Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems
暫譯: 電力系統應用的深度學習：連結人工智慧與電力系統的案例研究

Li, Fangxing, Du, Yan

Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems
  • 出版商: Springer
  • 出版日期: 2024-11-12
  • 售價: $4,440
  • 貴賓價: 9.5 折 $4,218
  • 語言: 英文
  • 頁數: 101
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 303145359X
  • ISBN-13: 9783031453595
  • 相關分類: 人工智慧、DeepLearning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.
Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.
  • Provides a history of AI in power grid operation and planning;
  • Introduces deep learning algorithms and applications in power systems;
  • Includes several representative case studies.

商品描述(中文翻譯)

本書為讀者提供了基於深度學習技術的深入回顧，並討論了這些技術如何能夠惠及電力系統應用。書中探討並討論了深度學習技術在電力系統中的代表性案例研究，包括用於電力系統安全篩選和級聯故障評估的卷積神經網絡（CNN）、用於需求響應管理的深度神經網絡（DNN），以及用於暖通空調（HVAC）控制的深度強化學習（deep RL）。
深度學習在電力系統應用中的案例研究：連結人工智慧與電力系統 是電力系統領域的教授、學生、工業及政府研究人員，以及實務工程師和人工智慧研究者的理想資源。


  • 提供了人工智慧在電網運行和規劃中的歷史；

  • 介紹了深度學習算法及其在電力系統中的應用；


  • 包含幾個代表性的案例研究。


作者簡介

Fangxing "Fran" Li received his B.S.E.E. and M.S.E.E. degrees from Southeast University, Nanjing, in 1994 and 1997, respectively, and his Ph.D. from Virginia Tech, Blacksburg, VA, in 2001. He is the James McConnell Professor with the University of Tennessee, Knoxville, TN. His research interests include power system artificial intelligence, renewable energy integration, demand response, power markets, and power system control. He is a registered Professional Engineer (P.E.) in the State of North Carolina, a Fellow of the IEEE (Class of 2017), the current Editor-In-Chief of IEEE Open Access Journal of Power and Energy (OAJPE), the current Chair of the IEEE/PES Power System Operation, Planning and Economics (PSOPE) committee, and the current Chair of the IEEE/PES Task Force on Machine Learning in Power Systems. He received the 2020 Best Paper Award from the Journal of Modern Power Systems and Clean Energy (MPCE), the Third Prize Paper Award from CSEE Journal of Powerand Energy Systems (JPES) in 2019, the 2019 IEEE/PES Technical Committee Prize Paper Award, the Applied Energy Highly Cited Paper Awards three times for papers published in 2016, 2020, and 2021, and six Best Conference Papers/Posters awards. As a Principal Investigator, he received the prestigious 2020 R&D 100 Finalist honor for the project "DCNNN (Deep Convolutional Neural Network for N-1)" which is closely related to this book. Also, as a Principal Investigator, he received the prestigious R&D 100 Award in 2020 for the project "CURENT LTB (Large-scale Test Bed)".

Yan Du received her B.S. degree from Tianjin University, Tianjin, in 2013, an M.S. degree from the Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, in 2016, and her Ph.D. degree from The University of Tennessee (UT) in 2020. She received the UT EECS Department Outstanding Graduate Research Assistant award in 2019, the UT Chancellor's Citation Award in Extraordinary Professional Promise in 2020, and the UT Min Kao Fellowship in 2019-2020. Presently, she is a software engineer at Google, Seattle, WA. Her research interest is deep learning in power systems. As the lead developer, she was a co-recipient of the prestigious R&D 100 Finalist honor in 2020 for the project "DCNNN (Deep Convolutional Neural Network for N-1)" which is closely related to this book.

作者簡介(中文翻譯)

方興（Fran）李於1994年和1997年分別獲得南京東南大學的電機工程學士（B.S.E.E.）和碩士（M.S.E.E.）學位，並於2001年獲得維吉尼亞理工大學（Virginia Tech）博士學位。他是田納西大學（University of Tennessee）在諾克斯維爾（Knoxville, TN）的詹姆斯·麥康奈爾教授（James McConnell Professor）。他的研究興趣包括電力系統人工智慧、可再生能源整合、需求響應、電力市場和電力系統控制。他是北卡羅來納州的註冊專業工程師（P.E.），2017年成為IEEE院士（Fellow of the IEEE），目前擔任《IEEE開放存取電力與能源期刊》（IEEE Open Access Journal of Power and Energy, OAJPE）的主編，IEEE/PES電力系統運行、規劃與經濟（Power System Operation, Planning and Economics, PSOPE）委員會的現任主席，以及IEEE/PES電力系統機器學習工作組的現任主席。他於2020年獲得《現代電力系統與清潔能源期刊》（Journal of Modern Power Systems and Clean Energy, MPCE）的最佳論文獎，2019年獲得《中國電力與能源系統學報》（CSEE Journal of Power and Energy Systems, JPES）的三等獎論文獎，2019年IEEE/PES技術委員會獎論文獎，並三次獲得應用能源（Applied Energy）高引用論文獎，分別是2016年、2020年和2021年發表的論文，以及六次最佳會議論文/海報獎。作為主要研究者，他因與本書密切相關的項目「DCNNN（N-1的深度卷積神經網絡）」獲得2020年R&D 100決賽入圍者的榮譽。此外，作為主要研究者，他於2020年因項目「CURENT LTB（大規模測試平台）」獲得2020年R&D 100獎。

顏杜於2013年獲得天津大學的學士學位，2016年獲得中國科學院電力工程研究所的碩士學位，並於2020年獲得田納西大學（UT）的博士學位。她於2019年獲得UT電機與計算機科學系的優秀研究助理獎，2020年獲得UT校長的卓越專業承諾獎，以及2019-2020年的UT閔高獎學金。目前，她是位於華盛頓州西雅圖的Google軟體工程師。她的研究興趣是電力系統中的深度學習。作為首席開發者，她是與本書密切相關的項目「DCNNN（N-1的深度卷積神經網絡）」的共同獲獎者，並於2020年獲得R&D 100決賽入圍者的榮譽。

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