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

Object category:
Elektronische Ressource
Person/Institution:
Harrou, Fouzi
Sun, Ying
Publisher:
[Verlag nicht ermittelbar]
Place of publication:
Erscheinungsort nicht ermittelbar
Date:
2020
Extent, illustration, format:
1 Online-Ressource
Language:
Englisch
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
Object text:
English

Access and usage options

Universität Erfurt
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Created:
2023-04-13
Last changed:
2022-04-26
Added to portal:
2023-04-13