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Deep Learning for Power System Applications : Case Studies Linking Artificial Intelligence and Power Systems

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This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.

Author(s): Fangxing Li; Yan Du
Series: Power Electronics and Power System
Publisher: Springer International Publishing
Year: 2023

Language: English
Pages: 200

Cover
Front Matter
1. Introduction: A Brief History of Deep Learning and Its Applications in Power Systems
2. Deep Neural Network for Microgrid Management
3. Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening
4. Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning
5. Summary and Future Works
Back Matter