Maintenance Scheduling Algorithm for Transformers in Tanzania Electrical Secondary Distribution Networks

Abstract

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