Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas

Halbianto Adi Sucipto; Dimas Fajar Uman; Adi Soeprijanto

doi:10.1109/ISITIA66279.2025.11137472

Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas

Halbianto Adi Sucipto
, Dimas Fajar Uman
, Adi Soeprijanto

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Upstream oil and gas companies play a vital role in fossil energy production but frequently encounter challenges in maintaining production continuity due to electrical disturbances. In isolated power systems, the design of Under Frequency Load Shedding (UFLS) must prioritize production to minimize economic losses. This study proposes an optimization approach for production sustainability using a UFLS scheme based on Artificial Neural Network (ANN) clustering. The clustering method categorizes loads according to electricity consumption, production levels, and facility priority. The proposed method significantly reduces potential production losses, achieving reductions of 18,661.26 barrels of oil (BO) (equivalent to IDR 1,009,143,475 in 2022) and 25,956 barrels of oil (equivalent to IDR 2,604,520,000 in 2024). These results demonstrate the effectiveness of the ANN-based UFLS scheme in ensuring production continuity and economic efficiency in upstream oil and gas operations.

Original language	English
Title of host publication	26th International Seminar on Intelligent Technology and Its Applications
Subtitle of host publication	Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	549-554
Number of pages	6
Edition	2025
ISBN (Electronic)	9798331537609
DOIs	https://doi.org/10.1109/ISITIA66279.2025.11137472
Publication status	Published - 2025
Event	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025 - Hybrid, Surabaya, Indonesia Duration: 23 Jul 2025 → 25 Jul 2025

Conference

Conference	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025
Country/Territory	Indonesia
City	Hybrid, Surabaya
Period	23/07/25 → 25/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial Neural Network (ANN) Clustering
Oil & gas company
Optimal Production
Under Frequency Load Shedding (UFLS)

Access to Document

10.1109/ISITIA66279.2025.11137472

Cite this

Sucipto, H. A., Uman, D. F., & Soeprijanto, A. (2025). Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings (2025 ed., pp. 549-554). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISITIA66279.2025.11137472

Sucipto, Halbianto Adi ; Uman, Dimas Fajar ; Soeprijanto, Adi. / Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas. 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 549-554

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title = "Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas",

abstract = "Upstream oil and gas companies play a vital role in fossil energy production but frequently encounter challenges in maintaining production continuity due to electrical disturbances. In isolated power systems, the design of Under Frequency Load Shedding (UFLS) must prioritize production to minimize economic losses. This study proposes an optimization approach for production sustainability using a UFLS scheme based on Artificial Neural Network (ANN) clustering. The clustering method categorizes loads according to electricity consumption, production levels, and facility priority. The proposed method significantly reduces potential production losses, achieving reductions of 18,661.26 barrels of oil (BO) (equivalent to IDR 1,009,143,475 in 2022) and 25,956 barrels of oil (equivalent to IDR 2,604,520,000 in 2024). These results demonstrate the effectiveness of the ANN-based UFLS scheme in ensuring production continuity and economic efficiency in upstream oil and gas operations.",

keywords = "Artificial Neural Network (ANN) Clustering, Oil \& gas company, Optimal Production, Under Frequency Load Shedding (UFLS)",

author = "Sucipto, \{Halbianto Adi\} and Uman, \{Dimas Fajar\} and Adi Soeprijanto",

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year = "2025",

doi = "10.1109/ISITIA66279.2025.11137472",

language = "English",

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Sucipto, HA, Uman, DF & Soeprijanto, A 2025, Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas. in 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 edn, Institute of Electrical and Electronics Engineers Inc., pp. 549-554, 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025, Hybrid, Surabaya, Indonesia, 23/07/25. https://doi.org/10.1109/ISITIA66279.2025.11137472

Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas. / Sucipto, Halbianto Adi; Uman, Dimas Fajar; Soeprijanto, Adi.
26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. p. 549-554.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas

AU - Sucipto, Halbianto Adi

AU - Uman, Dimas Fajar

AU - Soeprijanto, Adi

PY - 2025

Y1 - 2025

N2 - Upstream oil and gas companies play a vital role in fossil energy production but frequently encounter challenges in maintaining production continuity due to electrical disturbances. In isolated power systems, the design of Under Frequency Load Shedding (UFLS) must prioritize production to minimize economic losses. This study proposes an optimization approach for production sustainability using a UFLS scheme based on Artificial Neural Network (ANN) clustering. The clustering method categorizes loads according to electricity consumption, production levels, and facility priority. The proposed method significantly reduces potential production losses, achieving reductions of 18,661.26 barrels of oil (BO) (equivalent to IDR 1,009,143,475 in 2022) and 25,956 barrels of oil (equivalent to IDR 2,604,520,000 in 2024). These results demonstrate the effectiveness of the ANN-based UFLS scheme in ensuring production continuity and economic efficiency in upstream oil and gas operations.

AB - Upstream oil and gas companies play a vital role in fossil energy production but frequently encounter challenges in maintaining production continuity due to electrical disturbances. In isolated power systems, the design of Under Frequency Load Shedding (UFLS) must prioritize production to minimize economic losses. This study proposes an optimization approach for production sustainability using a UFLS scheme based on Artificial Neural Network (ANN) clustering. The clustering method categorizes loads according to electricity consumption, production levels, and facility priority. The proposed method significantly reduces potential production losses, achieving reductions of 18,661.26 barrels of oil (BO) (equivalent to IDR 1,009,143,475 in 2022) and 25,956 barrels of oil (equivalent to IDR 2,604,520,000 in 2024). These results demonstrate the effectiveness of the ANN-based UFLS scheme in ensuring production continuity and economic efficiency in upstream oil and gas operations.

KW - Artificial Neural Network (ANN) Clustering

KW - Oil & gas company

KW - Optimal Production

KW - Under Frequency Load Shedding (UFLS)

UR - https://www.scopus.com/pages/publications/105016904702

U2 - 10.1109/ISITIA66279.2025.11137472

DO - 10.1109/ISITIA66279.2025.11137472

M3 - Conference contribution

AN - SCOPUS:105016904702

SP - 549

EP - 554

BT - 26th International Seminar on Intelligent Technology and Its Applications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025

Y2 - 23 July 2025 through 25 July 2025

ER -

Sucipto HA, Uman DF, Soeprijanto A. Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 ed. Institute of Electrical and Electronics Engineers Inc. 2025. p. 549-554 doi: 10.1109/ISITIA66279.2025.11137472

Optimization of Under Frequency Load Shedding Using Artificial Neural Network Clustering for Production Security in Upstream Oil and Gas

Abstract

Conference

UN SDGs

Keywords

Access to Document

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