Harmonic Estimation and Forecasting in Sparsely Monitored Uncertain Power Systems
Probabilistic and Machine Learning Approaches
Springer
ISBN 978-3-031-99047-2
Standardpreis
Bibliografische Daten
Fachbuch
Buch. Hardcover
2025
28 s/w-Abbildungen, 55 Farbabbildungen.
In englischer Sprache
Umfang: xvi, 232 S.
Format (B x L): 15,5 x 23,5 cm
Verlag: Springer
ISBN: 978-3-031-99047-2
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Das Werk ist Teil der Reihe: Springer Theses
Produktbeschreibung
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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