A combined mono- and multi-turbine approach for fault indicator synthesis and wind turbine monitoring using SCADA data. (April 2019)

• Record Type: Journal Article
• Authors: Lebranchu, Alexis; Charbonnier, Sylvie; Bérenguer, Christophe; Prévost, Frédéric
• Is Part Of: ISA transactions. Volume 87(2019)
• Page Start: 272
• Page End: 281
• Publication Date: 2019-04
• Subjects: Wind turbine monitoring -- Wind farm monitoring -- SCADA data -- Fault detection -- Condition monitoring -- Performance evaluation; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.11.041
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The monitoring of wind turbines using SCADA data has received lately a growing interest from the fault diagnosis community because of the very low cost of these data, which are available in number without the need for any additional sensor. Yet, these data are highly variable due to the turbine constantly changing its operating conditions and to the rapid fluctuations of the environmental conditions (wind speed and direction, air density, turbulence, …). This makes the occurrence of a fault difficult to detect. To address this problem, we propose a multi-level (turbine and farm level) strategy combining a mono- and a multi-turbine approach to create fault indicators insensitive to both operating and environmental conditions. At the turbine level, mono-turbine residuals (i.e. a difference between an actual monitored value and the predicted one) obtained with a normal behavior model expressing the causal relations between variables from the same single turbine and learnt during a normal condition period are calculated for each turbine, so as to get rid of the influence of the operating conditions. At the farm level, the residuals are then compared to a wind farm reference in a multi-turbine approach to obtain fault indicators insensitive to environmental conditions. Indicators for the objective performance evaluation are also proposed to compare wind turbine fault detection methods, which aim at evaluating the cost/benefit of the methods from a production manager's point of view. The performance of the proposed combined mono- and multi-turbine method is evaluated and compared to more classical methods proposed in the literature on a large real data set made of SCADA data recorded on a French wind farm during four years : it is shown than it can improve the fault detection performance when compared to a residual analysis limited at the turbine level only. Highlights: A mono- and a multi-turbine indicator is proposed. It is insensitive to operation and environmental conditions. Mono turbine residuals are built at the turbine level. Mono turbine residuals are compared to a wind farm reference. Objective performance metrics are proposed to evaluate the fault indicators.