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The Power Systems AI Laboratory conducts cutting-edge research at the intersection of artificial intelligence, smart energy systems, and modern power infrastructure. Our work focuses on developing intelligent, resilient, and sustainable solutions for next-generation electric power systems through advanced modeling, data analytics, and cyber-physical integration.
The Power Systems AI Laboratory conducts cutting-edge research at the intersection of artificial intelligence, smart energy systems, and modern power infrastructure. Our work focuses on developing intelligent, resilient, and sustainable solutions for next-generation electric power systems through advanced modeling, data analytics, and cyber-physical integration.
We actively publish our research in leading peer-reviewed journals and international conferences, contributing to advancements in smart grids, renewable energy integration, digital twins, grid resilience, and AI-driven power system operation. Our publications reflect our commitment to innovation, real-world impact, and the advancement of intelligent energy technologies.
We actively publish our research in leading peer-reviewed journals and international conferences, contributing to advancements in smart grids, renewable energy integration, digital twins, grid resilience, and AI-driven power system operation. Our publications reflect our commitment to innovation, real-world impact, and the advancement of intelligent energy technologies.
Selected Journal Publications
Selected Journal Publications
Hussain, A., Yadav, A. and Ravikumar, G., 2026. "Anomaly Detection in EVCS using Federated Learning with Auto-Optimized Edge Models." IEEE Transactions on Industry Applications.
Hussain, A., Yadav, A. and Ravikumar, G., 2024. "Anomaly detection using bi-directional long short-term memory networks for cyber-physical electric vehicle charging stations." IEEE Transactions on Industrial Cyber-Physical Systems, 2, pp.508-518.
Hussain, A., Kumar, K. and Ravikumar, G., 2026. "Bayesian-optimized bidirectional long-short-term memory network for wind power forecasting with uncertainty quantification." Electric Power Systems Research, 251, p.112261.
Hussain, A., Mehdi, A. and Kim, C.H., 2024. "A communication-less islanding detection scheme for hybrid distributed generation systems using recurrent neural network." International Journal of Electrical Power & Energy Systems, 155, p.109659.
Hussain, A., Mirza, S. and Kim, C.H., 2023. "Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks." Electric Power Systems Research, 224, p.109807.
Hussain, A., Kim, C. H., & Jabbar, S. (2022). "An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems," IEEE Access, vol. 10, pp. 131920-131931.
Hussain, A., Kim, C. H., & Mehdi, A. (2021). “A Comprehensive Review of Intelligent Islanding Schemes and Feature Selection Techniques for Distributed Generation System.” IEEE Access, vol. 9, pp. 146603-146624.
Hussain, A., Kim, C. H., & Admasie, S. (2021). “An intelligent islanding detection of distribution networks with synchronous machine DG using ensemble learning and canonical methods.” IET Generation, Transmission & Distribution”, 15(23), 3242-3255.
Hussain, A., Perwez, U., Ullah, K., Kim, C. H., & Asghar, N. (2021). “Long-term scenario pathways to assess the potential of best available technologies and cost reduction of avoided carbon emissions in an existing 100% renewable regional power system: A case study of Gilgit-Baltistan (GB), Pakistan”. Energy, 221, 119855.
Mehdi, A., Hussain, A., Song, J.S. and Kim, C.H., 2024. A robust unified data-driven protection scheme for hybrid AC/DC transmission lines. Electric Power Systems Research, 233, p.110453.
Mirza. S., Hussain, A., & Kim, C. H. (2023). "Quasi-Resonant FCS Model Predictive Control for Three Phase Grid Connected Highly Intermittent PV-Inverter with Voltage Support." Electric Power Systems Research, 221, p.109413.
Mehdi, A., Kim, C. H., Hussain, A., Kim, J. S., & Hassan, S. J. U. (2021). “A comprehensive review of auto-reclosing schemes in AC, DC, and hybrid (AC/DC) transmission lines.” IEEE Access
Haider, W., Hassan, S. J. U., Mehdi, A., Hussain, A., Adjayeng, G. O. M., & Kim, C. H. (2021). “Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network.” Machines, 9(1), 20.
Baloch, S., Jamali, S. Z., Mehmood, K. K., Bukhari, S. B. A., Saeed Uz Zaman, M., Hussain, A., & Kim, C. H. (2020). “Microgrid protection strategy based on the autocorrelation of current envelopes using the squaring and Low-pass filtering method.” Energies, 13(9), 2350.
Khatibi, M., Jalilzadeh, S., Hussain, A, Haider, W. (2022). “A PSO-Based Approach for Optimal Allocation and Sizing of Resistive-Type SFCLs to Enhance the Transient Stability of Power Systems.” Electronics, 11(23):3980.
Gupta, J., Hussain, A., Singla, M.K., Nijhawan, P., Haider, W., Kotb, H., AboRas, K.M. (2023). “Parameter Estimation of Different Photovoltaic Models Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm.” Appl. Sci., 13, 249.
S. Ahmad., Hussain, A., Haider, W., S, Ali, Abbas, K. (2023). “Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV, Wind and Biomass-based Resources.” Energies, 16(4), 1667.
Dashtdar, M., Hussain, A., Hassan, Z., A., A, Masud., Haider, W., Kotb, H.; AboRas, K.M. (2023). “Fault location in distribution network by solving the optimization problem based on power system status estimation using the PMU.” Machines, 11(1), 109.
Huzaifa, M., Hussain, A., Haider, W., Kazmi, S. A. A., Ahmad, U., & Rehman, H. U. (2023). “Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation.” Sustainability, 15(9), 7499.
Rehman, H.U., Hussain, A., Haider, W., Ali, S.A., Kazmi, S.A.A., Huzaifa, M. (2023). “Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models.” Energies, 16, 2444.
Ali, T., Haider, W., Haziq, M., Khan, MO., Hussain, A. (2023). “The Development and Validation of Correlation Charts to Predict the Undisturbed Ground Temperature of Pakistan: A Step towards Potential Geothermal Energy Exploration.” Eng., 4(3):1837-1850.
Book Chapters
Book Chapters
1. Hussain A and Ravikumar G (2025) Machine Learning-Driven Stacked Ensemble Meta-Learning for Short-Term Solar Power Forecasting. Smart Grids - Innovations for a Sustainable Future [Working Title]. Intech Open.
2. Mehdi, A., Hussain, A., Shinw, G.S., Kim, H.Y., Liu, Y. and Kim, C.H., 2026. Fault detection and classification in hybrid AC/DC systems using artificial neural networks. In Advanced Analytics for Reliability and Resilience of Energy System (pp. 189-225). Elsevier.
Selected Conference and Poster Papers
Selected Conference and Poster Papers
Hussain, A., Yadav, A. and Ravikumar, G., 2025, February. Federated learning for detecting cyber attacks in EVCS using a lightweight neural network. In 2025 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE.
Yadav, A., Hussain, A. and Ravikumar, G., 2025, February. EMT-Model-Based HIL Testbed for Large Scale EV-Integrated Distribution Grids. In 2025 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE.
Hussain, A. and Ravikumar, G., 2025, January. Deep Learning-based Autoencoder for Fault Detection and Localization in DER-Microgrids. In 2025 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge) (pp. 1-5). IEEE.
Mantha, A.A., Hussain, A. and Ravikumar, G., 2024, February. HIL testbed-based auto feature extraction and data generation framework for ML/DL-based anomaly detection and classification. In 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.
Hussain, A., Ullah, K., Perwez, U., & Shahid, M. (2018, September). The long-term forecast of Gilgit Baltistan (GB)’s electricity demand. In 2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET) (pp. 1-5). IEEE.
Hussain, A., Kim, J. S., Admasie, S., & Kim, C. H. (2021). Ensemble Learning based Islanding Detection for DG using Passive Electrical Parameters. KIEE Summer Conference, 2021, 900-901.
Hussain, A., Mehdi, A., Park, W. G., & Kim, C. H. (2021). Long term wind energy estimation using adaptive boosting ensemble learning model. KIEE Summer Conference, 2021, 479-480.
Hussain, A., Mehdi, A., Baloch, S., Ashraf, H., & Kim, C. H. (2020). THDv based Islanding Detection using Decision Tree for Synchronous DG. KIEE Summer Conference, 2020, 331-332.
Mehdi, A., Hussain, A., Haider, W., Hassan, S. J., Saad, M., & Kim, C. H. (2020). Classification of Power System Faults Using Random Forests. KIEE Summer Conference, 2020, 957-958.
Baloch, S., Hussain, A., Hassan, S. J. U., Haider, W., & Kim, C. H. (2020). Fault Detection in Microgrid based on Envelope of Current Signal. KIEE Summer Conference, 2020, 923-924.
Ashraf, H. M., Saad, M., Hussain, A., ul Hassan, J., & Kim, C. H. (2020). Optimal Performance of Microgrid Using Sympathetic Immunities of IGDT: An Overview. KIEE Summer Conference, 2020, 843-844.
Saad, M., Ashraf, H. M., Hussain, A., Mehdi, A., & Kim, C. H. (2020). Unscented Kalman Filter Based Online Estimation of Grid Harmonics. KIEE Summer Conference, 2020, 839-840.
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