Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals

Moura, Elineudo Pinho de; Melo, Francisco Erivan de Abreu; Damasceno, Filipe Francisco Rocha; Figueiredo, Luis Câmara Campos; Andrade, Carla Freitas de; Almeida, Maurício Soares de; Rocha, Paulo Alexandre Costa

doi:10.1016/j.renene.2016.05.005

Cited by 22 publications

(21 citation statements)

References 39 publications

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“…Neural networks, or artificial neural networks (ANNs), represent a technology with origins in many disciplines: neuroscience, math, statistics, physics, computer science and engineering. Neural networks have applications in fairly diverse fields as classification [37][38][39], clustering [40][41][42], prediction [43][44][45] and pattern recognition [46][47][48][49]. The main reason concerns its property to learn from input data with or without supervision [50,51] through the use of an appropriate training method.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

Rocha

Fernandes

Modolo

et al. 2019

Int J Energy Environ Eng

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A 14-year-long data set containing daily values of meteorological variables was used to train three artificial neural networks (ANNs) for daily, weekly averaged and monthly averaged global solar radiation prediction for Fortaleza, in the Brazilian Northeast region. Local climate is semiarid coastal. Day of the year, maximum temperature, minimum temperature, irradiance, precipitation, cloudiness, extraterrestrial radiation, relative humidity, evaporation and wind speed were adopted as predictors. The ANNs were developed by an in-house code and trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Besides the lack of explicit predictors able to model El Niño and La Niña phenomena, which have strong influence on local weather, the accuracy of the predictions was considered excellent according to its values of normalized root-mean-square error (nRMSE) and good relative to mean absolute percentage error (MAPE) values. Both error metrics presented the smallest values for the monthly case study.

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Section: Related Work and Research Gapsmentioning

confidence: 99%

Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

Rocha

Fernandes

Modolo

et al. 2019

Int J Energy Environ Eng

Self Cite

View full text Add to dashboard Cite

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“…For instance, Swain and Ray [65] proposed the analysis of short circuit fault for a DFIG with active crowbar protection; Qiu et al studied open-circuit fault features and proposed a new fault diagnosis algorithm to accurately detect faulted IGBT in the circuit arms of WT converters [66]. De Moura et al [67] proposed a classification of imbalance levels based on the analysis of vibration signal. Faiz and Moosavi [68] developed a method for detecting air gap and other kinds of eccentricities.…”

Section: Ft Dynamic Analysis For Converter Generator Electrical Andmentioning

confidence: 99%

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

et al. 2017

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Electrical and electronic components are very important subcomponents in modern industrial wind turbines. Complex multimegawatt wind turbines are continuously being installed both onshore and offshore, continuously increasing the demand for sophisticated electronic and electrical components. In this work, most critical electrical and electronic components in industrial wind turbines have been identified and the applicability of appropriate condition monitoring processes simulated. A fault tree dynamic analysis has been carried out by binary decision diagrams to obtain the system failure probability over time and using different time increments to evaluate the system. This analysis allows critical electrical and electronic components of the converters to be identified in different conditions. The results can be used to develop a scheduled maintenance that improves the decision making and reduces the maintenance costs.

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“…Furthermore, it avoids the spurious detection of correlations which are artifacts of nonstationary time series [19]. us, DFA method is applied into many fields, such as climate [20], heart rate dynamic [21][22][23], and mechanical engineering [24][25][26][27][28][29][30][31][32]. Lin and Chen claimed the valuable crossover properties of the scale exponents corresponding to different time scales in double logarithmic chart [26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Xiao

2018

Shock and Vibration

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The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.

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Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals

Cited by 22 publications

References 39 publications

Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

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