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Intelligent Contingency Ranking of Nigeria's 330 KV Transmission Network Using Artificial Neural Network

Domaine:

environment and energydigital infrastructure

Type de record:

paper
Créateur:
IheAtuOkoOdi
Éditeur:
ISA Publisher
Hôte:avatar
This research examines and prioritizes contingencies within Nigeria’s 330 KV transmission network, concentrating on the South-South and South-East areas.  The study utilizes MATLAB/Simulink with actual grid data to evaluate the efficacy of Artificial Neural Network (ANN) in enhancing voltage stability and minimizing power losses during line interruptions.  The Voltage Contingency Deviation Index (VCDI) and the Power Contingency Deviation Index (PCDI) were two important measures of how well the system worked.  The results reveal that the ANN did a great job of recovering voltage and reducing losses.  The results show that using ANN-based control may make Nigeria's power grid more reliable and stable. This is a useful tool for smart contingency management. Keywords: Contingency Analysis, ANN, Power System Stability, MATLAB, 330 KV Network, Nigeria.

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doi.orgzenodo.org

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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