Inefficient fault management in electrical Secondary Distribution Network (SDN) is one of the major challenges facing most power utility companies around the world including Tanzania. Currently, faults management processes from detection to clearance are done manually due to the lack of visibility in SDN resulting to long Mean Time To Repair (MTTR) and high operational costs. Advancements in Information and Communication Technology (ICT) and sensing technologies have made it possible to have cost effective electrical power network visibility solutions. This study proposes algorithms that enhance fault detection and monitoring in the Tanzania SDN based on distributed processing architecture. The proposed algorithms include sensing and data acquisition, fault detection, localization and visualization. The algorithms were deployed and tested on live network at

Transformers are essential and costly components of electrical secondary distribution networks (ESDNs). Distribution transformers provide electricity to low-voltage consumers that need a consistent power supply for their daily tasks. Transformer faults have an impact on ESDN power reliability. Even though several studies have attempted to investigate fault parameters; types, causes, and indicators in transformers, it is still difficult to generalize these criteria based on diversifications. These diversifications are caused by the architecture of the ESDN itself, transformer type, and insulation materials. Therefore, this paper investigates fault types, causes, and indicators specifically on oil-based transformers in Tanzania’s ESDN using the oil analysis technique and the Dissolved Gas Analysis (DGA) tool based on descriptive statistical analysis. Results show that cellulose deterioration accounted for 33.2% of all faults, and the leading causes are overload, aging, and moisture content. Despite cellulose deterioration issues, the arcing fault is 26.2% caused by trippings, short circuits, and flashovers. The outcome of this work may help the utility implement a more advanced monitoring tool and maintenance mechanisms to enhance power reliability and reduce transformer faults in ESDN.

The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using a knowledge-based system (KBS). Applying the challenge driven education approach, the requirements for developing an algorithm were established through an extensive literature survey and engagement of the key stakeholders from the Tanzania utility company. Our study uses the Dissolved Gas Analysis tool to collect the transformer parameters used in algorithm design. The parameter analysis was performed using Statistical Package for Social Sciences software. Results show that the designed KBS algorithm minimizes human-related maintenance errors and lowers labour costs as the system makes all the maintenance decisions. Specifically, the proposed maintenance scheduling algorithm reduces downtime maintenance costs by 1.45 times relative to the classical inspection-based maintenance model while significantly saving the maintenance costs. Keywords: Electrical power network, Forecasted load consumption, Knowledge-Based System, Maintenance Scheduling, Predictive Maintenance, Secondary Distribution

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