Current applications of artificial intelligence in rural power grids

The evolving energy landscape, characterized by decarbonization, the integration of renewable energy sources (RES), and the growing demand for grid reliability and energy efficiency, necessitates a profound transformation of power grids, particularly in rural areas. Conventional methods of power grid management often prove insufficient for these challenges, driving the adoption of artificial intelligence (AI) solutions. This study provides a systematic analysis of key AI applications in power grids, examining the algorithms employed and practical implementation examples from both international and domestic contexts. Drawing on a comprehensive review of literature, the authors have identified four primary application areas: load forecasting, power system and grid optimization, fault detection and equipment monitoring, and optimal resource management within power grids. Within these domains, effective algorithms include deep learning techniques such as LSTM, GRU, and CNN, along with machine learning models like SVM and various metaheuristic methods. Practical examples highlight the diverse deployment of AI, adapting to national power system specificities. For instance, countries with a high share of renewable energy sources (RES) often prioritize AI for load forecasting, while in Russia, the focus is on automating the monitoring and diagnostics of extensive rural grids through computer vision and UAVs. AI is instrumental in the design of Smart Grids, enabling the digital transformation of power infrastructure to enhance efficiency, resilience, and adaptability. However, successful AI integration requires addressing challenges related to reliability, cybersecurity, and the explainability of automation-driven decision-making.