A comparative performance evaluation framework for power based wind turbine fault detection methods

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Analyse de données de surveillance et synthèse d'indicateurs de défauts et de dégradation pour l'aide à la maintenance prédictive de parcs de turbines éoliennes

The wind energy sector has rapidly gown in the last 10 years. The number and the size of wind turbines have multiplied, which increases the difficulty and the criticality of the maintenance, and forces the wind turbine industry to change from a corrective and systematic maintenance to a conditional and predictive maintenance. The objective of this research is to develop failure indicators using numerical SCADA data, available at a low price but with a very low sampling frequency (10 min), in order to make online monitoring.A thorough bibliographical analysis on the surveillance of wind farms using SCADA data shows that two types of approaches are usually suggested. The first approach, called mono-turbine, where a good behaviour model of a turbine is learnt over unfaulty periods. With this approach, it is possible to create residuals measuring the difference between the predicted value by the model and the on-line measure, which serves as failure indicators. The mono-turbine models have the peculiarity in that they use variables coming from the same turbine as the farms. The second approach, called multi-turbine, are methods where the similarity between machines is used. Where the most recent researches mostly suggest creating performance curves for every machine on the farm during a period of time and comparing these curves between each other, we make the original proposal to combine both approaches and compare mono-turbine residuals with a farm reference representing the behaviour of the turbines of the farm.We validate in an extensive way those failure indicators by analysing their performances on a database made up of SCADA variable recordings of a duration of 4 years on a windfarm of 6 machines. We also propose relevant performance criteria allowing an estimation in a realistic way of the gains and possible additional costs which would generate these indicators if they were integrated into a tool of maintenance. Therefore, we show that the rate of useless interventions associated with false alarms produced by the failure indicators, which cause a heavy additional cost for the company, can be strongly decreased by the mono-turbines indicators merging that we propose, while preserving a sufficient detection time for the maintenance teams to plan interventions.Le secteur de l’énergie éolienne est en pleine expansion depuis les 10 dernières années. Le nombre et la taille des éoliennes ont été multipliés, ce qui accroît la difficulté et la criticité de la maintenance, et impose aux industriels de passer d’une maintenance corrective et systématique à une maintenance conditionnelle et prédictive. L'objectif de ces travaux est de développer des indicateurs de dégradation, en utilisant les données numériques fournies par le SCADA, disponibles à faible coût mais enregistrées à une faible fréquence d'échantillonnage (10 min) dans un objectif de suivi de production. Une analyse bibliographique approfondie des travaux réalisés sur la surveillance de parcs éoliens à partir de données SCADA montre que deux types d’approches sont généralement proposés. Les méthodes dites mono-turbines où un modèle de comportement d’une turbine est appris sur des périodes de bon fonctionnement, à partir duquel il est possible de créer des résidus mesurant la différence entre la valeur prédite par le modèle et la mesure en ligne, qui servent d’indicateurs de défaut. Les modèles mono-turbines ont la particularité d’utiliser des variables provenant de la même turbine du parc. Les deuxièmes méthodes, dites multi-turbines, sont des méthodes où l'effet parc et la similarité entre machines sont utilisés. Là où les recherches les plus récentes proposent principalement de créer des courbes de performances pour chaque machine du parc pendant une période de temps et de comparer ces courbes entre elles, nous faisons la proposition originale de combiner les deux approches et de comparer les résidus mono-turbines à une référence parc traduisant le comportement majoritaire des turbines du parc. Nous validons de manière extensive ces indicateurs en analysant leurs performances sur une base de données composée d’enregistrements de variables SCADA d’une durée de 4 ans sur un parc de 6 machines. Nous proposons aussi des critères de performances pertinents permettant d’évaluer de manière réaliste les gains et éventuels surcoûts que généreraient ces indicateurs s’ils étaient intégrés dans un outil de maintenance. Ainsi, nous montrons que le taux d’interventions inutiles associées à des fausses alarmes produites par les indicateurs de défaut, et qui provoquent un surcoût très important pour l’entreprise, peut être fortement diminué par la fusion d’indicateurs mono-turbines que nous proposons, tout en conservant une avance à la détection suffisante pour planifier la mise en place d’interventions par les équipes de maintenance

Monitoring data analysis and synthesis of deterioration & failure indicators for predictive maintenance decision-making. Application to offshore windfarms

Le secteur de l’énergie éolienne est en pleine expansion depuis les 10 dernières années. Le nombre et la taille des éoliennes ont été multipliés, ce qui accroît la difficulté et la criticité de la maintenance, et impose aux industriels de passer d’une maintenance corrective et systématique à une maintenance conditionnelle et prédictive. L'objectif de ces travaux est de développer des indicateurs de dégradation, en utilisant les données numériques fournies par le SCADA, disponibles à faible coût mais enregistrées à une faible fréquence d'échantillonnage (10 min) dans un objectif de suivi de production. Une analyse bibliographique approfondie des travaux réalisés sur la surveillance de parcs éoliens à partir de données SCADA montre que deux types d’approches sont généralement proposés. Les méthodes dites mono-turbines où un modèle de comportement d’une turbine est appris sur des périodes de bon fonctionnement, à partir duquel il est possible de créer des résidus mesurant la différence entre la valeur prédite par le modèle et la mesure en ligne, qui servent d’indicateurs de défaut. Les modèles mono-turbines ont la particularité d’utiliser des variables provenant de la même turbine du parc. Les deuxièmes méthodes, dites multi-turbines, sont des méthodes où l'effet parc et la similarité entre machines sont utilisés. Là où les recherches les plus récentes proposent principalement de créer des courbes de performances pour chaque machine du parc pendant une période de temps et de comparer ces courbes entre elles, nous faisons la proposition originale de combiner les deux approches et de comparer les résidus mono-turbines à une référence parc traduisant le comportement majoritaire des turbines du parc. Nous validons de manière extensive ces indicateurs en analysant leurs performances sur une base de données composée d’enregistrements de variables SCADA d’une durée de 4 ans sur un parc de 6 machines. Nous proposons aussi des critères de performances pertinents permettant d’évaluer de manière réaliste les gains et éventuels surcoûts que généreraient ces indicateurs s’ils étaient intégrés dans un outil de maintenance. Ainsi, nous montrons que le taux d’interventions inutiles associées à des fausses alarmes produites par les indicateurs de défaut, et qui provoquent un surcoût très important pour l’entreprise, peut être fortement diminué par la fusion d’indicateurs mono-turbines que nous proposons, tout en conservant une avance à la détection suffisante pour planifier la mise en place d’interventions par les équipes de maintenance.The wind energy sector has rapidly gown in the last 10 years. The number and the size of wind turbines have multiplied, which increases the difficulty and the criticality of the maintenance, and forces the wind turbine industry to change from a corrective and systematic maintenance to a conditional and predictive maintenance. The objective of this research is to develop failure indicators using numerical SCADA data, available at a low price but with a very low sampling frequency (10 min), in order to make online monitoring.A thorough bibliographical analysis on the surveillance of wind farms using SCADA data shows that two types of approaches are usually suggested. The first approach, called mono-turbine, where a good behaviour model of a turbine is learnt over unfaulty periods. With this approach, it is possible to create residuals measuring the difference between the predicted value by the model and the on-line measure, which serves as failure indicators. The mono-turbine models have the peculiarity in that they use variables coming from the same turbine as the farms. The second approach, called multi-turbine, are methods where the similarity between machines is used. Where the most recent researches mostly suggest creating performance curves for every machine on the farm during a period of time and comparing these curves between each other, we make the original proposal to combine both approaches and compare mono-turbine residuals with a farm reference representing the behaviour of the turbines of the farm.We validate in an extensive way those failure indicators by analysing their performances on a database made up of SCADA variable recordings of a duration of 4 years on a windfarm of 6 machines. We also propose relevant performance criteria allowing an estimation in a realistic way of the gains and possible additional costs which would generate these indicators if they were integrated into a tool of maintenance. Therefore, we show that the rate of useless interventions associated with false alarms produced by the failure indicators, which cause a heavy additional cost for the company, can be strongly decreased by the mono-turbines indicators merging that we propose, while preserving a sufficient detection time for the maintenance teams to plan interventions

Review and analysis of SCADA data-based methods for health monitoring of wind turbines

International audienceThe need for renewable energy has led to a fast increase of number of the wind turbines constructed each year. To monitor wind turbines farms, operating and maintenance managers need new effective and automatic tools compatible with a large number of wind turbines. This monitoring task is usually completed by Condition Monitoring System, but researches have been conducted on the utilization of SCADA (Supervisory Control And Data Acquisition) data for condition and predictive maintenance. This paper explains the difficulty of using this new source of information, and introduces the different techniques presented in the literature form 2001 up to 2014. Two classes of approaches can be identified: “internal” approaches using only data from one turbine, and “external” approaches relying on the comparison of one turbine to the other within the same farm. Both approaches have different pros and cons: internal approaches make use of the link between the components in the same turbine and so reduce the influence of operating conditions on fault indicators; external approaches make use of the correlation between SCADA variables measured on different turbines and can thus reduce the influence of wind conditions on the fault indicators. This paper sums up the latest available techniques and it shows that new areas of research can be explored with SCADA data. The obtained fault indicators still remain sensitive to the operating conditions and stochastic variations of the wind load. Combining advantages of the two approaches could reduce both influences

A combined mono- and multi-turbine approach for fault indicator synthesis and wind turbine monitoring using SCADA data

International audienceThe 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 onl

On the Joint Use of An Ensemble of Linear Residuals to Improve Fault Detection in Wind Turbines

International audienceOne of the biggest levers for reducing the cost of wind power generation is to minimize the replacement frequency of large components. To address this need, researchers have focused on the development of real-time health monitoring of component to perform condition-based maintenance. In a previous work, a fault detection solution based on multi- turbine indicators built from automatically generated linear models has been presented and validated on a converter fault case. However, the application of this method on other faults revealed weaknesses in the detection performance, making the solution unreliable. To address these issues, the solution proposed in this study is to consider an ensemble method to automatically generate a set of tri-variable linear models predicting the evolution of a common variable. The linear models are constructed using a constrained greedy selection algorithm, providing unique sets of model variables. From these models, residual-based multi-turbine health indicators are constructed, and a mean linear residual is considered, computed as the mean of seven different indicators. The comparative analysis of these indicators, carried out based on the area under two receiver operating characteristic curves on two fault cases, shows that the use of a mean linear residual computed from a set of linear residuals significantly improves the global detection performance, and thus the reliability of the condition-based maintenance process under development

Data-driven Model Generation Process for Thermal Monitoring of Wind Farm Main Components through Residual Indicators Analysis

International audienceMost of the Supervisory Control and Data Acquisition (SCADA) fault indicators proposed in the literature to detect a fault that induces a temperature increase of the physical components of a wind turbine are temperature residuals. Temperature residuals measure the difference between the current value of the temperature of a component and its prediction by a normal behavior model. In the literature, normal behavior models built from variable selection algorithms are ad-hoc models, designed to correctly predict the temperature of a specific component of a specific turbine of a specific wind farm. In practice, these models cannot be used to predict the temperature of a component of another turbine, let alone a turbine in a different wind farm, because the sensors used by wind turbine manufacturers are not the same. It is therefore impossible for an industrial wind farm manager to deploy a residual-based fault detection system on a wind farm scale. In order to make it possible to deploy these methods in an industrial context, we propose in this paper a methodology to automatically build linear models capable of predicting all temperatures of any component of any turbine of a given wind farm. The method is designed to be easy to implement, interpretable by the operator, and fast to execute to meet industrial constraints. The set of models obtained allows to build a network of thermal state indicators, which can be used for fault isolation. The method is applied to the monitoring of the thermal condition of a real French wind farm for illustration

Simulation of wind turbine faulty production profiles and performance assessment of fault monitoring methods

International audienceWind turbines being one of the fastest growing sources of renewable energy have garnered significant scientific interest for the monitoring and fault analysis using SCADA (supervisory control and data acquisition) data. Various monitoring approaches using power curves, i.e. industry wide characteristic curves expressing produced power as a function of wind speed, have been proposed in the literature. However, an objective comparison of the performance of these methods is difficult. The difficulty comes from (i) the variability in operational and environmental conditions taken into account; (ii) the nature, size and type of data-sets used and (iii) the type and signatures of faults considered for validation. To solve this problem, an approach with a twofold contribution is proposed in this work: 1) an original procedure to generate realistic and controlled simulations of 10 minutes SCADA data, simulating situations when the wind turbine is operating in normal or faulty conditions, is presented; 2) a framework for objective performance assessment of the fault detection methods, based on the proposed controlled and standardized simulation scheme is presented. Objective performance evaluation metrics, such as detection probability and false alarm rates are computed and represented as characteristic receiver operating curves (ROC). The proposed simulation approach is shown to provide a useful global framework for objective performance analysis. A number of realistically simulated and controlled data streams are used to compare and discuss the performances of two fault detection methods referenced in the literature

A multi-turbine approach for improving performance of wind turbine powerbased fault detection methods

International audienceThe relationship between wind speed and the power produced by a wind turbine is expressed by its power curve. Power curves are commonly used to monitor the production performance of a wind turbine by asset managers to ensure optimal production. They can also be used as a tool to detect faults occurring on a wind turbine when the fault causes a decrease in performance. However, the wide dispersion of data generally observed around the reference power curve limits the detection performance of power curve-based techniques. Fault indicators, such as residuals, which measure the difference between the actual power produced and the expected power, are largely affected by this dispersion. To increase the detection performance of power-based fault detection methods, a hybrid solution of mono-multi-turbine residual generation is proposed in this paper to reduce the influence of the power curve dispersion. A new simulation framework, modeling the effect of wind nature (turbulent/laminar) on the wind turbine performance, is also proposed. This allows us to evaluate and compare the performances of two fault detection methods in their multi-turbine implementation. The results show that the application of a multi-turbine approach to a basic residual generation method significantly improves its detection performance and makes it as efficient as a more complex method

SCADA data based realistic simulation framework to evaluate environmental impact on performance of wind turbine condition monitoring systems

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