Smart Grid Monitoring: Non-Contact Phase Current Sensing with GMR Sensors

MD RIFAT AL AMIN KHAN — Sun, 20 Sep 2026 11:52:17 +0000

Modern power distribution networks are under pressure to improve reliability, cut maintenance costs, and streamline operational safety. Conventional methods for measuring overhead line currents rely heavily on contact-based sensors—such as Current Transformers (CTs) or Hall-effect sensors connected directly to conductors. While functional, contact-based methods bring high installation costs, safety risks during manual setup, and susceptibility to electromagnetic interference (EMI). Traditional alternatives like Rogowski coils lack sensitivity over broad bandwidths, and options like Tunnel Magneto-Resistance (TMR) or Anisotropic Magneto-Resistance (AMR) are either prone to magnetic saturation or remain expensive. A promising non-contact solution uses Giant Magneto-Resistance (GMR) sensors positioned at a distance beneath overhead distribution lines.
Key Performance Insights
Relative Accuracy: The GMR non-contact monitoring system achieved 64.64% to 91.49% relative accuracy overall, with performance across most phases exceeding 80%. Linear Load Handling: Reconstructed current waveforms closely mirrored reference Hall-effect measurements, maintaining residual current margins within +/- 2 Amp.

Non-Linear / Harmonic Stress: Under non-linear loads (combining CFLs, microwave ovens, and air conditioners), odd-order harmonics increased distortion. This resulted in higher residual error on the neutral conductor (Phase N NMAE: 35.36%).

Error Factors: Deviations stem primarily from physical mounting tolerances, minor axis misalignments, and sensor hysteresis—areas targeted for post-assembly AC calibration improvements.

Reference:
M. M. Rahman Chy, M. Rifat Al Amin Khan, M. S. Mahamud, A. Islam Sifat and F. J. Stevens McFadden, "Real-time Load Current Monitoring of Overhead Lines Using GMR Sensors," 2026 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Chicago, IL, USA, 2026, pp. 1-5, doi: 10.1109/TD48022.2026.11562453.
keywords: {Current;Image sensors;Magnetic fields;Loading;Printing;Arrays;Conductors;Hall effect;Head;Sensitivity;GMR;Test Facility;MATLAB;Overhead Lines;Hall-effect Sensor},

Beyond Single Sensors: AI-Driven Multimodal Fusion for Smarter Grid Maintenance

MD RIFAT AL AMIN KHAN — Sun, 20 Sep 2026 11:38:36 +0000

Smart grid reliability hinges on catching equipment degradation before a catastrophic failure occurs. While traditional predictive maintenance often relies on a single data stream—such as vibration analysis for mechanical dynamics or thermal imaging for heat dissipation—single-modality systems have clear blind spots. Vibration sensors miss thermal degradation, while non-contact thermal monitoring cannot capture mechanical resonance. To overcome these limitations, a research team developed a unified deep learning framework that fuses heterogeneous vibration and thermal data using an attention-based sensor fusion architecture.
Key Architectural Highlights
Vibration Feature Extraction: A 1D Convolutional Neural Network (CNN) extracts local temporal patterns, which are then passed through a Bidirectional Long Short-Term Memory (BiLSTM) network to capture long-range dependencies.

Thermal Feature Extraction: A Temporal Convolutional Network (TCN) processes multivariate thermal and environmental time-series data to capture broad temporal contexts efficiently via dilated convolutions, followed by Global Average Pooling.

Attention-Based Multimodal Fusion: Rather than simply concatenating the inputs, an attention layer dynamically weights the vibration and thermal embeddings based on which modality provides the most critical fault signatures for a given operational state. Experimental Results & Performance Evaluated across combined industrial time-series datasets, the multimodal fusion model significantly outperformed traditional machine learning baselines and single-modality deep learning models.

Overall Classification Performance Accuracy: 96.74% (vs. 91.85% for vibration-only LSTM and 92.11% for thermal-only TCN) Precision: 95.89% Recall: 95.34% F1-Score: 95.61%

reference:
M. R. Al Amin Khan, M. O. Faruq, M. N. Suhaimee, F. Shakil, S. R. Sultana and S. Firdaus, "AI-Driven Multimodal Sensor Fusion for Predictive Maintenance of Smart Grid Equipment Using Vibration and Thermal Data," 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS), Kushtia, Bangladesh, 2025, pp. 1-6, doi: 10.1109/COMPAS67506.2025.11381618.
keywords: {Vibrations;Deep learning;Accuracy;Torque;Multimodal sensors;Fault detection;Smart grids;Convolutional neural networks;Monitoring;Predictive maintenance;Predictive maintenance;smart grid equipment;multimodal sensor fusion;vibration analysis;thermal data;deep learning;temporal convolutional networks;attention mechanism;equipment health monitoring;industrial IoT},

DEV Community: MD RIFAT AL AMIN KHAN

Smart Grid Monitoring: Non-Contact Phase Current Sensing with GMR Sensors

Beyond Single Sensors: AI-Driven Multimodal Fusion for Smarter Grid Maintenance