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Real-Time System Monitoring for Stability and Optimization of Port Harcourt Transmission Substation
Subject area: Science,Engineering and Technology · Area of research: Engineering Management
Abstract
The dependability and reliability of transmission substations are vital for the delivery of safe and steady electricity across the Transmission Company of Nigeria (TCN) National Grid. The research undertakes real-time system monitoring for stability and optimization of the Port Harcourt Transmission Substation applying Electrical Transient Analyzer Program (ETAP) simulations and supplemented and optimized by Python-based Genetic Algorithm (GA). To achieve this, a model of the Port Harcourt Transmission Substation was developed in ETAP to conduct load flow, contingency and transient stability analyses. The model identified baseline weaknesses including voltage dips, feeder overloads, and frequency deviations. The simulation results were exported to a Python Genetic Algorithm (GA), where control parameters of turbine governor droop, Automatic Voltage Regulator (AVR) gain, Power System Stabilizer (PSS) settings, and Synchronous condenser capacity were then optimized iteratively through a fitness function to reduce voltage deviations, frequency instability, and thermal overloading from the ETAP simulations. Results indicated that prior to the optimization, the system was vulnerable to voltage instability, line overloads exceeding thermal limits, and oscillatory frequency deviations. But after utilization of the optimization model, all bus voltages were able to stabilize near 1.0 pu, loadings were balanced within safe voltage limits (i.e. 1.02 pu +/−) and the frequency oscillations were damped in a more consistent manner with better settling times. The Genetic Algorithm (GA) fitness evolution curve indicated progressive improvements, as seen with the fitness moving from approximately, 0.55 to 0.93. The conclusion of this study is that the ETAP – Python-Based Generic Algorithm (GA) integration provides an efficient, effective and resilient platform for monitoring and optimizing the stability of the System in real time and proves that it is possible to leverage a deterministic simulation, combined with evolutionary optimization, into an improved approach for increasing flexibility at the Port Harcourt Transmission Substation level in giving added value to Transmission Company of Nigeria (TCN) operations and contributing to the advancement of Smart Grid development in Nigeria.
Keywords
Transmission substation, ETAP, Genetic Algorithm, voltage stability, contingency analysis, transient stability, real-time monitoring, power system optimization, Nigeria power grid, load flow analysis
How to cite this paper
@article{1722769,
author = {Amadi, Promise Chime, Chizindu Stanley Esobinenwu},
title = {Real-Time System Monitoring for Stability and Optimization of Port Harcourt Transmission Substation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {321-331},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722769.pdf},
abstract = {The dependability and reliability of transmission substations are vital for the delivery of safe and steady electricity across the Transmission Company of Nigeria (TCN) National Grid. The research undertakes real-time system monitoring for stability and optimization of the Port Harcourt Transmission Substation applying Electrical Transient Analyzer Program (ETAP) simulations and supplemented and optimized by Python-based Genetic Algorithm (GA). To achieve this, a model of the Port Harcourt Transmission Substation was developed in ETAP to conduct load flow, contingency and transient stability analyses. The model identified baseline weaknesses including voltage dips, feeder overloads, and frequency deviations. The simulation results were exported to a Python Genetic Algorithm (GA), where control parameters of turbine governor droop, Automatic Voltage Regulator (AVR) gain, Power System Stabilizer (PSS) settings, and Synchronous condenser capacity were then optimized iteratively through a fitness function to reduce voltage deviations, frequency instability, and thermal overloading from the ETAP simulations. Results indicated that prior to the optimization, the system was vulnerable to voltage instability, line overloads exceeding thermal limits, and oscillatory frequency deviations. But after utilization of the optimization model, all bus voltages were able to stabilize near 1.0 pu, loadings were balanced within safe voltage limits (i.e. 1.02 pu +/−) and the frequency oscillations were damped in a more consistent manner with better settling times. The Genetic Algorithm (GA) fitness evolution curve indicated progressive improvements, as seen with the fitness moving from approximately, 0.55 to 0.93. The conclusion of this study is that the ETAP – Python-Based Generic Algorithm (GA) integration provides an efficient, effective and resilient platform for monitoring and optimizing the stability of the System in real time and proves that it is possible to leverage a deterministic simulation, combined with evolutionary optimization, into an improved approach for increasing flexibility at the Port Harcourt Transmission Substation level in giving added value to Transmission Company of Nigeria (TCN) operations and contributing to the advancement of Smart Grid development in Nigeria.},
keywords = {Transmission substation, ETAP, Genetic Algorithm, voltage stability, contingency analysis, transient stability, real-time monitoring, power system optimization, Nigeria power grid, load flow analysis},
month = {September},
}