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

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REAL-TIME SOLAR PANEL FAULT DETECTION USING YOLOv8-BASED DEEP LEARNING APPROACH Andi Nur Faisal; Ali Isra
Jurnal Media Elektrik Vol. 22 No. 3 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v22i3.9182

Abstract

This study presents the implementation of the YOLOv8s-cls model for automatic classification of solar panel surface conditions into six categories: Clean, Dusty, Bird-drop, Snow-Covered, Electrical-Damage, and Physical-Damage. A dataset comprising 619 images was used to train the modified YOLOv8s-cls architecture, spanning 50 epochs with a batch size of 16, input dimensions set to 128×128, and the AdamW optimizer applied throughout. The training was conducted on a CPU-only system, yet the inference benchmark was performed in a separate testing phase, yielding an average inference time of 0.032 seconds per image, indicating strong feasibility for real-time deployment. The achieved accuracies were 85.88% for Top-1 and 99.44% for Top-5 predictions, demonstrating robust performance in multi-class classification tasks. Nonetheless, some visual ambiguities remained between similar classes such as Dusty vs. Snow-Covered and Electrical-Damage vs. Physical-Damage. These results affirm the effectiveness of YOLOv8s-cls as a lightweight and adaptable deep learning solution for solar panel condition monitoring. Future enhancements are proposed, including targeted data augmentation, texture-based preprocessing, and deployment on GPU-accelerated or edge-optimized platforms to improve generalization and deployment flexibility in real-world settings.
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.
Continuous-Time Transient Stability Assessment of Inverter-Dominated Microgrids viaPhysics-Informed Neural ODEs Andi Nur Faisal
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.v3i2.12701

Abstract

Objective: Inverter-dominated microgrids exhibit low rotational inertia and fast electro-magnetic dynamics, making transient stability assessment significantly more challenging than in synchronous-machine-dominated systems. This paper pro-poses a Physics-Informed Neural ODE (PI-NODE) framework that models post-disturbance inverter dynamics in continuous time, embedding the governing ODE as a hybrid of known droop-controlled inverter physics and a learned neural correction solved forward by an adaptive-step Dormand-Prince integrator. Gradients are propagated through the solver via the continuous adjoint method, keeping memory cost constant with respect to integration depth. A total of 1200 disturbance scenarios were generated from reduced-order time-domain simulations of droop-controlled grid-forming inverter microgrids with randomized virtual inertia, droop damping, fault severity, clearing time, and stochastic renewable fluctuations. On the held-out test set (180 scenarios), PI-NODE achieved 95.56% accuracy, 98.67% precision, 96.10% recall, and 97.37% F1-score for transient stability classification, with CCT MAE of 55.72 ms and RMSE of 140.48 ms. Compared with the discrete-time physics-informed deep learning (PIDL) baseline, PI-NODE yields higher precision (+1.27 percentage points) at the cost of lower recall (−1.30 percentage points), while CCT regression error is substantially larger, attributable to insufficient trajectory-fitting convergence under the 40-epoch Adam training configuration. Inference latency of 2.78 ms per sample (CPU-only) represents a 3.2× speedup over direct RK4 numerical simulation (8.98 ms per sample). Robustness testing under ±20% virtual inertia and droop scaling yielded 82.22% and 92.78% accuracy respectively, revealing that the current PI-NODE training configuration does not yet achieve the parametric robustness of the PIDL baseline. These findings identify the conditions under which continuous-time ODE formulation requires additional training strategies extended optimization, trajectory regularization, and boundary-aware sampling to realize its theoretical advantage over discrete-time physics penalization for microgrid transient stability assessment.
Sosialisasi Penggunaan Bioaditif untuk Efisiensi Bahan Bakar pada Alat Mesin Pertanian di Kabupaten Takalar Azizah Fauziah Misbahuddin; Andi Nur Faisal; Andi Shridivia Nuran; Hamidah Hamris; Ramli Rasjid
Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2026): Jurnal Pengabdian Masyarakat (Abdimas)
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/abdimas.v4i1.12488

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman petani terhadap penggunaan bioaditif Econella sebagai solusi efisiensi bahan bakar pada alat dan mesin pertanian di Kabupaten Takalar. Metode yang digunakan meliputi pre-test, sosialisasi, demonstrasi penggunaan bioaditif, dan post-test untuk mengukur tingkat pemahaman peserta. Kegiatan dilaksanakan pada tanggal 16 Maret 2026 dengan melibatkan 7 orang petani. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada tingkat pemahaman peserta, ditunjukkan dengan kenaikan rata-rata skor dari 17 menjadi 40 atau meningkat sebesar 135%. Seluruh peserta mengalami peningkatan kategori pemahaman dari tidak tahu menjadi tahu dan sangat paham, serta menunjukkan minat untuk menggunakan bioaditif. Dengan demikian, kegiatan sosialisasi ini terbukti efektif dalam meningkatkan literasi teknologi petani dan berpotensi mendorong penggunaan bioaditif untuk efisiensi bahan bakar pada sektor pertanian.
Co-Authors Ali Isra Andi Shridivia Nuran azizah fauziah misbahuddin Hamidah Hamris Ramli Rasjid