Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free IoT-based approach to condition monitoring of the wave power generation system

© The Institution of Engineering and Technology
Received 11/10/2018, Accepted 10/06/2019, Revised 19/05/2019, Published 11/06/2019

Accurate and reliable fault detecting plays a key role in application of grid-connected wave power generation systems. This study presents a novel IoT-based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform; and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT-based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications.

Inspec keywords: learning (artificial intelligence); fault diagnosis; power grids; wave power generation; wave power plants; condition monitoring; Internet of Things

Other keywords: extreme learning machine algorithm; cloud platform; grid-connected wave power generation systems; CS method; IoT-based wave power generation condition monitoring system test platform; ELM algorithm; compressed sensing method

Subjects: Inspection and quality control; Knowledge engineering techniques; Power engineering computing; Mobile, ubiquitous and pervasive computing; Reliability; Wave power

References

    1. 1)
      • 1. Reikard, G., Robertson, B., Bidlot, J.-R.: ‘Wave energy worldwide: simulating wave farms, forecasting, and calculating reserves’, Int. J. Marine Energy, 2017, 17, pp. 156–185.
    2. 2)
      • 6. Sotres, P., Lanza, J., Sanchez, L., et al: ‘Breaking vendors and city locks through a semantic-enabled global interoperable internet-of-things system: A smart parking case’, Sensors (Basel), 2019, 19, pp. 229.
    3. 3)
      • 12. Ghosh, S., Chakraborty, T., Saha, S., et al: ‘Development of the location suitability index for wave energy production by ANN and MCDM techniques’, Renew. Sust. Energy Rev., 2016, 59, pp. 1017–1028.
    4. 4)
      • 18. Feng, G., Huang, G.B., Lin, Q., et al: ‘Error minimized extreme learning machine with growth of hidden nodes and incremental learning’, IEEE Trans. Neural Netw., 2009, 20, pp. 1352–1357.
    5. 5)
      • 22. Liang, N.Y., Huang, G.B., Saratchandran, P., et al: ‘A fast and accurate online sequential learning algorithm for feedforward networks’, IEEE Trans. Neural Netw., 2006, 17, pp. 1411–1423.
    6. 6)
      • 21. Yang, J., Zhang, D.-H., Chen, Y., et al: ‘Design, optimization and numerical modelling of a novel floating pendulum wave energy converter with tide adaptation’, China Ocean Eng., 2017, 31, pp. 578–588.
    7. 7)
      • 24. Alam, K.M., El Saddik, A.: ‘C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems’, IEEE. Access., 2017, 5, pp. 2050–2062.
    8. 8)
      • 9. Wang, Y., Mao, S., Nelms, R. M.: ‘Distributed online algorithm for optimal real-time energy distribution in the smart grid’, IEEE Internet Things J., 2014, 1, pp. 70–80.
    9. 9)
      • 30. Bangalore, P., Tjernberg, L.B.: ‘An artificial neural network approach for early fault detection of gearbox bearings’, IEEE Trans. Smart Grid, 2015, 6, pp. 980–987.
    10. 10)
      • 17. Zhang, R., Lan, Y., Huang, G.B., et al: ‘Dynamic extreme learning machine and its approximation capability’, IEEE. Trans. Cybern., 2013, 43, pp. 2054–2065.
    11. 11)
      • 15. Huibin, Z., Jon Finney, S., Massoud, A., et al: ‘An SVM algorithm to balance the capacitor voltages of the three-level NPC active power filter’, IEEE Trans. Power Electron., 2008, 23, pp. 2694–2702.
    12. 12)
      • 29. Zhang, D.-H., Li, W., Zhao, H.-T., et al: ‘design of a hydraulic power take-off system for the wave energy device with an inverse pendulum’, China Ocean Eng., 2014, 28, pp. 283–292.
    13. 13)
      • 3. Hüffmeier, J.: ‘Condition monitoring systems of wave energy converters’, Reliability in a Sea of Risk 2, 2018, pp. 1–51.
    14. 14)
      • 16. Guang-Bin, H., Qin-Yu, Z., Mao, K.Z., et al: ‘Can threshold networks be trained directly?’, IEEE Trans. Circuits Syst. II, Express Briefs, 2006, 53, pp. 187–191.
    15. 15)
      • 28. Ou, G.W., Ling, B.W.K., Lun, D.P.K.: ‘Compressive sensing of images based on discrete periodic radon transform’, Electron. Lett., 2014, 50, pp. 591–593.
    16. 16)
      • 19. Qian, P., Ma, X., Zhang, D., et al: ‘Data-driven condition monitoring approaches to improving power output of wind turbines’, IEEE Trans. Ind. Electron., 2019, 66, (8), pp. 6012–6020.
    17. 17)
      • 2. Penalba, M., Giorgi, G., Ringwood, J. V.: ‘Mathematical modelling of wave energy converters: A review of nonlinear approaches’, Renew. Sust. Energy Rev., 2017, 78, pp. 1188–1207.
    18. 18)
      • 13. Qiao, W., Lu, D.: ‘A survey on wind turbine condition monitoring and fault diagnosis – Part II: signals and signal processing methods’, IEEE Trans. Ind. Electron., 2015, 62, pp. 6546–6557.
    19. 19)
      • 20. Zang, Z., Zhang, Q., Qi, Y., et al: ‘Hydrodynamic responses and efficiency analyses of a heaving-buoy wave energy converter with PTO damping in regular and irregular waves’, Renew. Energy, 2018, 116, pp. 527–542.
    20. 20)
      • 7. Casado-Vara, R., Novais, P., Gil, A. B., et al: ‘Distributed continuous-time fault estimation control for multiple devices in IoT networks’, IEEE. Access., 2019, 7, pp. 11972–11984.
    21. 21)
      • 27. Liu, N., Zhang, J., Zhang, H., et al: ‘Security assessment for communication networks of power control systems using attack graph and MCDM’, IEEE Trans. Power Deliv., 2010, 25, pp. 1492–1500.
    22. 22)
      • 5. Xu, L. D., He, W., Li, S.: ‘Internet of things in industries: A survey’, IEEE Trans. Ind. Inf., 2014, 10, pp. 2233–2243.
    23. 23)
      • 23. Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J.C., et al: ‘Computational intelligence in wave energy: comprehensive review and case study’, Renew. Sust. Energy Rev., 2016, 58, pp. 1223–1246.
    24. 24)
      • 14. Zeng, J., Lu, D., Zhao, Y., et al:‘Wind turbine fault detection and isolation using support vector machine and a residual-based metho’. presented at the American Control Conf. (ACC),Washington, DC, USA, 2013.
    25. 25)
      • 11. Ozcelik, B., Erzurumlu, T.: ‘Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm’, J. Mater. Process. Technol., 2006, 171, pp. 437–445.
    26. 26)
      • 8. Shifeng, F., Li Da, X., Yunqiang, Z., et al: ‘An integrated system for regional environmental monitoring and management based on internet of things’, IEEE Trans. Ind. Inf., 2014, 10, pp. 1596–1605.
    27. 27)
      • 4. Qian, P., Ma, X., Cross, P.: ‘Integrated data-driven model-based approach to condition monitoring of the wind turbine gearbox’, IET Renew. Power Gener., 2017, 11, pp. 1177–1185.
    28. 28)
      • 26. Cross, P., Ma, X.: ‘Nonlinear system identification for model-based condition monitoring of wind turbines’, Renew. Energy, 2014, 71, pp. 166–175.
    29. 29)
      • 25. Zamani, A., Solomatine, D., Azimian, A., et al: ‘Learning from data for wind–wave forecasting’, Ocean Eng., 2008, 35, pp. 953–962.
    30. 30)
      • 10. Borchersen, A.B., Kinnaert, M.: ‘Model-based fault detection for generator cooling system in wind turbines using SCADA data’, Wind Energy, 2016, 19, pp. 593–606.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5918
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5918
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address