instability. A Simulink model was developed to simulate the network, confirming the instability. To address this, an optimization process was applied to maximize voltage levels at the weak buses, constrained by their initial faulty voltages. ANN controllers were trained using supervised learning techniques to regulate control actions such as reactive power compensation and generation dispatch. After deployment, the ANN dynamically adjusted control parameters, maintaining frequency stability across the network. Results showed a significant improvement in performance when compared to the conventional setup. This study demonstrates the effectiveness of ANN controllers in enhancing the stability of Nigeria’s transmission network, although challenges such as high implementation costs and technical complexity persist. The findings validate ANN’s potential in modernizing the Nigerian power infrastructure and improving overall reliability.