Power curve monitoring using weighted moving average control charts

Cambron, Philippe; Lepvrier, R.; Masson, Christian; Tahan, Antoine; Pelletier, Francis

doi:10.1016/j.renene.2016.03.031

Cited by 54 publications

(59 citation statements)

References 19 publications

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“…It is also a function of the WT operating modes. Figure 1 from previous work presents the power curve of a WT and illustrates the four zones corresponding to the operating modes [5]. Zone I is where the wind speed is lower than the cut-in speed.…”

Section: Power Curve Of a Wind Turbinementioning

confidence: 99%

“…So as recommended by [8] and done in [5], values with wind speed greater than the rated wind speed will be removed from the dataset. …”

Section: Power Curve Of a Wind Turbinementioning

confidence: 99%

“…A ̅ Shewhart chart is use in [4] and a EWMA chart is use in [5]. The process monitored here is the power residuals.…”

Section: Control Chartsmentioning

confidence: 99%

“…Interesting works have been published lately on various monitoring condition techniques for WT [1,2]. Among the monitoring methods, some authors proposed approaches to monitor the production of a WT using Power Curve Monitoring (PCM) [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…The first one from [4] uses data mining methods for the power curve model together with a Shewhart control chart for the monitoring. This approach will be in direct comparison with the one proposed by [5] which is based on the binning method defined in the IEC 64100-12-1 standard. For this second PCM method, an Exponentially Weighted Moving Average (EWMA) control chart is use to establish if the power curve sustained a change.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of power curve monitoring methods

et al. 2017

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Abstract. Performance monitoring is an important aspect of operating wind farms. This can be done through the power curve monitoring (PCM) of wind turbines (WT). In the past years, important work has been conducted on PCM. Various methodologies have been proposed, each one with interesting results. However, it is difficult to compare these methods because they have been developed using their respective data sets. The objective of this actual work is to compare some of the proposed PCM methods using common data sets. The metric used to compare the PCM methods is the time needed to detect a change in the power curve. Two power curve models will be covered to establish the effect the model type has on the monitoring outcomes. Each model was tested with two control charts. Other methodologies and metrics proposed in the literature for power curve monitoring such as areas under the power curve and the use of statistical copulas have also been covered. Results demonstrate that modelbased PCM methods are more reliable at the detecting a performance change than other methodologies and that the effectiveness of the control chart depends on the types of shift observed.

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Section: Power Curve Of a Wind Turbinementioning

confidence: 99%

“…So as recommended by [8] and done in [5], values with wind speed greater than the rated wind speed will be removed from the dataset. …”

Section: Power Curve Of a Wind Turbinementioning

confidence: 99%

“…A ̅ Shewhart chart is use in [4] and a EWMA chart is use in [5]. The process monitored here is the power residuals.…”

Section: Control Chartsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of power curve monitoring methods

et al. 2017

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Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

Fan

Jia³

et al. 2019

Wind Energy

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Under the framework of normal behavior modeling, this paper develops a novel scheme for fault detection via quantile regression neural networks (QRNNs). The QRNN model is a combination of quantile regressions and neural networks. It is able to identify the normal status or extract the normal behavior data accurately and quickly through lower and upper regression quantiles.Additionally, it is flexible to explore the potential nonlinear patterns contained in the normal status by taking advantage of neural networks. Finally, we monitor the residuals produced from QRNN to detect faults by using the exponentially weighted moving average (EWMA) control chart. The utility of our scheme is illustrated by empirical analyses of bearing fault detection based on Supervisory Control and Data Acquisition (SCADA) data from a wind turbine. We find that the QRNN model outperforms the multiple linear regression (MLR) and back propagation neural networks (BPNNs) in terms of mean absolute error (MAE). Besides, the obtained relationship between the width of control limits in EWMA and the number of alarms provides an important and convenient way for practical applications. KEYWORDS exponentially weighted moving average, fault detection, normal behavior modeling, quantile regression neural networks, wind turbine INTRODUCTIONWind energy is one of the most significant clean renewable energies for electrical generation. An increasing number of wind farms have been built to fulfill human's growing power demand. However, wind turbines (WTs) are typically deployed in a remote or offshore location with abundant wind energy, where performing regular inspections to maintain normal operations will cost a lot. In addition, unscheduled and reactive maintenances could increase the meaningless downtime, causing the loss of revenue. Therefore, how to perform more effective operations and maintenance procedures via monitoring remotely with lower cost has attracted more and more attentions from both academics and practitioners.Condition monitoring systems (CMSs) require not only high-level knowledge about the systems but also the high-cost vibration sensors, which hinders from the prevalence of CMSs. 1-3 On the contrary, the data collected via Supervisory Control and Data Acquisition (SCADA) systems that archive useful information in a convenient manner is universally available. Extensive efforts have been devoted to developing various methods for fault detection of WTs based on SCADA data.Based on SCADA data, the residuals produced from three models including a generalized mapping regressor (GMR), a general regression neural network (GRNN), and a feed-forward multilayer perceptron (MLP) have already been used to monitor the power curve of WTs. 4 The copula approach, estimating bivariate probability distribution function, is proposed to model the power curve so that the deviation between the existing and expected behavior can be checked. 5 The Mahalanobis distance is applied to detect faults in the optional curves containing the power curve, rotor curv...

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Condition Monitoring of Wind Turbines Using Adaptive Control Charts

Han

Chu

2019

Lecture Notes in Mechanical Engineering

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Power curve monitoring using weighted moving average control charts

Cited by 54 publications

References 19 publications

Comparison of power curve monitoring methods

Comparison of power curve monitoring methods

Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

Condition Monitoring of Wind Turbines Using Adaptive Control Charts

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