Automating distribution networks: Backtracking search algorithm for efficient and cost-effective fault management

Abstract

Electricity outages can result in consequences for customers and cause disruptions that result in revenue loss, business productivity reduction, appliance damage, and compromised public safety. To minimize outage duration, locating faults quickly and accurately is crucial. Advanced fault management strategies leverage computational intelligence and deploy fault indicators (FIs) and sectionalizing switches (SSs) to locate faults and isolate faulty parts. Although FIs and SSs can improve distribution network reliability, deploying them at every node is impractical owing to cost and complexity. Therefore, it is important to optimize their placement in strategic locations. This research proposes a new optimization problem for the simultaneous placement of FIs and SSs. The aim is to minimize the cost of equipment placement, dispatching field crews, and service interruption. To solve the proposed formulation, the Backtracking Search Algorithm (BSA), a population-based meta-heuristic method, is used to identify optimal nodes for FIs and SSs placement. The performance of the BSA is evaluated through statistical tests, and the proposed strategy is shown to be effective compared to state-of-the-art methods. The results reveal a significant 7% reduction in device requirement, leading to a substantial 6.69% decrease in equipment expenditures and an overall decline in system costs compared to the established approach implemented in the test system. This research highlights the importance of optimization strategies for fault management systems and demonstrates the potential of meta-heuristic algorithms for solving complex problems.