Aniket Vatsa

About

Senior Research Fellow in the Department of Electrical Engineering at IIT (ISM) Dhanbad, Jharkhand, INDIA. My expertise lies in the utilization of advanced Artificial Intelligence techniques, AI-enabled signal processing, algorithm development, and deep learning for monitoring the condition of transformers, high-voltage power equipment and other complex dynamic systems. I have extensive experience in applying knowledge of dielectric response analysis, insulation health assessment, dielectric modeling, and PDC to monitor transformer health. My commitment is to advance the field through innovative research and contribute to the development of more efficient AI techniques for Electrical Engineering.

Employment

• Bengal College of Engineering and Technology Assistant Professor
  2017 - 2018

Education

• Indian Institute of Technology (Indian School of Mines) Ph.D. (Pursuing)
  2018 - 2023
• Indian Institute of Technology (Indian School of Mines) M.Tech
  2015 - 2017
• Maulana Abul Kalam Azad University of Technology, West Bengal B.Tech
  2010 - 2014

Projects & Funding

Projects & funding information is unavailable.

Publications (11)

• Enhanced Visual Identification of Dimensionally Expanded FDS for Transformers Insulation Aging State Assessment
  IEEE Transactions on Industry Applications 2025 DOI: 10.1109/TIA.2024.3523886
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• Enhanced Transformers Insulation Assessment of Noise-Affected Time-Domain Spectroscopy Using Hybrid EMD-en–de-LSTM
  IEEE Transactions on Instrumentation and Measurement 2025 DOI: 10.1109/TIM.2025.3544393
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• Insulation aging condition assessment of transformer in the visual domain based on SE-CNN
  Engineering Applications of Artificial Intelligence 2024 DOI: 10.1016/j.engappai.2023.107409
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• Residual LSTM-based short duration forecasting of polarization current for effective assessment of transformers insulation
  Scientific Reports 2024 DOI: 10.1038/s41598-023-50641-z
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• Enhancing Transformer Health Monitoring With AI-Driven Prognostic Diagnosis Trends: Overcoming Traditional Methodology’s Computational Limitations
  IEEE Industrial Electronics Magazine 2024 DOI: 10.1109/MIE.2023.3329277
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• Efficient Residual LSTM-based Short Duration Polarization Current Forecasting Technique of Transformer
  2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE) 2023 DOI: 10.1109/icemce57940.2023.10434272
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• Intelligent Explosivity Prediction of Underground Coal Mines using a Deep Parametrised Residual RNN
  2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) 2023 DOI: 10.1109/smartgencon56628.2022.10083556
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• Ageing Condition Assessment of Transformer Insulation in Visual Domain Using DCNN
  2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) 2023 DOI: 10.1109/pedes56012.2022.10080633
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• Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks
  Energies 2023 DOI: 10.3390/en16052382
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• Transformer Faults Detection using Inrush Transients based on Multi-class SVM
  2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) 2022 DOI: 10.1109/catcon56237.2022.10077668
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