國立高雄大學圖資館 |

登入

回首頁

Harmonic estimation and forecasting in sparsely monitored uncertain power systemsprobabilistic and machine learning approaches /

紀錄類型:	書目-電子資源 : Monographic component part
正題名/作者:	Harmonic estimation and forecasting in sparsely monitored uncertain power systemsby Yuqi Zhao.
其他題名:	probabilistic and machine learning approaches /
作者:	Zhao, Yuqi.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xvii, 209 p. :ill. (chiefly col.), digital ;24 cm.
附註:	"Doctoral thesis accepted by the University of Manchester, Manchester, UK."
Contained By:	Springer Nature eBook
標題:	Electric power distributionMathematical models.
電子資源:	https://doi.org/10.1007/978-3-031-99048-9
ISBN:	9783031990489$q(electronic bk.)

Harmonic estimation and forecasting in sparsely monitored uncertain power systemsprobabilistic 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$q(electronic bk.)

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

282440
Electric power distribution
--Mathematical models.

LC Class. No.: TK3091

Dewey Class. No.: 621.319

Harmonic estimation and forecasting in sparsely monitored uncertain power systemsprobabilistic and machine learning approaches /
LDR:03182nmam 22003493 a 450 001 691753
003 DE-He213
005 20260102120702.0
006 m d
007 cr nn 008maaau
008 260527s2025 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. $3 1008144
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 282440
650 0 $a Renewable energy sources. $3 190766
650 1 4 $a Electrical Power Engineering. $3 919410
650 2 4 $a Mechanical Power Engineering. $3 919412
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 557607
856 4 0 $u https://doi.org/10.1007/978-3-031-99048-9
950 $a Engineering (SpringerNature-11647)