Environmental impact-aware and cost-efficient optimal predictive maintenance scheduling for Gas Insulated Substations

This paper addresses the challenge of optimizing maintenance scheduling for Sulfur hexafuoride (SF 6 ) leakage in Gas Insulated Substations to minimize maintenance costs while reducing environmental impact and adhering to operational constraints. The study proposes a deterministic predictive maintenance framework using a mixed-integer linear programming approach. It includes a comparative analysis of the proposed predictive maintenance strategy with a benchmark condition-based maintenance strategy and validates its performance in reducing both costs and SF 6 leakage over varying time horizons, especially for larger systems or extended planning horizons. This research establishes a foundation for future work, including extending the model to incorporate uncertainties to further enhance the robustness and applicability of the framework in real-world scenarios.