A Case for Micromachines in Laboratory-Based DFIG Wind Turbine Systems for Fault Studies

Hamatwi, Ester; Barendse, Paul; Khan, Azeem

doi:10.1109/tia.2022.3232308

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…Reference [64] investigates the correlation between dimensional parameters and electrical asymmetry indicators in laboratory-based systems compared to actual wind generators. The study compares small-scale off-theshelf wound rotor induction machines (WRIMs) and micromachines.…”

Section: Conditional Monitoring: a Literature Review (Wind Turbine)mentioning

confidence: 99%

A survey on data acquisition methods in conditional monitoring of wind turbines

Heibati,

Alipour-Sarabi,

Bathaee

2023

Scientia Iranica

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The generation of electrical power through wind turbines has significantly increased nowadays. However, these systems are prone to faults that can disrupt the network and incur substantial costs for the generation units. Therefore, effective maintenance scheduling becomes crucial. A major challenge faced by wind turbines is their maintenance requirements, as any interruption in their operation and power generation can result in significant economic losses. Consequently, meticulous planning is indispensable to minimize such consequences. This paper that is the first part of the study conducts a survey of data acquisition methods in condition monitoring of wind turbines. In the second part, signal processing techniques for condition monitoring of wind turbines are presented. Furthermore, the paper examines a range of studies that have implemented practical condition monitoring methods in wind turbines, delving into the associated challenges and proposing potential solutions. Various methods such as vibration analysis, acoustic analysis, electrical parameter analysis, AI-based techniques, and fault-tolerant control have been employed for wind turbine maintenance. However, limitations exist in terms of data availability and computational burden. Future challenges include developing algorithms that require less data, reducing computational requirements, updating models with new conditions, enabling early detection and proactive maintenance, and reducing maintenance costs.

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Section: Conditional Monitoring: a Literature Review (Wind Turbine)mentioning

confidence: 99%

A survey on data acquisition methods in conditional monitoring of wind turbines

Heibati,

Alipour-Sarabi,

Bathaee

2023

Scientia Iranica

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“…With the increasing operation time of wind turbines, structural components, both internally and externally, are susceptible to aging, thereby leading to an elevated risk index of generator failure [1]. For wind turbines deployed in the earliest stages of wind power adoption [4,5]. The lifespan of a generator is typically determined by the longevity of the statorwinding insulation material [6].…”

Section: Introductionmentioning

confidence: 99%

“…The lifespan of a generator is typically determined by the longevity of the statorwinding insulation material [6]. According to a relevant survey, winding failures account for more than 29% of faults in electrical machinery, and 36% of these failures were mostly related to stator windings [1,5,7,8]. Stator winding failures are the most common in DFIG and are typically caused by inter-turn short-circuit (ITSC) faults, in which two or more turns in the winding coil are short-circuited.…”

Section: Introductionmentioning

confidence: 99%

“…The CMS of wind turbines is mainly focused on vibration technologies applied to drivetrains, including gearboxes and bearings [1,2]. The motor current signature analysis, as one of the most widely studied methods, is currently being applied to the DFIG [1,2,5]. Now various advanced signal processing methods from the time-domain, frequency-domain, and timefrequency-domain analysis have been used for adaptive noise reduction and feature extraction of current or vibration signals, such as wavelet transform (WT), short-time fourier transform, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), variational mode decomposition (VMD), synchrosqueezing transform and so on [1,11].…”

Section: Introductionmentioning

confidence: 99%

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Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Zhao,

Chen,

Liang

et al. 2023

Meas. Sci. Technol.

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Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function (NCDF) and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from modified multiscale poincare plots, established by support vector data description (SVDD) with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of inter-turn short-circuit fault severity.

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Dual-module multi-head spatiotemporal joint network with SACGA for wind turbines fault detection

Wang,

Yin

2024

Energy

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A Case for Micromachines in Laboratory-Based DFIG Wind Turbine Systems for Fault Studies

Cited by 4 publications

References 31 publications

A survey on data acquisition methods in conditional monitoring of wind turbines

A survey on data acquisition methods in conditional monitoring of wind turbines

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Dual-module multi-head spatiotemporal joint network with SACGA for wind turbines fault detection

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