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Prediction of Voltage Collapse in Electrical Power System Network Using Voltage Stability Index
Subject area: Science,Engineering and Technology · Area of research: Voltage Collapse
DOI: 10.64388/IREV9I11-1717852
Abstract
This study developed and validated a comprehensive framework for predicting voltage collapse in electrical power systems using advanced voltage stability indices (VSIs), with specific application to the Nigerian 330kV transmission grid. The research addressed critical challenges in power system reliability through the integration of voltage stability assessment, artificial intelligence-driven predictive maintenance, and engineering management principles. A systematic comparative evaluation of multiple voltage stability indices was conducted, including L-index, Fast Voltage Stability Index (FVSI), Modern Voltage Stability Index (MVSI), Voltage Collapse Proximity Index (VCPI), and Novel Line Stability Index (NLSI). The indices were rigorously tested on IEEE standard test systems (14-bus, 30-bus, 39-bus) and the Nigerian 330kV 50-bus grid under various operating scenarios including base load, peak load, and contingency conditions. The methodology employed quantitative simulation-based approaches using Python/Pandapower and MATLAB/Simulink platforms, complemented by comprehensive load flow analysis, P-V and Q-V curve generation, and N-1 contingency assessment. An artificial intelligence framework utilizing multilayer perception and Long Short-Term Memory networks was developed for predictive maintenance integration. Results demonstrated that MVSI achieved superior performance with 96.8% accuracy and exceptional robustness to parameter variations, while FVSI provided optimal computational efficiency at 27.2 milliseconds for real-time applications. Critical infrastructure elements were identified, including vulnerable buses (Kaduna, Kano, Abuja, Lagos, Port Harcourt) and transmission lines requiring enhanced monitoring. The AI-driven predictive maintenance framework achieved 96.2% accuracy, resulting in zero unplanned outages during the six-month pilot deployment. The research provides immediate applicability for Nigerian grid operations, offering validated tools for early warning systems and preventive control strategies. The integrated framework supports sustainable power system development and provides a foundation for enhanced grid reliability and voltage collapse prevention in developing power systems.
Keywords
Voltage Stability, Voltage Collapse Prediction, Voltage Stability Indices (VSI), Modern Voltage Stability Index (MVSI), Fast Voltage Stability Index (FVSI)
References
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[2] Alayande, A. S. (2024). L-Index-Based Technique for Voltage Collapse Prediction and Weak Bus Identification in Power Systems. AJERD, 7(1), 260–277.
[3] Baleboina, G. M. & Mageshvaran R. (2023). A survey on voltage stability indices for power system transmission and distribution systems, Frontiers in Energy Research, 11.
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[5] IEEE PES. (2023). Test Systems for Voltage Stability Analysis. IEEE.
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How to cite this paper
@article{1717852,
author = {Akpan, Ukeme Udoette, Chizindu Stanley Esobinenwu},
title = {Prediction of Voltage Collapse in Electrical Power System Network Using Voltage Stability Index},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {3812-3832},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717852.pdf},
abstract = {This study developed and validated a comprehensive framework for predicting voltage collapse in electrical power systems using advanced voltage stability indices (VSIs), with specific application to the Nigerian 330kV transmission grid. The research addressed critical challenges in power system reliability through the integration of voltage stability assessment, artificial intelligence-driven predictive maintenance, and engineering management principles. A systematic comparative evaluation of multiple voltage stability indices was conducted, including L-index, Fast Voltage Stability Index (FVSI), Modern Voltage Stability Index (MVSI), Voltage Collapse Proximity Index (VCPI), and Novel Line Stability Index (NLSI). The indices were rigorously tested on IEEE standard test systems (14-bus, 30-bus, 39-bus) and the Nigerian 330kV 50-bus grid under various operating scenarios including base load, peak load, and contingency conditions. The methodology employed quantitative simulation-based approaches using Python/Pandapower and MATLAB/Simulink platforms, complemented by comprehensive load flow analysis, P-V and Q-V curve generation, and N-1 contingency assessment. An artificial intelligence framework utilizing multilayer perception and Long Short-Term Memory networks was developed for predictive maintenance integration. Results demonstrated that MVSI achieved superior performance with 96.8% accuracy and exceptional robustness to parameter variations, while FVSI provided optimal computational efficiency at 27.2 milliseconds for real-time applications. Critical infrastructure elements were identified, including vulnerable buses (Kaduna, Kano, Abuja, Lagos, Port Harcourt) and transmission lines requiring enhanced monitoring. The AI-driven predictive maintenance framework achieved 96.2% accuracy, resulting in zero unplanned outages during the six-month pilot deployment. The research provides immediate applicability for Nigerian grid operations, offering validated tools for early warning systems and preventive control strategies. The integrated framework supports sustainable power system development and provides a foundation for enhanced grid reliability and voltage collapse prevention in developing power systems.},
keywords = {Voltage Stability, Voltage Collapse Prediction, Voltage Stability Indices (VSI), Modern Voltage Stability Index (MVSI), Fast Voltage Stability Index (FVSI)},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717852}
}