Authors:
Afshin Tafazzoli
1
and
Alvaro Novoa Mayo
2
Affiliations:
1
Global Services, Siemens Gamesa Renewable Energy, Calle Ramirez Arellano, 37, Madrid 28043 and Spain
;
2
Energy Consultant, KPMG, Torre de Cristal, Paseo de la Castellana, 259C, Madrid 28046 and Spain
Keyword(s):
Wind Turbine Generator (WTG), Artificial Intelligence (AI), Condition Monitoring System (CMS).
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy Monitoring
;
Energy-Aware Systems and Technologies
;
Renewable Energy Resources
Abstract:
This project is motivated by the importance of wind energy and reducing the financial and operational impact of faults in wind turbine generator using artificial intelligence based condition monitoring system. It is to classify the fault alarms and diagnose smart solutions at level zero to resolve the faults without service expert’s intervention. Big data analysis of the large historical data pool results in the intelligent algorithms that can power the diagnostic models. For maximum efficiency, wind turbines tend to be located in remote locations such as on offshore platforms. However, this remoteness leads to high maintenance costs and high downtime when faults occur. These factors highlight the importance of early fault detection and fast resolution in great extent. The aim of the project has been to have smart wind turbines integrated with artificial intelligence. The condition monitoring system should have the capability to detect, identify, and locate a fault in a wind turbine
and remotely reset the turbines whenever possible.
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