Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)

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Stability Margin When Wind Turbine Large Scale Penetrated to South Sulawesi-Indonesia Power System Using Optimally Pruned Extreme Learning Machine (OPELM)

Authors
Indar Chaerah Gunadin1, *, Zaenab Muslimin1, Armin Lawi2, Gassing Gassing1, Elly Warni3, Safrizal Safrizal4, Agus Siswanto5
1Department of Electrical Engineering, Faculty of Engineering, Hasanuddin University, Makassar, Indonesia
2Information Systems, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia
3Department of Informatics, Faculty of Engineering, Hasanuddin University, Makassar, Indonesia
4Department of Electrical Engineering, Universitas Islam Nadhlatul Ulama Jepara, Jepara, Indonesia
5Department of Electrical Engineering, Faculty of Engineering, 17 Agustus 1945 of University, Cirebon, Indonesia
*Corresponding author. Email: indar@eng.unhas.ac.id
Corresponding Author
Indar Chaerah Gunadin
Available Online 2 February 2024.
DOI
10.2991/978-94-6463-366-5_21How to use a DOI?
Keywords
Wind Turbine; Stability Margin; Intermittent; REI-net; OPELM
Abstract

The objective of this study is to determine the distance to the point of power system instability that occurs when wind turbine power output suddenly changes. The introduction of large-scale wind turbines affects the stability of the power system, and the intermittency factor has a significant influence on this distance to the instability condition. The South Sulawesi—Indonesia interconnection system is used as a test case in Indonesia. The OPELM method yields an average testing accuracy of 1.537%, which is derived from the training and testing results of the REI net system value.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2024
ISBN
10.2991/978-94-6463-366-5_21
ISSN
1951-6851
DOI
10.2991/978-94-6463-366-5_21How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Indar Chaerah Gunadin
AU  - Zaenab Muslimin
AU  - Armin Lawi
AU  - Gassing Gassing
AU  - Elly Warni
AU  - Safrizal Safrizal
AU  - Agus Siswanto
PY  - 2024
DA  - 2024/02/02
TI  - Stability Margin When Wind Turbine Large Scale Penetrated to South Sulawesi-Indonesia Power System Using Optimally Pruned Extreme Learning Machine (OPELM)
BT  - Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
PB  - Atlantis Press
SP  - 224
EP  - 234
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-366-5_21
DO  - 10.2991/978-94-6463-366-5_21
ID  - Gunadin2024
ER  -