Automatic bird identification for offshore wind farms: A case study for deep learning

Niemi, Juha; Tanttu, Juha T.

doi:10.23919/elmar.2017.8124482

Cited by 6 publications

(2 citation statements)

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“…The number of images in the original data set have been increased since our first publication resulting in the better state-of-the-art performance of 0.9463 compared to the first result of 0.9100. It is noteworthy that this better result is achieved despite of the increased number of the classes, i.e., 8 compared to 6 [37].…”

Section: Discussionmentioning

confidence: 91%

Deep Learning Case Study for Automatic Bird Identification

Niemi

Tanttu

2018

Applied Sciences

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An automatic bird identification system is required for offshore wind farms in Finland. Indubitably, a radar is the obvious choice to detect flying birds, but external information is required for actual identification. We applied visual camera images as external data. The proposed system for automatic bird identification consists of a radar, a motorized video head and a single-lens reflex camera with a telephoto lens. A convolutional neural network trained with a deep learning algorithm is applied to the image classification. We also propose a data augmentation method in which images are rotated and converted in accordance with the desired color temperatures. The final identification is based on a fusion of parameters provided by the radar and the predictions of the image classifier. The sensitivity of this proposed system, on a dataset containing 9312 manually taken original images resulting in 2.44 × 106 augmented data set, is 0.9463 as an image classifier. The area under receiver operating characteristic curve for two key bird species is 0.9993 (the White-tailed Eagle) and 0.9496 (The Lesser Black-backed Gull), respectively. We proposed a novel system for automatic bird identification as a real world application. We demonstrated that our data augmentation method is suitable for image classification problem and it significantly increases the performance of the classifier.

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Section: Discussionmentioning

confidence: 91%

Deep Learning Case Study for Automatic Bird Identification

Niemi

Tanttu

2018

Applied Sciences

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“…to offshore wind farms, which found that the marine birds' long-time flight (whether they were breeding, migrating, wintering, or as prebreeders) were more likely to face the risk of collision. Niemi et al [33] proposed an automatic bird identification system based on a fusion of radar data and image data. The data were adopted to train the classifier based on the small convolutional neural network (CNN); the classifier could then be used to monitor the bird species' behavior in the vicinity of the wind turbines, but more untrained data should be adopted to test the trained model.…”

Section: Sea-sky Monitoringmentioning

confidence: 99%

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Kou

Zhang

et al. 2022

Sensors

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In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

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