Harmonic Estimation and Forecasting in Sparsely Monitored Uncertain Power Systems Probabilistic and Machine Learning Approaches /
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...
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| Materialtyp: | Elektronisk E-bok |
| Språk: | engelska |
| Publicerad: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Upplaga: | 1st ed. 2025. |
| Serie: | Springer Theses, Recognizing Outstanding Ph.D. Research,
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| Ämnen: | |
| Länkar: | https://doi.org/10.1007/978-3-031-99048-9 |
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| 100 | 1 | |a Zhao, Yuqi. |e author. |0 (orcid)0000-0001-6347-2927 |1 https://orcid.org/0000-0001-6347-2927 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 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. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XVII, 209 p. 85 illus.). |b 78 illus. in color. | ||
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| 490 | 1 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 | |
| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Electric power production. | |
| 650 | 1 | 4 | |a Electrical Power Engineering. |
| 650 | 2 | 4 | |a Mechanical Power Engineering. |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031990472 |
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| 830 | 0 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-99048-9 |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||
| 950 | |a Focus Bundle Energy (SpringerNature-85422) | ||