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

Indar Chaerah Gunadin; Zaenab Muslimin; Armin Lawi; Gassing Gassing; Elly Warni; Safrizal Safrizal; Agus Siswanto

doi:10.2991/978-94-6463-366-5_21

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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_21 How 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.
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: 978-94-6463-366-5
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-366-5_21 How to use a DOI?
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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ris enw bib

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  -

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