{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:53:33Z","timestamp":1771307613054,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guoneng Nanjing Electric Power Test &amp; Research Limited","award":["DY2024Y02"],"award-info":[{"award-number":["DY2024Y02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This study introduces an Enhanced Error-Adaptive Extended-State Kalman Filter Model Predictive Control (EEA-ESKF-MPC) method to tackle strong coupling and inertia in supercritical power plants. By enhancing the ESKF-MPC framework with a mechanism that dynamically adjusts error weights based on real-time deviations and employs exponential smoothing, alongside a BP neural network for thermal unit simulation, the approach achieves superior performance. Simulations show reductions in the Integrated Absolute Error (IAE) for load and temperature by 3.05% and 2.46%, respectively, with a modest 0.43% pressure IAE increase compared to ESKF-MPC. Command disturbance tests and real condition tracking experiments, utilizing data from a 350 MW supercritical unit, reinforce the method\u2019s effectiveness, highlighting its exceptional dynamic performance and precise tracking of operational parameter changes under multivariable coupling conditions, offering a scalable solution for modern power systems.<\/jats:p>","DOI":"10.3390\/a18070387","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T11:15:23Z","timestamp":1750936523000},"page":"387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Enhanced Error-Adaptive Extended-State Kalman Filter Model Predictive Controller for Supercritical Power Plants"],"prefix":"10.3390","volume":"18","author":[{"given":"Gang","family":"Chen","sequence":"first","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Shan","family":"Hua","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Changhao","family":"Fan","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"},{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}]},{"given":"Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Shuchong","family":"Wang","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8960-8773","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.conengprac.2016.06.013","article-title":"Control-oriented modeling and analysis of direct energy balance in coal-fired boiler-turbine unit","volume":"55","author":"Sun","year":"2016","journal-title":"Control Eng. 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