Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks

Abstract

Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of 96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors.
 Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution network