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DOI: 10.14569/IJACSA.2018.090319
PDF

A Smart Under-Frequency Load Shedding Scheme based on Takagi-Sugeno Fuzzy Inference System and Flexible Load Priority

Author 1: J. A. Laghari
Author 2: Suhail Ahmed Almani
Author 3: Jagdesh Kumar
Author 4: Hazlie Mokhlis

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 3, 2018.

  • Abstract and Keywords
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Abstract: This paper proposes a new smart under frequency load shedding (UFLS) scheme, based on Takagi-Sugeno (TS) fuzzy inference system and flexible load priority. The proposed scheme consists of two parts. First part consists of fuzzy load shed amount estimation module (FLSAEM) which uses TS-fuzzy to estimate the amount of load shed and sends its value to accurate load shedding module (ALSM) to perform accurate load shedding using flexible load priority. The performance of the proposed scheme is tested for intentional islanding case and increment of sudden load in the system. Moreover, the response of the proposed scheme is compared with adaptive UFLS scheme to highlight its advantages. The simulation results show that the proposed UFLS scheme provides the accurate load shedding due to advantage of flexible priority whereas adaptive UFLS scheme due to fixed load priority does not succeed to achieve accurate load shedding.

Keywords: Distributed generation (DG); flexible load priority; fuzzy load shed amount estimation module (FLSAEM), islanded distribution network; under-frequency load shedding (UFLS)

J. A. Laghari, Suhail Ahmed Almani, Jagdesh Kumar and Hazlie Mokhlis, “A Smart Under-Frequency Load Shedding Scheme based on Takagi-Sugeno Fuzzy Inference System and Flexible Load Priority” International Journal of Advanced Computer Science and Applications(IJACSA), 9(3), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090319

@article{Laghari2018,
title = {A Smart Under-Frequency Load Shedding Scheme based on Takagi-Sugeno Fuzzy Inference System and Flexible Load Priority},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090319},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090319},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {3},
author = {J. A. Laghari and Suhail Ahmed Almani and Jagdesh Kumar and Hazlie Mokhlis}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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