{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:04:39Z","timestamp":1791223479639,"version":"4.1.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-72286-2","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T10:03:42Z","timestamp":1727431422000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Artificial intelligence for cybersecurity monitoring of cyber-physical power electronic converters: a DC\/DC power converter case study"],"prefix":"10.1038","volume":"14","author":[{"given":"Mohammad Reza","family":"Habibi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josep M.","family":"Guerrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan C.","family":"Vasquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"72286_CR1","first-page":"1","volume":"39","author":"O Rubasinghe","year":"2023","unstructured":"Rubasinghe, O. et al. A novel sequence to sequence data modelling based cnn-lstm algorithm for three years ahead monthly peak load forecasting. IEEE Trans. Power Syst. 39, 1\u201315 (2023).","journal-title":"IEEE Trans. Power Syst."},{"issue":"5","key":"72286_CR2","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.35833\/MPCE.2021.000276","volume":"10","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Kuenzel, S., Colombo, N. & Watkins, C. Hybrid short-term load forecasting method based on empirical wavelet transform and bidirectional long short-term memory neural networks. J. Modern Power Syst. Clean Energy 10(5), 1216\u20131228 (2022).","journal-title":"J. Modern Power Syst. Clean Energy"},{"key":"72286_CR3","doi-asserted-by":"publisher","first-page":"23272","DOI":"10.1109\/ACCESS.2022.3154362","volume":"10","author":"Y Huang","year":"2022","unstructured":"Huang, Y., Zhao, R., Zhou, Q. & Xiang, Y. Short-term load forecasting based on a hybrid neural network and phase space reconstruction. IEEE Access 10, 23272\u201323283 (2022).","journal-title":"IEEE Access"},{"key":"72286_CR4","doi-asserted-by":"publisher","first-page":"68686","DOI":"10.1109\/ACCESS.2022.3185747","volume":"10","author":"PN Thanh","year":"2022","unstructured":"Thanh, P. N., Cho, M.-Y., Chang, C.-L. & Chen, M.-J. Short-term three-phase load prediction with advanced metering infrastructure data in smart solar microgrid based convolution neural network bidirectional gated recurrent unit. IEEE Access 10, 68686\u201368699 (2022).","journal-title":"IEEE Access"},{"key":"72286_CR5","doi-asserted-by":"publisher","first-page":"84227","DOI":"10.1109\/ACCESS.2022.3197609","volume":"10","author":"A Inteha","year":"2022","unstructured":"Inteha, A., Hussain, F. & Khan, I. A. A data driven approach for day ahead short term load forecasting\u2019. IEEE Access 10, 84227\u201384243 (2022).","journal-title":"IEEE Access"},{"issue":"5","key":"72286_CR6","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TNNLS.2021.3106968","volume":"34","author":"J Li","year":"2023","unstructured":"Li, J., Wei, S. & Dai, W. Combination of manifold learning and deep learning algorithms for mid-term electrical load forecasting. IEEE Trans. Neural Netw. Learn. Syst. 34(5), 2584\u20132593 (2023).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7","key":"72286_CR7","first-page":"2583","volume":"68","author":"B Adineh","year":"2021","unstructured":"Adineh, B., Habibi, M. R., Akpolat, A. N. & Blaabjerg, F. Sensorless voltage estimation for total harmonic distortion calculation using artificial neural networks in microgrids. IEEE Trans. Circuits Syst. II Express Briefs 68(7), 2583\u20132587 (2021).","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"3","key":"72286_CR8","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1109\/TEC.2020.3044270","volume":"36","author":"AN Akpolat","year":"2021","unstructured":"Akpolat, A. N. et al. Sensorless control of dc microgrid based on artificial intelligence. IEEE Trans. Energy Convers. 36(3), 2319\u20132329 (2021).","journal-title":"IEEE Trans. Energy Convers."},{"issue":"2","key":"72286_CR9","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1109\/TEC.2021.3118664","volume":"37","author":"AN Akpolat","year":"2022","unstructured":"Akpolat, A. N. et al. Dynamic stabilization of dc microgrids using ann-based model predictive control. IEEE Trans. Energy Conv. 37(2), 999\u20131010 (2022).","journal-title":"IEEE Trans. Energy Conv."},{"issue":"1","key":"72286_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TPEL.2022.3205437","volume":"38","author":"C Pan","year":"2023","unstructured":"Pan, C., Peng, Z., Yang, S., Wen, G. & Huang, T. Adaptive neural network-based prescribed-time observer for battery state-of-charge estimation. IEEE Trans. Power Electron. 38(1), 165\u2013176 (2023).","journal-title":"IEEE Trans. Power Electron."},{"issue":"3","key":"72286_CR11","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1109\/TTE.2020.3041604","volume":"7","author":"C Bian","year":"2021","unstructured":"Bian, C., Yang, S. & Miao, Q. Cross-domain state-of-charge estimation of li-ion batteries based on deep transfer neural network with multiscale distribution adaptation. IEEE Trans. Transportation Electrif. 7(3), 1260\u20131270 (2021).","journal-title":"IEEE Trans. Transportation Electrif."},{"issue":"4","key":"72286_CR12","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/TVT.2015.2427659","volume":"65","author":"X Chen","year":"2016","unstructured":"Chen, X. et al. Robust adaptive sliding-mode observer using rbf neural network for lithium-ion battery state of charge estimation in electric vehicles. IEEE Trans. Veh. Technol. 65(4), 1936\u20131947 (2016).","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"5","key":"72286_CR13","doi-asserted-by":"publisher","first-page":"5565","DOI":"10.1109\/TIA.2020.3004294","volume":"56","author":"DNT How","year":"2020","unstructured":"How, D. N. T. et al. State-of-charge estimation of li-ion battery in electric vehicles: A deep neural network approach. IEEE Trans. Ind. Appl. 56(5), 5565\u20135574 (2020).","journal-title":"IEEE Trans. Ind. Appl."},{"issue":"7","key":"72286_CR14","doi-asserted-by":"publisher","first-page":"7349","DOI":"10.1109\/TPEL.2020.3041876","volume":"36","author":"MA Hannan","year":"2021","unstructured":"Hannan, M. A. et al. Soc estimation of li-ion batteries with learning rate-optimized deep fully convolutional network. IEEE Trans. Power Electron. 36(7), 7349\u20137353 (2021).","journal-title":"IEEE Trans. Power Electron."},{"issue":"8","key":"72286_CR15","doi-asserted-by":"publisher","first-page":"6730","DOI":"10.1109\/TIE.2017.2787586","volume":"65","author":"E Chemali","year":"2018","unstructured":"Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R. & Emadi, A. Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Trans. Industr. Electron. 65(8), 6730\u20136739 (2018).","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"8","key":"72286_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TASC.2021.3091116","volume":"31","author":"MA Hossain","year":"2021","unstructured":"Hossain, M. A., Chakrabortty, R. K., Elsawah, S., Gray, E. M. & Ryan, M. J. Predicting wind power generation using hybrid deep learning with optimization. IEEE Trans. Appl. Supercond. 31(8), 1\u20135 (2021).","journal-title":"IEEE Trans. Appl. Supercond."},{"key":"72286_CR17","doi-asserted-by":"publisher","first-page":"165279","DOI":"10.1109\/ACCESS.2019.2952555","volume":"7","author":"B Zhou","year":"2019","unstructured":"Zhou, B., Ma, X., Luo, Y. & Yang, D. Wind power prediction based on lstm networks and nonparametric kernel density estimation. IEEE Access 7, 165279\u2013165292 (2019).","journal-title":"IEEE Access"},{"issue":"4","key":"72286_CR18","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TPWRS.2012.2187803","volume":"27","author":"G Sideratos","year":"2012","unstructured":"Sideratos, G. & Hatziargyriou, N. D. Probabilistic wind power forecasting using radial basis function neural networks. IEEE Trans. Power Syst. 27(4), 1788\u20131796 (2012).","journal-title":"IEEE Trans. Power Syst."},{"issue":"2","key":"72286_CR19","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1109\/TII.2022.3176821","volume":"19","author":"Y Song","year":"2023","unstructured":"Song, Y., Tang, D., Yu, J., Yu, Z. & Li, X. Short-term forecasting based on graph convolution networks and multiresolution convolution neural networks for wind power. IEEE Trans. Industr. Inf. 19(2), 1691\u20131702 (2023).","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"2","key":"72286_CR20","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/TSTE.2020.3043884","volume":"12","author":"M-S Ko","year":"2021","unstructured":"Ko, M.-S. et al. Deep concatenated residual network with bidirectional lstm for one-hour-ahead wind power forecasting. IEEE Trans. Sustain. Energy 12(2), 1321\u20131335 (2021).","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"72286_CR21","doi-asserted-by":"publisher","first-page":"131920","DOI":"10.1109\/ACCESS.2022.3229698","volume":"10","author":"A Hussain","year":"2022","unstructured":"Hussain, A., Kim, C.-H. & Jabbar, M. S. An intelligent deep convolutional neural networks-based islanding detection for multi-dg systems. IEEE Access 10, 131920\u2013131931 (2022).","journal-title":"IEEE Access"},{"key":"72286_CR22","doi-asserted-by":"publisher","first-page":"106471","DOI":"10.1109\/ACCESS.2020.3000872","volume":"8","author":"AA Abdelsalam","year":"2020","unstructured":"Abdelsalam, A. A., Salem, A. A., Oda, E. S. & Eldesouky, A. A. Islanding detection of microgrid incorporating inverter based dgs using long short-term memory network. IEEE Access 8, 106471\u2013106486 (2020).","journal-title":"IEEE Access"},{"issue":"7","key":"72286_CR23","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.3390\/electronics12071685","volume":"12","author":"MR Habibi","year":"2023","unstructured":"Habibi, M. R., Golestan, S., Guerrero, J. M. & Vasquez, J. C. Deep learning for forecasting-based applications in cyber-physical microgrids: Recent advances and future directions\u2019. Electronics 12(7), 1685 (2023).","journal-title":"Electronics"},{"issue":"2","key":"72286_CR24","first-page":"209","volume":"7","author":"M Khodayar","year":"2021","unstructured":"Khodayar, M., Liu, G., Wang, J. & Khodayar, M. E. Deep learning in power systems research: A review. CSEE J. Power Energy Syst. 7(2), 209\u2013220 (2021).","journal-title":"CSEE J. Power Energy Syst."},{"issue":"2","key":"72286_CR25","first-page":"717","volume":"68","author":"MR Habibi","year":"2021","unstructured":"Habibi, M. R., Baghaee, H. R., Dragi\u010devi\u0107, T. & Blaabjerg, F. False data injection cyber-attacks mitigation in parallel dc\/dc converters based on artificial neural networks. IEEE Trans. Circuits Syst. II Express Briefs 68(2), 717\u2013721 (2021).","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"1","key":"72286_CR26","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1109\/JSYST.2021.3086145","volume":"16","author":"MR Habibi","year":"2022","unstructured":"Habibi, M. R., Baghaee, H. R., Blaabjerg, F. & Dragi\u010devi\u0107, T. Secure mpc\/ann-based false data injection cyber-attack detection and mitigation in dc microgrids. IEEE Syst. J. 16(1), 1487\u20131498 (2022).","journal-title":"IEEE Syst. J."},{"issue":"2","key":"72286_CR27","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.1109\/JSYST.2021.3119355","volume":"16","author":"MR Habibi","year":"2022","unstructured":"Habibi, M. R., Baghaee, H. R., Blaabjerg, F. & Dragi\u010devi\u0107, T. Secure control of dc microgrids for instant detection and mitigation of cyber-attacks based on artificial intelligence. IEEE Syst. J. 16(2), 2580\u20132591 (2022).","journal-title":"IEEE Syst. J."},{"issue":"4","key":"72286_CR28","doi-asserted-by":"publisher","first-page":"4629","DOI":"10.1109\/JESTPE.2021.3050851","volume":"9","author":"MR Habibi","year":"2021","unstructured":"Habibi, M. R., Sahoo, S., Rivera, S., Dragi\u010devi\u0107, T. & Blaabjerg, F. Decentralized coordinated cyberattack detection and mitigation strategy in dc microgrids based on artificial neural networks. IEEE J. Emerg. Select. Topics Power Electron. 9(4), 4629\u20134638 (2021).","journal-title":"IEEE J. Emerg. Select. Topics Power Electron."},{"key":"72286_CR29","doi-asserted-by":"crossref","unstructured":"Habibi, M.\u00a0R., Dragicevic, T., & Blaabjerg, F. Secure control of dc microgrids under cyber-attacks based on recurrent neural networks. In:  2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp.\u00a0517\u2013521, (2020).","DOI":"10.1109\/PEDG48541.2020.9244459"},{"issue":"5","key":"72286_CR30","doi-asserted-by":"publisher","first-page":"5294","DOI":"10.1109\/JESTPE.2020.2968243","volume":"9","author":"MR Habibi","year":"2021","unstructured":"Habibi, M. R., Baghaee, H. R., Dragi\u010devi\u0107, T. & Blaabjerg, F. Detection of false data injection cyber-attacks in dc microgrids based on recurrent neural networks. IEEE J. Emerg. Select. Topics Power Electron. 9(5), 5294\u20135310 (2021).","journal-title":"IEEE J. Emerg. Select. Topics Power Electron."},{"key":"72286_CR31","doi-asserted-by":"publisher","first-page":"10842","DOI":"10.1109\/ACCESS.2023.3239684","volume":"11","author":"M Rajaee","year":"2023","unstructured":"Rajaee, M. & Mazlumi, K. Multi-agent distributed deep learning algorithm to detect cyber-attacks in distance relays. IEEE Access 11, 10842\u201310849 (2023).","journal-title":"IEEE Access"},{"key":"72286_CR32","doi-asserted-by":"publisher","first-page":"18660","DOI":"10.1109\/ACCESS.2023.3247193","volume":"11","author":"SY Diaba","year":"2023","unstructured":"Diaba, S. Y., Shafie-Khah, M. & Elmusrati, M. Cyber security in power systems using meta-heuristic and deep learning algorithms. IEEE Access 11, 18660\u201318672 (2023).","journal-title":"IEEE Access"},{"issue":"3","key":"72286_CR33","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1109\/TPEL.2020.3017935","volume":"36","author":"F Li","year":"2021","unstructured":"Li, F. et al. Detection and diagnosis of data integrity attacks in solar farms based on multilayer long short-term memory network. IEEE Trans. Power Electron. 36(3), 2495\u20132498 (2021).","journal-title":"IEEE Trans. Power Electron."},{"issue":"3","key":"72286_CR34","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/JESTPE.2023.3242299","volume":"11","author":"VSP Machina","year":"2023","unstructured":"Machina, V. S. P., Koduru, S. S., Madichetty, S. & Mishra, S. A novel standalone implementation of mdnn controller for dc-dc converter resilient to sensor attacks-a design approach. IEEE J. Emerg. Select. Topics Power Electron. 11(3), 2805\u20132815 (2023).","journal-title":"IEEE J. Emerg. Select. Topics Power Electron."},{"issue":"2","key":"72286_CR35","first-page":"119","volume":"8","author":"KS Suprabhath","year":"2023","unstructured":"Suprabhath, K. S., Prasad, M. V. S., Madichetty, S. & Mishra, S. A deep learning based cyber attack detection scheme in dc microgrid systems. CPSS Trans. Power Electron. Appl. 8(2), 119\u2013127 (2023).","journal-title":"CPSS Trans. Power Electron. Appl."},{"issue":"3","key":"72286_CR36","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1109\/JSYST.2020.3012937","volume":"15","author":"R Yadav","year":"2021","unstructured":"Yadav, R. & Pradhan, A. K. Pca-lstm learning networks with markov chain models for online classification of cyber-induced outages in power system. IEEE Syst. J. 15(3), 3948\u20133957 (2021).","journal-title":"IEEE Syst. J."},{"issue":"12","key":"72286_CR37","doi-asserted-by":"publisher","first-page":"10687","DOI":"10.1109\/JIOT.2023.3240289","volume":"10","author":"H Jahangir","year":"2023","unstructured":"Jahangir, H., Lakshminarayana, S., Maple, C. & Epiphaniou, G. A deep-learning-based solution for securing the power grid against load altering threats by iot-enabled devices. IEEE Internet Things J. 10(12), 10687\u201310697 (2023).","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"72286_CR38","doi-asserted-by":"publisher","first-page":"2722","DOI":"10.1109\/TSG.2019.2960459","volume":"11","author":"F Wei","year":"2020","unstructured":"Wei, F., Wan, Z., He, H. & Lin, X. Ultrafast active response strategy against malfunction attack on fault current limiter. IEEE Trans. Smart Grid 11(3), 2722\u20132733 (2020).","journal-title":"IEEE Trans. Smart Grid"},{"issue":"4","key":"72286_CR39","doi-asserted-by":"publisher","first-page":"3428","DOI":"10.1109\/TSG.2020.2973681","volume":"11","author":"M Ismail","year":"2020","unstructured":"Ismail, M., Shaaban, M. F., Naidu, M. & Serpedin, E. Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans. Smart Grid 11(4), 3428\u20133437 (2020).","journal-title":"IEEE Trans. Smart Grid"},{"issue":"12","key":"72286_CR40","doi-asserted-by":"publisher","first-page":"10229","DOI":"10.1109\/TPEL.2018.2808246","volume":"33","author":"S Golestan","year":"2018","unstructured":"Golestan, S., Guerrero, J. M., Vasquez, J. C., Abusorrah, A. M. & Al-Turki, Y. Modeling, tuning, and performance comparison of second-order-generalized-integrator-based flls. IEEE Trans. Power Electron. 33(12), 10229\u201310239 (2018).","journal-title":"IEEE Trans. Power Electron."},{"issue":"2","key":"72286_CR41","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1109\/TPEL.2012.2204276","volume":"28","author":"S Golestan","year":"2013","unstructured":"Golestan, S., Monfared, M. & Freijedo, F. D. Design-oriented study of advanced synchronous reference frame phase-locked loops. IEEE Trans. Power Electron. 28(2), 765\u2013778 (2013).","journal-title":"IEEE Trans. Power Electron."},{"key":"72286_CR42","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.egyr.2022.11.185","volume":"9","author":"S Golestan","year":"2023","unstructured":"Golestan, S., Habibi, M., Mousavi, S. M., Guerrero, J. & Vasquez, J. Quantum computation in power systems: An overview of recent advances. Energy Rep. 9, 584\u2013596 (2023).","journal-title":"Energy Rep."},{"issue":"18","key":"72286_CR43","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.3390\/electronics11182919","volume":"11","author":"MR Habibi","year":"2022","unstructured":"Habibi, M. R., Golestan, S., Soltanmanesh, A., Guerrero, J. M. & Vasquez, J. C. Power and energy applications based on quantum computing: The possible potentials of grover\u2019s algorithm. Electronics 11(18), 2919 (2022).","journal-title":"Electronics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-72286-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-72286-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-72286-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T14:03:46Z","timestamp":1727705026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-72286-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["72286"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-72286-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"assertion":[{"value":"4 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22072"}}