Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward MJ Zideh, P Chatterjee, AK Srivastava IEEE Access, 2024 | 36 | 2024 |
Two-sided tacit collusion: Another step towards the role of demand-side M Jabbari Zideh, SS Mohtavipour Energies 10 (12), 2045, 2017 | 18 | 2017 |
An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market SS Mohtavipour, MJ Zideh IEEJ Transactions on Electrical and Electronic Engineering 14 (2), 252-260, 2019 | 13 | 2019 |
Power flow analysis using deep neural networks in three-phase unbalanced smart distribution grids D Tiwari, MJ Zideh, V Talreja, V Verma, SK Solanki, J Solanki IEEE Access, 2024 | 12 | 2024 |
An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids MJ Zideh, MR Khalghani, SK Solanki Electric Power Systems Research 232, 110407, 2024 | 11 | 2024 |
Physics-informed convolutional autoencoder for cyber anomaly detection in power distribution grids MJ Zideh, SK Solanki 2024 IEEE Power & Energy Society General Meeting (PESGM), 2024 | 6 | 2024 |
Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs MJ Zideh, SK Solanki arXiv preprint arXiv:2406.02927, 2024 | 2 | 2024 |