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Article

Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining

1
Manufacturing and Construction, Dalekovod JSC for Engineering, Zagreb 10000, Croatia
2
Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb 10000, Croatia
*
Author to whom correspondence should be addressed.
Energies 2018, 11(12), 3525; https://doi.org/10.3390/en11123525
Received: 31 October 2018 / Revised: 2 December 2018 / Accepted: 12 December 2018 / Published: 18 December 2018
(This article belongs to the Special Issue Methods and Concepts for Designing and Validating Smart Grid Systems)
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. View Full-Text
Keywords: smart grid; wind power; synchronized measurements; PMU; data mining smart grid; wind power; synchronized measurements; PMU; data mining
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MDPI and ACS Style

Klarić, M.; Kuzle, I.; Holjevac, N. Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining. Energies 2018, 11, 3525. https://doi.org/10.3390/en11123525

AMA Style

Klarić M, Kuzle I, Holjevac N. Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining. Energies. 2018; 11(12):3525. https://doi.org/10.3390/en11123525

Chicago/Turabian Style

Klarić, Mario, Igor Kuzle, and Ninoslav Holjevac. 2018. "Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining" Energies 11, no. 12: 3525. https://doi.org/10.3390/en11123525

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