AI-Driven Predictive Maintenance for High-Voltage Equipment in Saudi Power Systems
TL;DR
A five-layer framework linking sensing, trusted data engineering, hybrid analytics, risk-based decision support, and continuous learning is proposed, including asset-specific baselines, physics-informed models, explainability, cybersecurity, model-drift monitoring, and phased field validation.
Abstract
High-voltage assets are central to the reliability of modern power systems, yet their maintenance remains difficult because failures are rare, degradation is heterogeneous, and operational data are distributed across transformers, gas-insulated switchgear, circuit breakers, protection systems, and inspection platforms. This review examines how artificial intelligence can support predictive maintenance for high-voltage equipment in Saudi power systems. A structured review of literature published from 2020 to 2025 was conducted in line with PRISMA 2020 reporting principles. The evidence base comprises twenty-eight peer-reviewed studies and two official Saudi energy-sector reports covering dissolved gas analysis, partial-discharge recognition, thermal imaging, mechanical-state prediction, health indexing, remaining-life estimation, digital twins, anomaly detection, and deployment governance. The synthesis finds that artificial intelligence creates the greatest operational value when it combines multi-source condition data with asset context, uncertainty estimates, and maintenance decision rules rather than operating as an isolated fault classifier. Transformer applications are the most mature, particularly for dissolved gas analysis and health-index estimation, while gas-insulated switchgear and circuit-breaker applications show strong diagnostic accuracy but weaker evidence on long-term field generalisation. Saudi deployment conditions add specific requirements: extreme heat and dust, rapidly expanding transmission infrastructure, renewable-energy integration, large geographical service territories, and a strategic shift toward digital operations. The paper proposes a five-layer framework linking sensing, trusted data engineering, hybrid analytics, risk-based decision support, and continuous learning. It also identifies practical priorities for utilities, including asset-specific baselines, physics-informed models, explainability, cybersecurity, model-drift monitoring, and phased field validation. The review concludes that AI-driven predictive maintenance can strengthen reliability and lifecycle value in Saudi high-voltage networks, but its success depends more on data governance, engineering integration, and accountable maintenance workflows than on algorithmic accuracy alone.