Artigo Completo - Open Access.

Idioma principal

SISTEMAS DE MONITORAMENTO DE CONDIÇÃO EM USINAS EÓLICAS

Barreto, Fernanda de Melo; Medeiros, Maria Luiza Azevedo de; Santos, Marllen Aylla Teixeira dos; González, Mario Orestes Aguirre; Cavalcante, Everton Ranielly Sousa; Souza, Marrison Gabriel Guedes de; Souto, Marcus Eduardo Costa;

Artigo Completo:

O desempenho das usinas eólicas possui um papel primordial para a competitividade da indústria eólica no Brasil e no mundo. A ocorrência de falhas nesse tipo de usina pode resultar em consideráveis perdas de geração de energia e comprometer a viabilidade econômica do negócio. A Internet das Coisas possibilita o monitoramento contínuo dos fatores que afetam a disponibilidade e a eficiência das turbinas eólicas por meio de Sistemas de Monitoramento de Condição (Condition Monitoring System - CMS), que permitem a detecção precoce de falhas. Um CMS fornece dados detalhados do desempenho de cada turbina, que são utilizados para otimização da manutenção. O objetivo deste artigo é listar as abordagens mais recentes de Sistemas de Monitoramento de Condição de turbinas eólicas, observando: a integração à rede, o tipo de teste para coleta de dados, as ferramentas / parâmetros de coleta, as ferramentas de detecção / diagnóstico e o objeto de análise. Para tanto foi realizada uma revisão bibliográfica sistemática dos artigos publicados em periódicos entre 2015 e 2017. Foram selecionados 44 artigos. O procedimento da pesquisa consistiu em 5 etapas: (1) formulação do problema, (2) elaboração do protocolo de pesquisa, (3) pesquisa e seleção de artigos, (4) extração das informações e (5) elaboração do framework. Como resultado, é proposto um framework com os elementos que devem ser considerados no desenvolvimento de um CMS adequado à realidade do Brasil.

Artigo Completo:

Palavras-chave: Internet das Coisas, Sistema de Monitoramento de Condição, Turbina Eólica,

Palavras-chave:

DOI: 10.5151/cbgdp2017-055

Referências bibliográficas
  • [1] AHMED, M. A.; KIM, Y. Wireless communication architectures based on data aggregation for internal monitoring of large-scale wind turbines. v. 12, n. 8, 2016.
  • [2] ALKHADAFE, H.; AL-HABAIBEH, A.; LOTFI, A. Condition monitoring of helical gears using automated selection of features and sensors. Measurement, v. 93, p. 164–177, 2016.
  • [3] ATZORI, L.; IERA, A.; MORABITO, G. The Internet of Things: A survey. Computer Networks, v. 54, n. 15, p. 278 –2805, 2010.
  • [4] AZEVEDO, H.; ARAÚJO, A.; BOUCHONNEAU, N. A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, v. 56, p. 368–379, 2016.
  • [5] BANGALORE P, TJERNBERG LB. An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid; 6(2): 980–987, 201
  • [6] BANGALORE, P. et al. An artificial neural network-based condition monitoring method for wind turbines, with application. 2017.
  • [7] BI, R.; ZHOU, C.; HEPBURN, D. M. Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. Renewable Energy, v. 105, p. 674–688, 201
  • [8] BORGIA, E. The Internet of Things vision: Key features, applications and open issues. Computer Communications, v. 54, n. 1, p. 1–31, 2014.
  • [9] CAO, M. et al. Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data. 2016.
  • [10] CHEN, J. et al. Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renewable Energy, v. 89, p. 80–92, 2016a.
  • [11] CHEN, J. et al. Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain. Mechanical Systems and Signal Processing, v. 68–69, p. 44–67, 2016b.
  • [12] CROSS, P.; MA, X. Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines. v. 12, n. February, p. 25–34, 2015.
  • [13] DAI, J. et al. Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data. Renewable Energy, p. 1–10, 2017.
  • [14] ESU, O. O. et al. Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade. Renewable Energy, v. 97, p. 89–96, 2016.
  • [15] GONZÁLEZ, M. O. A.; TOLEDO, J. C., 2012. A integração do cliente no processo de desenvolvimento de produto: revisão bibliográfica sistemática e temas para pesquisa. Produção. 22 (1), 14-26.
  • [16] HA, J. M. et al. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mechanical Systems and Signal Processing, v. 70–71, p. 161–175, 20
  • [17] IRFAN, M. et al. An on-line condition monitoring system for induction motors via instantaneous power analysis †. v. 29, n. 4, p. 1483–1492, 2015.
  • [18] KONSTANTINIDIS, E. I.; BOTSARIS, P. N. Wind turbines: current status, obstacles, trends and technologies. 2016.
  • [19] KUSNICK, J.; ADAMS, D. E.; GRIFFITH, D. T. Wind turbine rotor imbalance detection using nacelle and blade measurements. n. January 2014, p. 267–276, 2015.
  • [20] LEE, J. et al. Transformation algorithm of wind turbine blade moment signals for blade condition monitoring. Renewable Energy, v. 79, p. 209–218, 2015.
  • [21] LIU, W. Y. et al. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. v. 44, p. 466–472, 2015.
  • [22] LUENGO, M. M.; KOLIOS, A. Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review. p. 8339–8354, 2015.
  • [23] MAHESWARI, R. U.; UMAMAHESWARI, R. Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train – A contemporary survey. Mechanical Systems and Signal Processing, v. 85, p. 296–311, 2017.
  • [24] MÁRQUEZ, F. et al. Identification of critical components of wind turbines using FTA over the time. v. 87, 2016.
  • [25] MAY, A.; MCMILLAN, D.; THÖNS, S. Economic analysis of condition monitoring systems for offshore wind turbine sub-systems. v. 9, p. 900–907, 2015.
  • [26] MÜCKE, T. A. et al. Langevin power curve analysis for numerical wind energy converter models with new insights on high frequency power performance. n. August 2014, p. 1953–1971, 2015.
  • [27] MUÑOZ, C. Q. G.; JIMÉNEZ, A. A.; MÁRQUEZ, F. P. G. Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis mez Mu n. p. 1–13, 2017.
  • [28] MUÑOZ, C. Q. G.; MÁRQUEZ, F. P. G.; TOMÁS, J. M. S. Ice detection using thermal infrared radiometry on wind turbine blades. v. 93, p. 157–163, 2016.
  • [29] MUÑOZ, C.; MÁRQUEZ, F. A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines. 2016.
  • [30] ODGAARD, P. F.; STOUSTRUP, J. Annual Reviews in Control Gear-box fault detection using time-frequency based methods R. Annual Reviews in Control, v. 40, p. 50–58, 2015.
  • [31] OH, K. et al. A Novel Method and Its Field Tests for Monitoring and Diagnosing Blade Health for Wind Turbines. v. 64, n. 6, p. 1726–1733, 2015a.
  • [32] OH, K. et al. Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions. Renewable Energy, v. 79, p. 150–160, 2015b.
  • [33] PAI, M.; McCULLOCH, M.; GORMAN, J.; PAI, N.; ENANORIA, W.; KENNDY, G., 2004. Systematic reviews and meta-analyses: An illustrated step-by-step guide. The National Medical Journal of India. 17 (2), 86-95.
  • [34] PÉREZ, J. M. P.; MÁRQUEZ, F. P. G.; HERNÁNDEZ, D. R. Economic viability analysis for icing blades detection in wind turbines. v. 135, p. 1150–1160, 2016.
  • [35] PERISIC, NAVENAKIRKEGAARD, P. H.; PEDERSEN, B. J. Cost-effective shaft torque observer for condition. n. October 2013, p. 1–19, 2015.
  • [36] ROMERO, A. et al. Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System. v. 2016, 2016.
  • [37] SIMM, A. et al. Laser based measurement for the monitoring of shaft misalignment. Measurement, v. 87, p. 104–116, 2016.
  • [38] SINGH, S. et al. Developing RCM strategy for wind turbines utilizing e-condition monitoring. International Journal of System Assurance Engineering and Management, v. 6, n. June, p. 150–156, 2015.
  • [39] STRĄCZKIEWICZ, M.; CZOP, P.; BARSZCZ, T. 1632 . The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings. p. 1760–1769, 2015.
  • [40] TANG, J. et al. An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades. Renewable Energy, v. 99, p. 170–179, 2016.
  • [41] TCHAKOUA, P. et al. Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges. p. 2595–2630, 2014.
  • [42] TENG, W. et al. Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform. Renewable Energy, v. 93, p. 591–598, 2016.
  • [43] VIDAL, Y. et al. Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator. p. 4300–4316, 2015.
  • [44] WANG, S. et al. Dynamic analysis of wind turbines including nacelle – tower – foundation interaction for condition of incomplete structural parameters. v. 9, n. 3, p. 1–17, 2017.
  • [45] WANG, Y.; MA, X.; JOYCE, M. J. Reducing sensor complexity for monitoring wind turbine performance using principal component analysis. Renewable Energy, v. 97, p. 444–456, 2016.
  • [46] WORMS, K. et al. Reliable and lightning-safe monitoring of wind turbine rotor blades using optically powered sensors. n. August 2016, p. 345–360, 2017.
  • [47] YANG, D. et al. Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion. Renewable Energy, v. 92, p. 104–116, 2016.
  • [48] YANG, W.; TIAN, S. W. Research on a power quality monitoring technique for individual wind turbines. Renewable Energy, v. 75, p. 187–198, 2015.
Como citar:

Barreto, Fernanda de Melo; Medeiros, Maria Luiza Azevedo de; Santos, Marllen Aylla Teixeira dos; González, Mario Orestes Aguirre; Cavalcante, Everton Ranielly Sousa; Souza, Marrison Gabriel Guedes de; Souto, Marcus Eduardo Costa; "SISTEMAS DE MONITORAMENTO DE CONDIÇÃO EM USINAS EÓLICAS", p. 543-552 . In: . São Paulo: Blucher, 2017.
ISSN 2318-6968, DOI 10.5151/cbgdp2017-055

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações