{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:01:37Z","timestamp":1762351297845,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.<\/jats:p>","DOI":"10.3390\/s21155173","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:44:32Z","timestamp":1627854272000},"page":"5173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3979-9166","authenticated-orcid":false,"given":"Hossein","family":"Hassani","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4330-3656","authenticated-orcid":false,"given":"Roozbeh","family":"Razavi-Far","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada"},{"name":"School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7587-4189","authenticated-orcid":false,"given":"Mehrdad","family":"Saif","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-8394","authenticated-orcid":false,"given":"Vasile","family":"Palade","sequence":"additional","affiliation":[{"name":"Center for Data Science, Coventry University, Coventry CV1 5FB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7318","DOI":"10.1109\/TII.2020.2977980","article-title":"Fault location in smart grids through multicriteria analysis of group decision support systems","volume":"16","author":"Hassani","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yu, Y., Ahmed, H.O.A., Darwish, M., and Nandi, A.K. (2020). Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods. Sensors, 20.","DOI":"10.3390\/s20164438"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1109\/JSYST.2020.3001932","article-title":"Regression Models With Graph-Regularization Learning Algorithms for Accurate Fault Location in Smart Grids","volume":"15","author":"Hassani","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Razavi-Far, R., Palade, V., and Zio, E. (2014, January 6\u201311). Optimal detection of new classes of faults by an invasive weed optimization method. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889887"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5672","DOI":"10.1049\/iet-gtd.2018.5482","article-title":"Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm","volume":"12","author":"Kari","year":"2018","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104150","DOI":"10.1016\/j.engappai.2020.104150","article-title":"Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems","volume":"100","author":"Hassani","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1109\/TPWRD.2019.2901634","article-title":"A feature selection method for high impedance fault detection","volume":"34","author":"Cui","year":"2019","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105538","DOI":"10.1016\/j.asoc.2019.105538","article-title":"A novel filter\u2013wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets","volume":"81","author":"Gangavarapu","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113691","DOI":"10.1016\/j.eswa.2020.113691","article-title":"A novel filter feature selection method using rough set for short text data","volume":"160","author":"Cekik","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114737","DOI":"10.1016\/j.eswa.2021.114737","article-title":"A novel multi-objective forest optimization algorithm for wrapper feature selection","volume":"175","author":"Ghazanfari","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107000","DOI":"10.1016\/j.cie.2020.107000","article-title":"Gaussian mixture model with feature selection: An embedded approach","volume":"152","author":"Fu","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.neucom.2018.07.034","article-title":"Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks","volume":"315","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.neucom.2021.01.076","article-title":"Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems","volume":"440","author":"Hallaji","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","article-title":"Generative adversarial networks for data augmentation in machine fault diagnosis","volume":"106","author":"Shao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107377","DOI":"10.1016\/j.measurement.2019.107377","article-title":"Machinery fault diagnosis with imbalanced data using deep generative adversarial networks","volume":"152","author":"Zhang","year":"2020","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Glowacz, A. (2021). Ventilation diagnosis of angle grinder using thermal imaging. Sensors, 21.","DOI":"10.3390\/s21082853"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, W., Chen, D., and Kong, Y. (2021). Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images. Sensors, 21.","DOI":"10.3390\/s21144774"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1109\/TSG.2021.3061395","article-title":"Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids","volume":"12","author":"Hallaji","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1214\/15-AOS1337","article-title":"Controlling the false discovery rate via knockoffs","volume":"43","author":"Barber","year":"2015","journal-title":"Ann. Stat."},{"key":"ref_20","unstructured":"Jordon, J., Yoon, J., and van der Schaar, M. (2018, January 6\u20139). KnockoffGAN: Generating knockoffs for feature selection using generative adversarial networks. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.1016\/j.neucom.2009.04.004","article-title":"Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks","volume":"72","author":"Davilu","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Roffo, G., Melzi, S., and Cristani, M. (2015, January 7\u201315). Infinite feature selection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.478"},{"key":"ref_23","unstructured":"Zaffalon, M., and Hutter, M. (2002, January 1\u20134). Robust feature selection using distributions of mutual information. Proceedings of the 18th International Conference on Uncertainty in Artificial Intelligence (UAI-2002), Edmonton, AB, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1002\/sim.7526","article-title":"Efficient l0-norm feature selection based on augmented and penalized minimization","volume":"37","author":"Li","year":"2018","journal-title":"Stat. Med."},{"key":"ref_25","unstructured":"Kira, K., and Rendell, L.A. (1992, January 12\u201316). The Feature Selection Problem: Traditional Methods and a New Algorithm. Proceedings of the AAAI-92, San Jose, CA, USA."},{"key":"ref_26","unstructured":"Gui, J., Sun, Z., Wen, Y., Tao, D., and Ye, J. (2020). A review on generative adversarial networks: Algorithms, theory, and applications. arXiv."},{"key":"ref_27","unstructured":"Rezende, D.J., Mohamed, S., and Wierstra, D. (2014, January 21\u201326). Stochastic backpropagation and approximate inference in deep generative models. Proceedings of the International Conference on Machine Learning; JMLR, Beijing, China."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_29","unstructured":"Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013). Generalized denoising auto-encoders as generative models. arXiv."},{"key":"ref_30","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016, January 5). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_31","unstructured":"Kurutach, T., Tamar, A., Yang, G., Russell, S., and Abbeel, P. (2018). Learning plannable representations with causal infogan. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Spurr, A., Aksan, E., and Hilliges, O. (2017). Guiding infogan with semi-supervision. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-319-71249-9_8"},{"key":"ref_33","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_34","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017, January 6\u201311). Conditional image synthesis with auxiliary classifier gans. Proceedings of the International Conference on Machine Learning; JMLR, Sydney, Australia."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., and Yosinski, J. (2017, January 21\u201326). Plug & play generative networks: Conditional iterative generation of images in latent space. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.374"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017, January 22\u201329). Dualgan: Unsupervised dual learning for image-to-image translation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref_38","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning; JMLR, Sydney, Australia."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wu, J., Huang, Z., Thoma, J., Acharya, D., and Van Gool, L. (2018, January 8\u201314). Wasserstein divergence for gans. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_40"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1007\/s11263-019-01265-2","article-title":"Loss-sensitive generative adversarial networks on lipschitz densities","volume":"128","author":"Qi","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","unstructured":"Wiatrak, M., Albrecht, S.V., and Nystrom, A. (2019). Stabilizing generative adversarial networks: A survey. arXiv."},{"key":"ref_42","unstructured":"Denton, E., Chintala, S., Szlam, A., and Fergus, R. (2015). Deep generative image models using a laplacian pyramid of adversarial networks. arXiv."},{"key":"ref_43","unstructured":"Shaham, T.R., Dekel, T., and Michaeli, T. (November, January 27). Singan: Learning a generative model from a single natural image. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_44","unstructured":"Shocher, A., Bagon, S., Isola, P., and Irani, M. (November, January 27). Ingan: Capturing and retargeting the \u201cdna\u201d of a natural image. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_45","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_46","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv."},{"key":"ref_47","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning; JMLR, Long Beach, CA, USA."},{"key":"ref_48","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_50","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_51","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., and Darrell, T. (2016). Adversarial feature learning. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2018, January 2\u20137). It takes (only) two: Adversarial generator-encoder networks. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11449"},{"key":"ref_53","unstructured":"Hoang, Q., Nguyen, T.D., Le, T., and Phung, D. (2017). Multi-generator generative adversarial nets. arXiv."},{"key":"ref_54","first-page":"469","article-title":"Coupled generative adversarial networks","volume":"29","author":"Liu","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_55","unstructured":"Cand\u00e8s, E.J., Fan, Y., Janson, L., and Lv, J. (2016). Panning for Gold: Model-Free Knockoffs for High-Dimensional Controlled Variable Selection, Department of Statistics, Stanford University."},{"key":"ref_56","unstructured":"Belghazi, M.I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D. (2018, January 10\u201315). Mutual information neural estimation. Proceedings of the International Conference on Machine Learning; JMLR, Stockholm, Sweden."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:37:20Z","timestamp":1760164640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,30]]},"references-count":56,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21155173"],"URL":"https:\/\/doi.org\/10.3390\/s21155173","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,7,30]]}}}