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Comprehensive bird preservation at wind farms
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences. Bioseco Sp. z. o. o., POL.ORCID iD: 0000-0003-4399-5477
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences. Bioseco Sp. z. o. o., POL.
Bioseco Sp. z. o. o., POL.
Bioseco Sp. z. o. o., POL.
Show others and affiliations
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 1, p. 1-35, article id 267Article in journal (Refereed) Published
Abstract [en]

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI AG , 2021. Vol. 21, no 1, p. 1-35, article id 267
Keywords [en]
Artificial intelligence, Bird monitoring system, Distributed computing, Environmental sustainability, Monitoring of avifauna, Safety system, Stereo-vision, Vision system, Aircraft detection, Decision making, Electric utilities, Fixed wings, Stereo image processing, Stereo vision, Wind power, Wind turbines, Artificial intelligence algorithms, Component selection, Decision-making systems, Design approaches, Development process, Localization procedure, Renewable energy source, Stereo vision system, Birds, algorithm, article, avoidance behavior, bird, human, mortality, nonhuman, reliability, renewable energy, sound, vision, wind farm, wing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-20993DOI: 10.3390/s21010267ISI: 000606059700001PubMedID: 33401575Scopus ID: 2-s2.0-85099421498OAI: oai:DiVA.org:bth-20993DiVA, id: diva2:1524557
Note

open access

Available from: 2021-02-01 Created: 2021-02-01 Last updated: 2022-02-10Bibliographically approved
In thesis
1. Sensors and Algorithms in Industry 4.0: Security and Health Preservation Applications
Open this publication in new window or tab >>Sensors and Algorithms in Industry 4.0: Security and Health Preservation Applications
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Globalisation and technological digitisation have triggered an Industry 4.0. revolution.  The core of this revolution is autonomisation of complex processes, which require expert knowledge. The technical foundations of Industry 4.0 are IoT, Big Data and AI technologies. Nowadays, autonomous systems are widely used to increase human and environmental safety and to prevent health degradation.  Such non-industrial, life related applications demand high reliability as well as precision and accuracy, which challenge engineering science. 

The thesis objective is to provide suitable solutions for non-invasive, automated, and autonomous systems used for life protection and health maintenance. The proposed solutions enable non-invasive measurements by means of vision and acoustic sensors. The presented methods and systems are designed based on an analytical assessment of existing technologies and algorithms. New hardware solutions, signal and data processing methods, as well as classification and decision-making algorithms are proposed. Where required, additional customisations and modifications are applied. The systems and methods presented have been modelled and rigorously validated, and subsequently implemented and verified in a real environment.  

The scope of the thesis includes the assessment of functional requirements, precision, accuracy and reliability of life-related technological systems. It covers an analytical evaluation of proposed methods and algorithms of filtration, feature extraction, also detection, localization, identification, and classification of objects. The application fields are health monitoring, nature observation and facilitating collaborative frameworks in modern factories. 

The thesis specifically focuses on methods and algorithms of autonomous decision making concerning the risk of heart disease, the threat of fatal collision of rare birds with man-made structures and the prevention of accidents in modern robotised factories. It also deals with the implementation of the Industry 4.0 fundamentals, which are smart sensing, IoT and AI methods optimised to improve the system performance in a broad sense. The applied distributed computing method and machine-to-machine communication are aimed at limiting the data stream at an early stage of the decision-making process, and thus ensure the system’s cost-effectiveness. From the thesis, one can understand how the Industry 4.0 paradigm can contribute to autonomisation of compound processes and to increase system performance, without compromising its affordability.

The thesis is divided into two parts. The first, Prolegomena provides an overview of the sensors and algorithms applicable to industrial safety along with human health and nature preservation. This part also visualizes the relationships and interactions among the articles comprising the second part named Papers. In general, each of the enclosed six papers deals with the problem of autonomisation of complex processes in real-time and in a regular environment.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 4
Keywords
Acoustic Sensor, Artificial Intelligence, Autonomisation, Classification, Detection, Feature Extraction, Health Preservation, Identification, Internet of Things, Machine Learning, Multi-Sensor System, Safety System, Vision System
National Category
Engineering and Technology Signal Processing Computer Systems
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-21387 (URN)978-91-7295-424-3 (ISBN)
Public defence
2021-09-10, Zoom, Campus Gräsvik, Karlskrona, 13:15 (English)
Opponent
Supervisors
Available from: 2021-05-11 Created: 2021-05-10 Last updated: 2021-09-20Bibliographically approved

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Gradolewski, DawidDziak, DamianKulesza, Wlodek

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