With the acceleration of global energy transition, smart grids have become the core infrastructure of the power system. However, as the scale of the power grid continues to expand and the types of equipment become increasingly complex, traditional manual inspections and planned maintenance models are no longer able to meet the growing demands for reliability and efficiency. The introduction of AI technology is fundamentally changing the way power grid operations and maintenance are carried out - from passive response to active prediction, from manual decision-making to automated repair.
The first core application of AI in smart grid operation and maintenance is fault prediction. By deploying sensors on key equipment such as transformers, circuit breakers, and transmission lines, real-time multidimensional data such as temperature, vibration, current, and partial discharge can be collected. AI models can learn the behavior patterns of normal equipment operation and issue warnings in the early stages of abnormalities. For example, a time series prediction model based on LSTM (Long Short Term Memory Network) can predict transformer oil temperature anomalies several hours or even days in advance, providing valuable response windows for the operation and maintenance team. According to data from the Electric Power Research Institute (EPRI) in the United States, grid operators using AI predictive maintenance have reduced unplanned downtime by an average of 40% and maintenance costs by over 25%.
The second key application is fault location and diagnosis. When faults such as short circuits and grounding occur in the power grid, AI systems can quickly analyze multi-source data from fault recorders, PMUs (phasor measurement units), and other sources to determine the fault type and precise location within milliseconds. The topology analysis model based on graph neural network (GNN) can track the fault propagation path in complex power grid structures, reducing the traditional manual troubleshooting that takes several hours to a few minutes.
The third application direction is automated repair and self-healing. Combining digital twin technology, AI can simulate the feasibility of different repair solutions in a virtual environment, select the optimal strategy, and automatically issue instructions to smart switches, automatic reclosers, and other devices. At the distribution network level, AI driven self-healing systems can complete fault isolation and power restoration in non fault areas within seconds, minimizing the impact of power outages.
It is worth mentioning that AI operation and maintenance systems need to be deeply integrated with existing industrial systems such as SCADA (data acquisition and monitoring control) and OMS (operation and maintenance management system). This requires AI models to have industrial grade robustness - they can still work reliably under harsh conditions such as data loss and communication delays. Currently, federated learning frameworks are being used to address data privacy issues, allowing multiple grid operators to collaboratively train more accurate prediction models without sharing raw data.
Looking ahead to the future, with the development of edge AI chips, more and more fault prediction and diagnosis models will be deployed on the edge side of substations, achieving on-site intelligence with millisecond level response. The AI driven smart grid operation and maintenance is moving from concept verification to large-scale deployment, providing solid guarantees for the safe and stable operation of the global energy system.
[Reference source] This article is a comprehensive compilation of technical reports publicly released by EPRI (Electric Power Research Institute) and relevant research literature in the field of electrical energy from IEEE.