What are the current state-of-the-art AI techniques being used in smart grid operations and maintenance?
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State-of-the-art AI techniques in smart grid operations and maintenance include transfer learning, deep attention mechanisms, graph learning, reinforcement learning, and physics-guided neural networks . Additionally, artificial intelligence approaches like multilayer perceptron (MLP) controllers are utilized for frequency control in smart microgrids . The integration of AI in smart grids enhances dependability, flexibility, and efficiency by processing vast amounts of data collected through modern metering and control technologies . Furthermore, advancements in sensors, communications, and smart electronics enable smarter and more automated grids, allowing for better monitoring of aging equipment, pre-planned load shedding, and remote disconnection of loads to prevent blackouts . These AI applications address challenges such as power flow, state estimation, fault detection, and security concerns in modern electric distribution systems.
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| Papers (5) | Insight |
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29 Dec 2022 | State-of-the-art AI techniques in smart grid operations include load prioritization, automated shedding, and remote disconnection to prevent blackouts, leveraging distributed sensors and computations for enhanced grid automation. |
| Current state-of-the-art AI techniques in smart grid include load forecasting, power system stability assessment, fault detection, and security enhancement, enhancing grid reliability and flexibility. | |
| The paper implements multilayer perceptron (MLP) controllers in a smart microgrid for adaptive control, showcasing AI techniques for frequency regulation and disturbance handling in smart grid operations. | |
| State-of-the-art AI techniques in smart grid operations include transfer learning, deep attention mechanism, and graph learning, combined with reinforcement learning and physics-guided neural networks for improved efficiency and control. | |
| Current state-of-the-art AI techniques in smart grid include load forecasting, power system stability assessment, fault detection, and addressing security challenges, enhancing grid reliability and flexibility. |
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