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Open AccessArticle

Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic

Gdańsk University of Technology, 11/12 Gabriela Narutowicza Street, 80-233 Gdańsk, Poland
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Energies 2020, 13(6), 1456; https://doi.org/10.3390/en13061456
Received: 17 February 2020 / Revised: 19 March 2020 / Accepted: 20 March 2020 / Published: 20 March 2020
In contemporary power systems, the load shedding schemes are typically based on disconnecting a pre-specified amount of load after the frequency drops below a predetermined value. The actual conditions at the time of disturbance may largely differ from the assumptions, which can lead to non-optimal or ineffective operation of the load shedding scheme. For many years, increasing the effectiveness of the underfrequency load shedding (UFLS) schemes has been the subject of research around the world. Unfortunately, the proposed solutions often require costly technical resources and/or large amounts of real-time data monitoring. This paper puts forth an UFLS scheme characterized by increased effectiveness in the case of large disturbances and reduced disconnected power in the case of small and medium disturbances compared to the conventional load-shedding solutions. These advantages are achieved by replacing time-consuming consecutive load dropping with the simultaneous load dropping mechanism and by replacing ineffective fixed-frequency activation thresholds independent of the state of the system with implicit adaptive thresholds based on fuzzy logic computations. The proposed algorithm does not require complex and costly technical solutions. The performance of the proposed scheme was validated using multivariate computer simulations. Selected test results are included in this paper. View Full-Text
Keywords: underfrequency load shedding; islanding operation; adaptive load shedding; fuzzy logic underfrequency load shedding; islanding operation; adaptive load shedding; fuzzy logic
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Małkowski, R.; Nieznański, J. Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic. Energies 2020, 13, 1456.

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