On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

Pozo, Francesc; Vidal, Yolanda; Serrahima, Josep M.

doi:10.3390/en9070520

Cited by 23 publications

(25 citation statements)

References 15 publications

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“…In some of these cases, PCA can be combined with univariate and multivariate hypothesis testing to correctly classify the current state of the structure. Both univariate [3] and multivariate [176] hypothesis testing have been used for damage detection in small-scale structures as well as for fault detection in wind turbines [177][178][179].…”

Section: Development Of Statistical Modelsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Section: Development Of Statistical Modelsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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“…Ruiz et al [19] assign one of the first six letters of the alphabet to each one of the six different ways of unfolding. In this chapter, as well as in [6,8,20,21], we have considered the so-called type E. However, we will present the collected SCADA data arranged in an already unfolded matrix.…”

Section: Data-driven Baseline Modeling Based On Mpcamentioning

confidence: 99%

“…5and L is the number of columns of the same matrix. The effect on the overall performance of the condition monitoring strategy on the choice of n and L is thoroughly analyzed on [21].…”

Section: Data-driven Baseline Modeling Based On Mpcamentioning

confidence: 99%

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Condition Monitoring of Wind Turbine Structures through Univariate and Multivariate Hypothesis Testing

Pozo¹,

Vidal²

2018

Structural Health Monitoring From Sensing to Processing

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This chapter presents a fault detection method through uni-and multivariate hypothesis testing for wind turbine (WT) faults. A data-driven approach is used based on supervisory control and data acquisition (SCADA) data. First, using a healthy WT data set, a model is constructed through multiway principal component analysis (MPCA). Afterward, given a WT to be diagnosed, its data are projected into the MPCA model space. Since the turbulent wind is a random process, the dynamic response of the WT can be considered as a stochastic process, and thus, the acquired SCADA measurements are treated as a random process. The objective is to determine whether the distribution of the multivariate random samples that are obtained from the WT to be diagnosed (healthy or not) is related to the distribution of the baseline. To this end, a test for the equality of population means is performed in both the univariate and the multivariate cases. Ultimately, the test results establish whether the WT is healthy or faulty. The performance of the proposed method is validated using an advanced benchmark that comprehends a 5-MW WT subject to various actuators and sensor faults of different types.

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“…The current need for data gathering and analysis is highlighted in [8] proposing "new conceptual expert systems that automate data processing and provide analysis to enhance decision-making". Efforts have been made with such goal by implementing SCADA-data-driven methods [9] that also look into optimizing the number of sensors while reducing the detection time [10,11].…”

Section: Introductionmentioning

confidence: 99%

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Artigao

Koukoura

Carroll³

et al. 2018

Energies

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Abstract:The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.

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On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

Cited by 23 publications

References 15 publications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Condition Monitoring of Wind Turbine Structures through Univariate and Multivariate Hypothesis Testing

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

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