IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3525-d191400.html
   My bibliography  Save this article

Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining

Author

Listed:
  • Mario Klarić

    (Manufacturing and Construction, Dalekovod JSC for Engineering, Zagreb 10000, Croatia)

  • Igor Kuzle

    (Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb 10000, Croatia)

  • Ninoslav Holjevac

    (Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb 10000, Croatia)

Abstract

More and more countries and utilities are trying to develop smart grid projects to make transformation of their power infrastructure towards future grids with increased share of renewable energy production and near zero emissions. The intermittent nature of solar and wind power can in general cause large problems for power system control. Parallel to this process, the aging of existing infrastructure also imposes requirements to utility budgets in the form of a need for large capital investments in reconstruction or maintenance of key equipment. Synchrophasor and other synchronized measurement technologies are setting themselves as one of the solutions for larger wind power integration. With that aim, in this paper one possible solution for wind power control through data mining algorithms used on a large quantity of data gathered from phasor measurement units (PMU) is described. Developed model and algorithm are tested on an IEEE 14 bus test system as well as on real measurements made on wind power plants currently in operation. One such wind power plant is connected to the distribution grid and the other one to the transmission grid. Results are analyzed and compared.

Suggested Citation

  • Mario Klarić & Igor Kuzle & Ninoslav Holjevac, 2018. "Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining," Energies, MDPI, vol. 11(12), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3525-:d:191400
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3525/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3525/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Perica Ilak & Slavko Krajcar & Ivan Rajšl & Marko Delimar, 2014. "Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach," Energies, MDPI, vol. 7(4), pages 1-26, April.
    2. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    3. Igor Ivanković & Igor Kuzle & Ninoslav Holjevac, 2018. "Algorithm for Fast and Efficient Detection and Reaction to Angle Instability Conditions Using Phasor Measurement Unit Data," Energies, MDPI, vol. 11(3), pages 1-21, March.
    4. Michal Wydra, 2018. "Performance and Accuracy Investigation of the Two-Step Algorithm for Power System State and Line Temperature Estimation," Energies, MDPI, vol. 11(4), pages 1-20, April.
    5. Igor Ivanković & Igor Kuzle & Ninoslav Holjevac, 2017. "Wide Area Information-Based Transmission System Centralized Out-of-Step Protection Scheme," Energies, MDPI, vol. 10(5), pages 1-28, May.
    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. Thomas I. Strasser & Sebastian Rohjans & Graeme M. Burt, 2019. "Methods and Concepts for Designing and Validating Smart Grid Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.
    2. Goran Petrovic & Juraj Alojzije Bosnic & Goran Majic & Marin Despalatovic, 2019. "A Design of PWM Controlled Calibrator of Non-Sinusoidal Voltage Waveforms," Energies, MDPI, vol. 12(10), pages 1-14, May.
    3. Gyul Lee & Do-In Kim & Seon Hyeog Kim & Yong-June Shin, 2019. "Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis," Energies, MDPI, vol. 12(4), pages 1-17, February.
    4. Zoran Zbunjak & Igor Kuzle, 2019. "System Integrity Protection Scheme (SIPS) Development and an Optimal Bus-Splitting Scheme Supported by Phasor Measurement Units (PMUs)," Energies, MDPI, vol. 12(17), pages 1-21, September.

    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. Bin Luo & Shumin Miao & Chuntian Cheng & Yi Lei & Gang Chen & Lang Gao, 2019. "Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets," Energies, MDPI, vol. 12(12), pages 1-17, June.
    2. Ngo Minh Khoa & Doan Duc Tung, 2018. "Locating Fault on Transmission Line with Static Var Compensator Based on Phasor Measurement Unit," Energies, MDPI, vol. 11(9), pages 1-14, September.
    3. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
    4. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    6. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    7. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    8. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    9. Mohammed Alnahhal†& Omar Antar & Ahmad Sakhrieh & Muataz Al Hazza, 2024. "Analyzing Energy Consumption in Universities: A Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 18-27, May.
    10. Raveendran, Visal & Alvarez-Bel, Carlos & Nair, Manjula G., 2020. "Assessing the ancillary service potential of electric vehicles to support renewable energy integration in touristic islands: A case study from Balearic island of Menorca," Renewable Energy, Elsevier, vol. 161(C), pages 495-509.
    11. Cezary Stępniak & Dorota Jelonek & Magdalena Wyrwicka & Iwona Chomiak-Orsa, 2021. "Integration of the Infrastructure of Systems Used in Smart Cities for the Planning of Transport and Communication Systems in Cities," Energies, MDPI, vol. 14(11), pages 1-19, May.
    12. Paweł Dymora & Mirosław Mazurek & Bartosz Sudek, 2021. "Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service," Energies, MDPI, vol. 14(22), pages 1-23, November.
    13. Schillinger, Moritz & Weigt, Hannes & Barry, Michael & Schumann, René, 2017. "Hydropower Operation in a Changing Market Environment A Swiss Case Study," Working papers 2017/19, Faculty of Business and Economics - University of Basel.
    14. Simona Vasilica Oprea & Adela Bâra, 2019. "Big Data Solutions for Efficient Operation of Microgrids," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 266-271, August.
    15. Zoran Zbunjak & Igor Kuzle, 2019. "System Integrity Protection Scheme (SIPS) Development and an Optimal Bus-Splitting Scheme Supported by Phasor Measurement Units (PMUs)," Energies, MDPI, vol. 12(17), pages 1-21, September.
    16. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    17. Ana De Las Heras & Amalia Luque-Sendra & Francisco Zamora-Polo, 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    18. Andrey Pazderin & Inga Zicmane & Mihail Senyuk & Pavel Gubin & Ilya Polyakov & Nikita Mukhlynin & Murodbek Safaraliev & Firuz Kamalov, 2023. "Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-43, August.
    19. Abiodun, Kehinde & Hood, Karoline & Cox, John L. & Newman, Alexandra M. & Zolan, Alex J., 2023. "The value of concentrating solar power in ancillary services markets," Applied Energy, Elsevier, vol. 334(C).
    20. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.

    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:11:y:2018:i:12:p:3525-:d:191400. 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.