IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1351-d220988.html
   My bibliography  Save this article

Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System

Author

Listed:
  • V.K. Arun Shankar

    (Department of Energy and Power Electronics, School of Electrical Engineering, VIT University, Vellore 632014, India)

  • Umashankar Subramaniam

    (Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh 12435, Saudi Arabia)

  • Sanjeevikumar Padmanaban

    (Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Jens Bo Holm-Nielsen

    (Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Frede Blaabjerg

    (Center of Reliable Power Electronics (CORPE), Department of Energy Technology, Aalborg University, 9220 Esbjerg, Denmark)

  • S. Paramasivam

    (Power Electronics and Drives—Research & Development, Danfoss A/S Drives, Chennai 602105, India)

Abstract

Among the total energy consumption by utilities, pumping systems contribute 30%. It is evident that a tremendous energy saving potential is achievable by improving the energy efficiency and reducing faults in the pumping system. Thus, optimal operation of centrifugal pumps throughout the operating region is desired for improved energy efficiency and extended lifetime of the pumping system. The major harmful operations in centrifugal pumps include cavitation and water hammering. The pump faults are simulated in a real-time experimental setup and the operating point of the pump is estimated correspondingly. In this article, the experimental power quality and vibration measurements of cascade pumps during cavitation and water hammering is recorded for different operating conditions. The results are compared with the normal operating conditions of the pumping system for fault prediction and parameter estimation in a cascade water pumping system. Moreover, the Fast Fourier Transform (FFT) analysis comparison of normal and water hammering (faulty condition) highlights the frequency response of the pumping system. Also, the various power quality issues, i.e., voltage, current, total harmonic distortion, power factor, and active, reactive, and apparent power for a cascade multipump control is discussed in this article. The vibration, FFT, and various power quality measurements serve as input data for the classification of faulty pump operating condition in contrast with the normal operation of pumping system.

Suggested Citation

  • V.K. Arun Shankar & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg & S. Paramasivam, 2019. "Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System," Energies, MDPI, vol. 12(7), pages 1-14, April.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1351-:d:220988
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1351/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/7/1351/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    2. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    3. Wang, Zhiyuan & Qian, Zhongdong, 2017. "Effects of concentration and size of silt particles on the performance of a double-suction centrifugal pump," Energy, Elsevier, vol. 123(C), pages 36-46.
    4. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    5. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    6. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    7. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    8. Campana, Pietro Elia & Li, Hailong & Yan, Jinyue, 2013. "Dynamic modelling of a PV pumping system with special consideration on water demand," Applied Energy, Elsevier, vol. 112(C), pages 635-645.
    9. Najafi, Massieh & Auslander, David M. & Bartlett, Peter L. & Haves, Philip & Sohn, Michael D., 2012. "Application of machine learning in the fault diagnostics of air handling units," Applied Energy, Elsevier, vol. 96(C), pages 347-358.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kamil Urbanowicz & Anton Bergant & Apoloniusz Kodura & Michał Kubrak & Agnieszka Malesińska & Paweł Bury & Michał Stosiak, 2021. "Modeling Transient Pipe Flow in Plastic Pipes with Modified Discrete Bubble Cavitation Model," Energies, MDPI, vol. 14(20), pages 1-22, October.
    2. Manickavel Baranidharan & Rassiah Raja Singh, 2022. "AI Energy Optimal Strategy on Variable Speed Drives for Multi-Parallel Aqua Pumping System," Energies, MDPI, vol. 15(12), pages 1-29, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    2. Wang, Zhiyuan & Qian, Zhongdong & Lu, Jie & Wu, Pengfei, 2019. "Effects of flow rate and rotational speed on pressure fluctuations in a double-suction centrifugal pump," Energy, Elsevier, vol. 170(C), pages 212-227.
    3. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    5. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    6. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    7. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    8. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    9. Michał Napierała, 2022. "A Study on Improving Economy Efficiency of Pumping Stations Based on Tariff Changes," Energies, MDPI, vol. 15(3), pages 1-17, January.
    10. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    11. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    12. Xuetao Wang & Qianchuan Zhao & Yifan Wang, 2020. "A Distributed Optimization Method for Energy Saving of Parallel-Connected Pumps in HVAC Systems," Energies, MDPI, vol. 13(15), pages 1-24, July.
    13. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    14. S. M. Mahfuz Alam & Mohd. Hasan Ali, 2020. "Equation Based New Methods for Residential Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-22, December.
    15. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
    16. Zuberi, M. Jibran S. & Tijdink, Anton & Patel, Martin K., 2017. "Techno-economic analysis of energy efficiency improvement in electric motor driven systems in Swiss industry," Applied Energy, Elsevier, vol. 205(C), pages 85-104.
    17. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    18. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    19. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    20. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1351-:d:220988. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.