SCADA data based condition monitoring of wind turbines

Wind turbines (WTs) are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process r...

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Bibliographic Details
Published inAdvances in manufacturing Vol. 2; no. 1; pp. 61 - 69
Main Authors Wang, Ke-Sheng, Sharma, Vishal S., Zhang, Zhen-You
Format Journal Article
LanguageEnglish
Published Heidelberg Shanghai University 01.03.2014
Springer Nature B.V
Subjects
Algorithms
Alternative energy sources
Artificial intelligence
Condition monitoring
Control
Data mining
Data processing
Electric industries
Electricity
Electronic devices
Engineering
Fault detection
Fault diagnosis
Machines
Maintenance
Manufacturing
Mechatronics
Nanotechnology and Microengineering
Neural networks
Power consumption
Processes
Prognosis
Repair & maintenance
Robotics
Supervisory control
Supervisory control and data acquisition
Turbines
Wind power
Wind turbines
Windmills
Denmark
United States > US
Data-driven approaches
Central monitoring system (CMS)
Diagnosis and prognosis of wind turbines
SCADA data
Artificial intelligence (AI)
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Summary:Wind turbines (WTs) are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process reasonable. Mostly the WTs are equipped with some kind of condition monitoring device/system, which provides the information about the device to the central data base i.e., supervisory control and data acquisition (SCADA) data base. These devices/systems make use of data processing techniques/methods in order to detect and predict faults. The information provided by condition monitoring equipments keeps on recoding in the SCADA data base. This paper dwells upon the techniques/methods/algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data. Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence (AI) based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed.
ISSN:2095-3127
2195-3597
DOI:10.1007/s40436-014-0067-0