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As wind farms expand with larger turbines and more powerful generators, conventional \u2018greedy\u2019 control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies\u2014especially those involving yaw-based wake steering\u2014remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control.<\/jats:p>","DOI":"10.3390\/en18092247","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T11:48:33Z","timestamp":1745840913000},"page":"2247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2612-0321","authenticated-orcid":false,"given":"Tiago R.","family":"Lucas Frutuoso","sequence":"first","affiliation":[{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-8880","authenticated-orcid":false,"given":"Rui","family":"Castro","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Rua Alves Redol, 9, 1000-029 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-0755","authenticated-orcid":false,"given":"Ricardo B. Santos","family":"Pereira","sequence":"additional","affiliation":[{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal"},{"name":"WavEC Offshore Renewables, Edif\u00edcio Diogo C\u00e3o, Doca de Alc\u00e2ntara Norte, 1350-352 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4271-7996","authenticated-orcid":false,"given":"Alexandra","family":"Moutinho","sequence":"additional","affiliation":[{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"ref_1","unstructured":"IEA (2022). World Energy Outlook 2022, International Energy Agency."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"032006","DOI":"10.1088\/2516-1083\/ac6cc1","article-title":"Wind farm control technologies: From classical control to reinforcement learning","volume":"4","author":"Dong","year":"2022","journal-title":"Prog. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1049\/rpg2.12160","article-title":"Wind farm control\u2014Part I: A review on control system concepts and structures","volume":"15","author":"Andersson","year":"2021","journal-title":"IET Renew. 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