Robust Smart Grid Stability Prediction under Measurement Perturbations

Rahman, Rodhiya; Afandi, Imron; Saputra, Ilham; Subirto, Pangeran Apriyono; Faruq, Amrul; Effendy, Machmud

doi:10.22146/ijccs.121261

Akses terbuka Jurnal SINTA 2

Robust Smart Grid Stability Prediction under Measurement Perturbations

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 4 (2026)

DOI: 10.22146/ijccs.121261
ISSN: 2460-7258, 1978-1520
Terbit: 2026-10-31
Penerbit: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

Artificial-intelligence-based smart-grid stability predictors are commonly assessed on clean data, leaving their tolerance to measurement degradation insufficiently characterized. This study compares Logistic Regression, Random Forest, XGBoost, SVM-RBF, and an optimized deep neural network using the Electrical Grid Stability Simulated Data. The data were stratified into 70% training, 15% validation, and 15% testing subsets. Trained models were evaluated on a fixed test set under Gaussian noise, Bernoulli–Gaussian impulsive noise, and measurement dropout at five severity levels and 30 paired realizations without retraining. Robustness was quantified using the Matthews correlation coefficient, ROC-AUC, false-negative rate, area under the robustness curve, and paired Friedman and Wilcoxon–Holm tests. SVM-RBF achieved the strongest clean performance and the highest MCC AURC under Gaussian noise and measurement dropout, whereas XGBoost achieved both the highest MCC AURC and the lowest FNR AURC under impulsive noise. Under equal disturbance-family weights, XGBoost obtained the highest mean MCC AURC of 0.843542 and the lowest mean FNR AURC of 0.043506, and it achieved the highest weighted MCC AURC in 78.66% of the evaluated weight combinations. Model selection should therefore reflect both the expected measurement disturbances and the operational metric priorities rather than clean accuracy alone.

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22 penulis · 70 tautan kepenulisan bersama · 16 lintas organisasi

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Universitas Muhammadiyah Malang
University of Technology Malaysia
State University of Makassar
Hasanuddin University
Universiti Teknikal Malaysia Melaka
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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Muhammad Irfan	Universitas Muhammadiyah Malang	6
Ilham Pakaya	Universitas Muhammadiyah Malang	4
Merinda Lestandy	Universitas Muhammadiyah Malang	3
Shahrum Shah Abdullah	University of Technology Malaysia	2
Mochammad Arif Hidayatullah	Universitas Muhammadiyah Malang	2
Ermanu Azizul Hakim	Universitas Muhammadiyah Malang	2
Diding Suhardi	Universitas Muhammadiyah Malang	2
Nur Kasan	Universitas Muhammadiyah Malang	2
Nur Alif	Afiliasi tidak diketahui	2
Lailis Syafaah	Afiliasi tidak diketahui	2
Khaeruddin Khaeruddin	State University of Makassar	1
M. F. Nor Shah	University of Technology Malaysia	1
Fitri Yakub	University of Technology Malaysia	1
Muhammad Irfan	Hasanuddin University	1
Lailis Syafaah	Universitas Muhammadiyah Malang	1
Shamsul Faisal Mohd Hussein	Universiti Teknikal Malaysia Melaka	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, pertama di-harvest 1 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Robust Smart Grid Stability Prediction under Measurement Perturbations</dc:title>
	<dc:creator>Rahman, Rodhiya</dc:creator>
	<dc:creator>Afandi, Imron</dc:creator>
	<dc:creator>Saputra, Ilham</dc:creator>
	<dc:creator>Subirto, Pangeran Apriyono</dc:creator>
	<dc:creator>Faruq, Amrul</dc:creator>
	<dc:creator>Effendy, Machmud</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Artificial Intelligence; Machine Learning; Deep Learning; Smart Grid Analytics</dc:subject>
	<dc:subject xml:lang="en-US">smart grid stability; robustness; measurement noise; machine learning; deep learning</dc:subject>
	<dc:description xml:lang="en-US">Artificial-intelligence-based smart-grid stability predictors are commonly assessed on clean data, leaving their tolerance to measurement degradation insufficiently characterized. This study compares Logistic Regression, Random Forest, XGBoost, SVM-RBF, and an optimized deep neural network using the Electrical Grid Stability Simulated Data. The data were stratified into 70% training, 15% validation, and 15% testing subsets. Trained models were evaluated on a fixed test set under Gaussian noise, Bernoulli–Gaussian impulsive noise, and measurement dropout at five severity levels and 30 paired realizations without retraining. Robustness was quantified using the Matthews correlation coefficient, ROC-AUC, false-negative rate, area under the robustness curve, and paired Friedman and Wilcoxon–Holm tests. SVM-RBF achieved the strongest clean performance and the highest MCC AURC under Gaussian noise and measurement dropout, whereas XGBoost achieved both the highest MCC AURC and the lowest FNR AURC under impulsive noise. Under equal disturbance-family weights, XGBoost obtained the highest mean MCC AURC of 0.843542 and the lowest mean FNR AURC of 0.043506, and it achieved the highest weighted MCC AURC in 78.66% of the evaluated weight combinations. Model selection should therefore reflect both the expected measurement disturbances and the operational metric priorities rather than clean accuracy alone.</dc:description>
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	<dc:date>2026-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
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	<dc:identifier>10.22146/ijccs.121261</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 4 (2026): October</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/121261/45920</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/downloadSuppFile/121261/46672</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/downloadSuppFile/121261/46695</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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