Development of Automation Maintenance Management System to Enhance Operational Efficiency for Unmanned Substations: A Case of 132kv Maweni Substation in Tanga

Juma Salum; Mwaka Juma

doi:10.37284/eaje.8.2.4047

Juma Salum Dar es Salaam Institute of Technology
Mwaka Juma, PhD Dar es Salaam Institute of Technology

DOI: https://doi.org/10.37284/eaje.8.2.4047

Keywords: Automation Maintenance Management, Unmanned Substations, Operational Efficiency, Machine Learning, Predictive Maintenance, Electrical Infrastructure, Condition Monitoring, System Integration

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Abstract

This study developed an automation maintenance management system to enhance operational efficiency for unmanned substations using the 132kV Maweni Substation in Tanga, Tanzania as a case study. Traditional reactive maintenance practices resulted in 65% operational efficiency and 15 unscheduled interruptions annually. A mixed-methods design combined quantitative surveys from 92 transmission professionals with qualitative expert interviews. The Relative Importance Index methodology identified seven critical technical factors with high significance (RII > 0.8): Security Protocols, Condition Monitoring, Operational Automation, Communication Redundancy, System Integration, Fault Localisation, and Remote Accessibility. Multiple regression analysis yielded a predictive model with exceptional performance (R² = 0.960), explaining 96% of maintenance efficiency variance. Twelve-month validation demonstrated improved availability from baseline 65% to 91%, with six consecutive months achieving 100% availability. The integrated system combines machine learning algorithms with real-time monitoring for intelligent work order management and predictive maintenance scheduling. Results confirm that data-driven automation significantly enhances operational reliability, reduces maintenance costs, and optimises resource allocation in unmanned electrical infrastructure.

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