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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Harmonic estimation and forecasting in sparsely monitored uncertain power systems/ by 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."
內容註:	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
標題:	Electric power distribution - Mathematical models. -
電子資源:	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 /
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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.
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