東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Harmonic estimation and forecasting ...

Zhao, Yuqi.

Linked to FindBook

Google Book

Amazon

博客來

Harmonic estimation and forecasting in sparsely monitored uncertain power systems = probabilistic and machine learning approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Harmonic estimation and forecasting in sparsely monitored uncertain power systems/ by Yuqi Zhao.
Reminder of title:	probabilistic and machine learning approaches /
Author:	Zhao, Yuqi.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvii, 209 p. :ill. (chiefly col.), digital ;24 cm.
Notes:	"Doctoral thesis accepted by the University of Manchester, Manchester, UK."
[NT 15003449]:	Introduction -- Power System Harmonic Modelling -- Harmonic Simulation Studies -- Probabilistic Harmonic Estimation in Non-Radial Distribution Networks -- Application of Machine Learning for Harmonic Estimation in Transmission Networks -- Robustness of the Methodology for Harmonic Forecasting in Transmission Networks -- Conclusions and Future Work -- Appendices.
Contained By:	Springer Nature eBook
Subject:	Electric power distribution - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-99048-9
ISBN:	9783031990489

Harmonic estimation and forecasting in sparsely monitored uncertain power systems = probabilistic and machine learning approaches /
Zhao, Yuqi.

Harmonic estimation and forecasting in sparsely monitored uncertain power systemsprobabilistic and machine learning approaches /[electronic resource] :by Yuqi Zhao. - Cham :Springer Nature Switzerland :2025. - xvii, 209 p. :ill. (chiefly col.), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis accepted by the University of Manchester, Manchester, UK."

Introduction -- Power System Harmonic Modelling -- Harmonic Simulation Studies -- Probabilistic Harmonic Estimation in Non-Radial Distribution Networks -- Application of Machine Learning for Harmonic Estimation in Transmission Networks -- Robustness of the Methodology for Harmonic Forecasting in Transmission Networks -- Conclusions and Future Work -- Appendices.

This book tackles the technical challenges of integrating renewable energy sources into power grids to reduce exposure to significant financial and operational risks. It does so by introducing advanced methods for harmonic estimation and forecasting in sparsely monitored and uncertain power networks, leveraging probabilistic and machine learning techniques. With a focus on practical applications, the book introduces a Monte-Carlo-based simulation framework to address operational randomness and uncertainties, along with the development of a Norton equivalent model of wind farms for probabilistic harmonic propagation studies. The author also presents cost-effective methods for harmonic estimation in non-radial distribution networks and proposes a sequential artificial-neural-network-based approach for probabilistic harmonic forecasting in transmission networks with limited harmonic measurements. By significantly reducing the reliance on extensive power-quality-monitoring installations, these methods provide robust, accurate, and reliable harmonic data and enable more effective and informed decision-making for future power system operations. Targeted at academic researchers, industrial engineers, and graduate students, this book matches theoretical advance with practical application. It supports the assessment of standard compliance and benchmarking, minimizes the need for power-quality-monitoring installations, accelerates the evaluation of harmonic propagation and mitigation strategies in uncertain, power-electronics-rich networks, and advances the forecasting of potential harmonic issues in future power systems.

ISBN: 9783031990489

Standard No.: 10.1007/978-3-031-99048-9doiSubjects--Topical Terms:

3462971
Electric power distribution
--Mathematical models.

LC Class. No.: TK3091

Dewey Class. No.: 621.319

Harmonic estimation and forecasting in sparsely monitored uncertain power systems = probabilistic and machine learning approaches /
LDR:03181nmm a2200349 a 4500 001 2422690
003 DE-He213
005 20260102120702.0
006 m d
007 cr nn 008maaau
008 260505s2025 sz s 0 eng d
020 $a 9783031990489 $q (electronic bk.)
020 $a 9783031990472 $q (paper)
024 7 $a 10.1007/978-3-031-99048-9 $2 doi
035 $a 978-3-031-99048-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TK3091
072 7 $a THR $2 bicssc
072 7 $a TEC031020 $2 bisacsh
072 7 $a THR $2 thema
082 0 4 $a 621.319 $2 23
090 $a TK3091 $b .Z63 2025
100 1 $a Zhao, Yuqi. $0 (orcid)0000-0002-4438-3635 $3 3773242
245 1 0 $a Harmonic estimation and forecasting in sparsely monitored uncertain power systems $h [electronic resource] : $b probabilistic and machine learning approaches / $c by Yuqi Zhao.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xvii, 209 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5061
500 $a "Doctoral thesis accepted by the University of Manchester, Manchester, UK."
505 0 $a Introduction -- Power System Harmonic Modelling -- Harmonic Simulation Studies -- Probabilistic Harmonic Estimation in Non-Radial Distribution Networks -- Application of Machine Learning for Harmonic Estimation in Transmission Networks -- Robustness of the Methodology for Harmonic Forecasting in Transmission Networks -- Conclusions and Future Work -- Appendices.
520 $a This book tackles the technical challenges of integrating renewable energy sources into power grids to reduce exposure to significant financial and operational risks. It does so by introducing advanced methods for harmonic estimation and forecasting in sparsely monitored and uncertain power networks, leveraging probabilistic and machine learning techniques. With a focus on practical applications, the book introduces a Monte-Carlo-based simulation framework to address operational randomness and uncertainties, along with the development of a Norton equivalent model of wind farms for probabilistic harmonic propagation studies. The author also presents cost-effective methods for harmonic estimation in non-radial distribution networks and proposes a sequential artificial-neural-network-based approach for probabilistic harmonic forecasting in transmission networks with limited harmonic measurements. By significantly reducing the reliance on extensive power-quality-monitoring installations, these methods provide robust, accurate, and reliable harmonic data and enable more effective and informed decision-making for future power system operations. Targeted at academic researchers, industrial engineers, and graduate students, this book matches theoretical advance with practical application. It supports the assessment of standard compliance and benchmarking, minimizes the need for power-quality-monitoring installations, accelerates the evaluation of harmonic propagation and mitigation strategies in uncertain, power-electronics-rich networks, and advances the forecasting of potential harmonic issues in future power systems.
650 0 $a Electric power distribution $x Mathematical models. $3 3462971
650 0 $a Renewable energy sources. $3 543030
650 1 4 $a Electrical Power Engineering. $3 3592498
650 2 4 $a Mechanical Power Engineering. $3 3592501
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 1314442
856 4 0 $u https://doi.org/10.1007/978-3-031-99048-9
950 $a Engineering (SpringerNature-11647)