AI-Driven Maintenance Optimization for Solar Minigrids: A Work In Progress Framework

Solar minigrids are a paradigm shift in rural electrification, but their sustainability is constrained by equipment failures, high cost of maintenance, and delayed supply chain services. This paper presents a targeted literature review focusing on key research gaps in AI-driven maintenance optimization applied to solar minigrids and suggesting an integrated framework in addressing these gaps. The gaps are mapped to four research questions: (a) minigrid component’s supply chain integration with predictive maintenance outputs, (b) holistic AI frameworks for minigrid predictive maintenance to improve reliability and component life span (c) validation of business models in minimizing maintenance costs, and Return on Investment (ROI) of minigrids (d) integration of inclusivity in equalizing distribution for all community members. The proposed framework integrates IoT sensors, MQTT/LoRaWAN communication, synchronization of the digital twin and AI-based health scoring with supply chain optimization. The framework will be validated using an experimental setup representing a laboratory-scale grid-connected solar photovoltaic power conversion system, and addresses research objectives: predictive maintenance strategy, supply chain optimization, business model design, and inclusive deployment. This study provides a base foundation towards scalable, cost-effective predictive maintenance systems to fill the gap between technical innovation and practical deployment especially in off-grid conditions where resources are constrained.