Futuristic trends in numerical relaying for transmission line protections /
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Author / Creator: | Patel, Ujjaval, author. |
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Imprint: | Singapore : Springer, 2021. |
Description: | 1 online resource (xxi, 146 p.) : ill. (some col.) |
Language: | English |
Series: | Energy Systems in Electrical Engineering, 2199-8582 Energy Systems in Electrical Engineering, |
Subject: | Electric lines. Electric power transmission. Electrical engineering. Electronic circuits. Electric power transmission. Electric lines. Electrical engineering. Electronic circuits. Electronic books. Electronic books. |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12608889 |
Other authors / contributors: | Bhatt, Praghnesh, author. Chothani, Nilesh G., author. |
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ISBN: | 9789811584657 9811584656 9811584648 9789811584640 |
Digital file characteristics: | text file |
Notes: | Includes bibliographical references. Online resource; title from PDF title page (SpringerLink, viewed December 22, 2020). |
Summary: | This book presents the state-of-the-art approach for transmission line protection schemes for smart power grid. It provides a comprehensive solution for real-time development of numerical relaying schemes for future power grids which can minimize cascade tripping and widespread blackout problems prevailing all around the world. The book also includes the traditional approach for transmission line protection along with issues and challenges in protection philosophy. It highlights the issues for sheltering power grid from unwanted hazards with very fundamental approach. The book follows a step-by-step approach for resolving critical issues like high impedance faults, power swing detection and auto-reclosing schemes with adaptive protection process. The book also covers the topic of hardware solution for real-time implementation of auto-reclosing scheme for transmission line protection schemes along with comparative analysis with the recently developed analytical approach such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and other machine learning algorithms. It will be useful to researchers and industry professionals and students in the fields of power system protection. |
Other form: | Print version: 9811584648 |
Standard no.: | 10.1007/978-981-15-8465-7 |
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