{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:56:49Z","timestamp":1770818209129,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Government","award":["IITP-2026-RS-2022-00156334"],"award-info":[{"award-number":["IITP-2026-RS-2022-00156334"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the \u03b1\u03b2\/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB\/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4\u20130.8 p.u. On the forecasting side, a CEEMDAN-based decomposition\u2013modeling\u2013reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer\u2013LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R2 compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within \u00b13% of nominal, shortens re-synchronization from \u22480.35 s to \u22480.15 s, reduces rotor-current peaks by \u22485.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification.<\/jats:p>","DOI":"10.3390\/e28020138","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T08:14:23Z","timestamp":1769415263000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Forecast-Enabled Adaptive Crowbar Coordination for LVRT Enhancement in DFIG Wind Turbines"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5501-345X","authenticated-orcid":false,"given":"Xianlong","family":"Su","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea"},{"name":"School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hankil","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Music & Sound Technology, Korea University of Media Arts, 300 Daehak-gil, Janggun-myeon, Sejong-si 30056, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-3142","authenticated-orcid":false,"given":"Changsu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7481-0992","authenticated-orcid":false,"given":"Mingxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7607-1126","authenticated-orcid":false,"given":"Hoekyung","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122909","DOI":"10.1016\/j.apenergy.2024.122909","article-title":"A parallel differential learning ensemble framework based on enhanced feature extraction and anti-information leakage mechanism for ultra-short-term wind speed forecast","volume":"361","author":"Wang","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_2","unstructured":"Global Wind Energy Council (2025, January 21). Global Wind Report 2025. Available online: https:\/\/www.gwec.net\/reports\/globalwindreport."},{"key":"ref_3","unstructured":"National Energy Administration (2025, January 21). National Energy Administration Releases 2024 National Electric Power Industry Statistics, Available online: https:\/\/www.nea.gov.cn\/20250121\/097bfd7c1cd3498897639857d86d5dac\/c.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.egyr.2023.12.030","article-title":"Powerformer: A temporal-based transformer model for wind power forecasting","volume":"11","author":"Wang","year":"2024","journal-title":"Energy Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s42835-021-00685-w","article-title":"A new architecture topology for back-to-back grid-connected hybrid wind and PV system","volume":"16","author":"Pangedaiah","year":"2021","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112254","DOI":"10.1016\/j.enconman.2019.112254","article-title":"A new prediction method based on VMD\u2013PRBF\u2013ARMA\u2013E model considering wind speed characteristic","volume":"203","author":"Zhang","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110421","DOI":"10.1016\/j.rser.2020.110421","article-title":"A generalized dynamical model for wind speed forecasting","volume":"136","author":"Duca","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.renene.2016.09.003","article-title":"The impact of climate change on the levelised cost of wind energy","volume":"101","author":"Hdidouan","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.egyr.2021.11.160","article-title":"Impact of renewable energy penetration rate on power system transient voltage stability","volume":"8","author":"Niu","year":"2022","journal-title":"Energy Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124046","DOI":"10.1016\/j.energy.2022.124046","article-title":"Capacity configuration optimization of multi-energy system integrating wind turbine\/photovoltaic\/hydrogen\/battery","volume":"252","author":"Zhang","year":"2022","journal-title":"Energy"},{"key":"ref_11","first-page":"79","article-title":"Analysis of the present situation of wind turbine technology and forecast of future development trend","volume":"54","author":"Yang","year":"2020","journal-title":"Power Electron. Technol."},{"key":"ref_12","unstructured":"Chen, Y. (2008). Research on Full-Scale Grid-Connected Power Conversion Technology for Direct-Driven Wind Generation Systems. [Master\u2019s Thesis, Beijing Jiaotong University]. (In Chinese)."},{"key":"ref_13","first-page":"1","article-title":"Several hot-spot issues associated with the grid-connected operations of wind-turbine-driven doubly fed induction generators","volume":"32","author":"He","year":"2012","journal-title":"Proc. CSEE"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13023","DOI":"10.1016\/j.egyr.2022.09.087","article-title":"Optimal scheduling for unit commitment with electric vehicles and uncertainty of renewable energy sources","volume":"8","author":"Pan","year":"2022","journal-title":"Energy Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.ifacol.2018.11.738","article-title":"Wind power forecasting","volume":"51","author":"Chen","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41601-022-00236-z","article-title":"Internal electrical fault detection techniques in DFIG-based wind turbines: A review","volume":"7","author":"Bebars","year":"2022","journal-title":"Prot. Control. Mod. Power Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17534","DOI":"10.1038\/s41598-023-44332-y","article-title":"Low-voltage ride-through capability in a DFIG using FO-PID and RCO techniques under symmetrical and asymmetrical faults","volume":"13","author":"Sabzevari","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2476","DOI":"10.1038\/s41598-023-29278-5","article-title":"Effect of DFIG control parameters on small signal stability in power systems","volume":"13","author":"Qi","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_19","first-page":"449","article-title":"Recent trends of control strategies for doubly fed induction generator based wind turbine systems: A comparative review","volume":"28","author":"Karad","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TEC.2021.3126855","article-title":"Transient stability analysis and improved control strategy for DC-link voltage of DFIG-based WT during LVRT","volume":"37","author":"Chen","year":"2021","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.4314\/njtd.v18i1.6","article-title":"Techniques for ensuring fault ride-through capability of grid connected DFIG-based wind turbine systems: A review","volume":"18","author":"Shuaibu","year":"2021","journal-title":"Niger. J. Technol. Dev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tripathi, P.M., Mishra, A., and Chatterjee, K. (2024). Fault ride through\/low voltage ride through capability of doubly fed induction generator\u2013based wind energy conversion system: A comprehensive review. Modeling and Control Dynamics in Microgrid Systems with Renewable Energy Resources, Academic Press.","DOI":"10.1016\/B978-0-323-90989-1.00008-7"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1049\/rpg2.12047","article-title":"Low voltage and high voltage ride-through technologies for doubly fed induction generator system: Comprehensive review and future trends","volume":"15","author":"Din","year":"2021","journal-title":"IET Renew. Power Gener."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ding, Y. (2019). Data Science for Wind Energy, Chapman and Hall\/CRC.","DOI":"10.1201\/9780429490972"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.energy.2018.09.118","article-title":"Deep belief network based k-means cluster approach for short-term wind power forecasting","volume":"165","author":"Wang","year":"2018","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jweia.2012.09.004","article-title":"Wind power prediction based on numerical and statistical models","volume":"112","author":"Stathopoulos","year":"2013","journal-title":"J. Wind. Eng. Ind. Aerodyn."},{"key":"ref_27","first-page":"59","article-title":"Spatial model for short term wind power prediction considering wake effects","volume":"40","author":"Zeng","year":"2012","journal-title":"Power Syst. Prot. Control."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apenergy.2012.03.054","article-title":"Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output","volume":"99","author":"Cassola","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.renene.2021.01.003","article-title":"Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting","volume":"169","author":"Zhang","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107011","DOI":"10.1016\/j.epsr.2020.107011","article-title":"Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function","volume":"190","author":"Liu","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rser.2014.03.033","article-title":"A review of combined approaches for prediction of short-term wind speed and power","volume":"34","author":"Tascikaraoglu","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105976","DOI":"10.1016\/j.asoc.2019.105976","article-title":"Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms","volume":"87","author":"Dufek","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118371","DOI":"10.1016\/j.energy.2020.118371","article-title":"Short-term wind power forecasting approach based on Seq2Seq model using NWP data","volume":"213","author":"Zhang","year":"2020","journal-title":"Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s44196-023-00371-x","article-title":"Developing a novel long short-term memory networks with seasonal wavelet transform for long-term wind power output forecasting","volume":"16","author":"Chen","year":"2023","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129714","DOI":"10.1016\/j.energy.2023.129714","article-title":"The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism","volume":"288","author":"Cui","year":"2024","journal-title":"Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"119759","DOI":"10.1016\/j.energy.2021.119759","article-title":"A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables","volume":"221","author":"Meka","year":"2021","journal-title":"Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100199","DOI":"10.1016\/j.egyai.2022.100199","article-title":"Wind power forecasting based on new hybrid model with TCN residual modification","volume":"10","author":"Zhu","year":"2022","journal-title":"Energy AI"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.egyr.2022.09.171","article-title":"Prediction of ultra-short-term wind power based on CEEMDAN\u2013LSTM\u2013TCN","volume":"8","author":"Hu","year":"2022","journal-title":"Energy Rep."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, Z., Peng, X., Wei, P., Xiong, Y., Xu, X., and Song, J. Short-term wind power prediction based on CEEMDAN and parallel CNN\u2013LSTM. Proceedings of the 2022 IEEE\/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Shanghai, China, 8\u201311 July 2022, IEEE.","DOI":"10.1109\/ICPSAsia55496.2022.9949917"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wang, D., Zhou, G., Wang, J., Zhao, S., and Ni, Y. (2023). An adaptive hybrid model for wind power prediction based on the IVMD\u2013FE\u2013Ad\u2013Informer. Entropy, 25.","DOI":"10.3390\/e25040647"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Loulijat, A., Chojaa, H., El marghichi, M., Ettalabi, N., Hilali, A., Mouradi, A., Abdelaziz, A.Y., Elbarbary, Z.M.S., and Mossa, M.A. (2023). Enhancement of LVRT Ability of DFIG Wind Turbine by an Improved Protection Scheme with a Modified Advanced Nonlinear Control Loop. Processes, 11.","DOI":"10.3390\/pr11051417"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012034","DOI":"10.1088\/1757-899X\/1101\/1\/012034","article-title":"Wind turbine modelling and simulation using Matlab\/SIMULINK","volume":"1101","author":"Chong","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/2\/138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T11:18:48Z","timestamp":1770808728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/2\/138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,25]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["e28020138"],"URL":"https:\/\/doi.org\/10.3390\/e28020138","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,25]]}}}