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All Journal Jurnal Pengabdian Masyarakat Journal of Electrical Engineering and Informatics Media Elektrik
Andi Shridivia Nuran
Universitas Negeri Makassar, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : journal of electrical engineering and informatics

 1 
Transient Stability Analysis of Inverter-Dominated Microgrids using Physics-Informed Deep Learning Andi Nur Faisal; Azizah Fauziah Misbahuddin; Andi Shridivia Nuran
Journal of Electrical Engineering and Informatics Vol. 3 No. 2 (2026): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v3i2.12490

Abstract

Inverter-dominated microgrids exhibit low rotational inertia and fast electromagnetic dynamics, making transient stability assessment significantly more challenging than in synchronous machine dominated systems. This paper proposes a physics informed deep learning (PIDL) framework to estimate post disturbance stability status and critical clearing time (CCT) directly from short window dynamic trajectories while embedding nonlinear inverter dynamics into model training. A total of 1200 disturbance scenarios were generated from reduced order time domain simulations of droop controlled inverter microgrids with randomized virtual inertia, droop damping, fault severity, clearing time, and stochastic renewable fluctuations. The proposed architecture combines a bidirectional temporal encoder with dual output heads and physics residual regularization, followed by two stage optimization (Adam and L-BFGS). On the held out test set, the model achieved 95.56% accuracy, 97.40% precision, 97.40% recall, and 97.40% F1-score for transient stability classification, with CCT error of 29.67 ms MAE and 45.55 ms RMSE. Inference speed reached 1.57 ms per sample, outperforming direct numerical simulation (8.37 ms per sample), and robustness testing under ±20% parameter scaling maintained 95.56% accuracy. These results indicate that integrating physical constraints with deep learning yields a practical and computationally efficient tool for real-time transient stability monitoring in inverter dominated microgrids.
A SHAP-Guided Framework for Sensor Feature Minimization in Human Activity Recognition Andi Shridivia Nuran
Journal of Electrical Engineering and Informatics Vol. 3 No. 2 (2026): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v3i2.12788

Abstract

The increasing adoption of smartphone-based Human Activity Recognition (HAR) systems has led to the generation of high-dimensional sensor features that may introduce redundancy, increase computational complexity, and reduce model efficiency. Although feature selection techniques have been widely investigated, limited studies have explored explainable artificial intelligence approaches for progressive sensor feature minimization while maintaining classification stability. This study proposes a SHAP-guided framework for sensor feature minimization in HAR using the UCI Human Activity Recognition Using Smartphones dataset containing 561 extracted features from accelerometer and gyroscope signals across six human activities. The study employed a quantitative experimental approach using a Random Forest classifier combined with SHAP for feature importance analysis and ranking. Progressive feature reduction experiments were conducted using subsets of 300, 200, 100, 50, and 25 features. The results demonstrated that reducing the feature set from 561 to 100 features achieved approximately 82.17% feature reduction while maintaining competitive classification performance with only a minor decrease in accuracy from 92.50% to 91.75%. Furthermore, the SHAP-guided approach produced lower standard deviation values compared with random feature selection, indicating improved stability and reproducibility across repeated experiments. The novelty of this research lies in the integration of SHAP-based explainability with progressive sensor feature minimization and stability analysis in HAR, providing an interpretable and systematic framework for reducing sensor dimensionality while preserving reliable classification performance.
Co-Authors Andi Nur Faisal azizah fauziah misbahuddin Hamidah Hamris Ramli Rasjid Selvy Zelia Sari