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    Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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    Contributor(s)
    Harrou, Fouzi (editor)
    Sun, Ying (editor)
    Collection
    Knowledge Unlatched (KU)
    Number
    105983
    Language
    English
    Show full item record
    Abstract
    Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.
    URI
    https://library.oapen.org/handle/20.500.12657/43847
    Keywords
    Technology & Engineering; Power Resources; Alternative & Renewable
    DOI
    http://dx.doi.org/10.5772/intechopen.85999
    ISBN
    9781838805463
    Publisher
    InTechOpen
    Publisher website
    https://www.intechopen.com/
    Publication date and place
    2020
    Grantor
    • Knowledge Unlatched
    Imprint
    IntechOpen
    Classification
    Alternative & renewable energy sources & technology
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/legalcode
    • Harvested from KU

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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