[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124766-en":3,"doc-seo-124766-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124766,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Active trailing edge flap system fault detection via machine learning - Research","Active trailing edge flap (AFlap) systems reduce wind turbine loads, yet their adoption depends on robust fault detection and health monitoring to prevent safety or performance risks under degradation. This study proposes two machine-learning diagnosis approaches using only sensors commonly available on commercial wind turbines. A manual feature engineering plus random forest method reliably classifies investigated AFlap health-state combinations, including pre-startup asymmetrical faults. A second approach based on random convolutional kernels identifies selected health states during normal power production.","Wind Energ. Sci., 9, 181–201, 2024  \n[https://doi.org/10.5194/wes-9-181-2024](https://doi.org/10.5194/wes-9-181-2024)[ ](https://doi.org/10.5194/wes-9-181-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nActive trailing edge flap system fault detection via machine learning  \nAndrea Gamberini 1,2 and Imad Abdallah3  \n1 Siemens Gamesa Renewable Energy A/S, Brande, Denmark  \n2Department of Wind and Energy Systems, DTU, Roskilde, Denmark  \n3 Chair of Structural Mechanics and Monitoring, ETH Zurich, Zurich, Switzerland Correspondence: Andrea Gamberini ([andgam@dtu.dk](andgam@dtu.dk))  \nReceived: 21 February 2023 – Discussion started: 11 May 2023  \nRevised: 20 October 2023 – Accepted: 17 November 2023 – Published: 22 January 2024  \nAbstract. Active trailing edge flap (AFlap) systems have shown promising results in reducing wind turbine (WT) loads. The design of WTs relying on AFlap load reduction requires implementing systems to detect, monitor, and quantify any potential fault or performance degradation of the flap system to avoid jeopardizing the wind turbine's safety and performance. Currently, flap fault detection or monitoring systems are yet to be developed. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach combines manual feature engineering with a random forest classifier. The second approach relies on random convolutional kernels to create the feature vectors. The study shows that the first method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the second method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production. These results contribute to developing the systems for detecting and monitoring active flap faults, which are paramount for the safe and reliable integration of active flap technology in future wind turbine design.  \n1 Introduction  \nThe pursuit of lower levelized cost of energy has driven a steady increase in the size of utility-scale wind turbines (WTs) over the past years, with a consequent increase in the load carried by the WT components. Among the new technologies studied to mitigate this load increase, actively controlled flaps located at the blade trailing edge (AFlap) have shown promising results in reducing fatigue and ultimate loads and increasing annual energy production, see Barlas et al. (2016) and Pettas et al. (2016) . Despite the potential benefits of AFlaps, this technology has yet to reach a sufficient level of maturity for its implementation in commercial WTs. To the authors' knowledge, only Siemens Gamesa Renewable Energy (SGRE) has publicly shared data of an AFlap system implemented on two different multi-megawatt  \nWTs: a 4.0 MW WT prototype and a 4.3 MW WT prototype, both installed in Høvsøre (Denmark); see Gomez Gonzalez et al. (2022) .  \nEvery time a new component is included in a wind turbine's design, the safe and reliable continuous wind turbine operation must be ensured for the whole turbine's lifetime. To fulfill this requirement, additional components, systems, and controller strategies are needed to identify, quantify, and resolve any potential issue deriving from the fault of the new component without compromising the WT safety. Once the active flap reaches an adequate level of maturity, the wind turbine design will rely on the load reduction provided by the active flap. Therefore, any potential fault or performance degradation of the flap system may jeopardize the safety and performance of the wind turbine if not adequately managed. Therefore, a system will be needed to identify, ","cbCaitDhYRdXsvLO","https://ap.wps.com/l/cbCaitDhYRdXsvLO","pdf",3598405,1,21,"English","en",105,"# Introduction\n## Motivation for active trailing edge flaps\n## Need for fault detection and monitoring\n## Sensor-based monitoring approach\n## Model-based residual-signal approach","[{\"question\":\"Why is fault detection for active trailing edge flap (AFlap) systems necessary?\",\"answer\":\"Any fault or performance degradation of the AFlap system may jeopardize wind turbine safety and performance, especially once load reduction relies on the flap technology.\"},{\"question\":\"What data sources do the proposed machine-learning methods use?\",\"answer\":\"Both approaches rely only on sensors commonly available on commercial wind turbines, avoiding the need for additional measurement systems.\"},{\"question\":\"How does the manual feature engineering approach perform for asymmetrical flap faults?\",\"answer\":\"It reliably classifies all investigated combinations of AFlap health states for asymmetrical flap faults, both during normal power production and before startup.\"}]","Active trailing edge flap system fault detection via machine learning - 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