A Novel Fire Detection and Suppression System for the Surveillance of a Wind Turbine Nacelle

Abstract

This paper proposes a novel fire detection and suppression system (FDSS) designed to detect and extinguish fires in the nacelle of a wind turbine. The FDSS incorporates three sensors: an infrared camera, an optical camera, and a 3D LiDAR, as well as a fire suppression system mounted on a pan and tilt control system. The FDSS features three key characteristics. First, an ensemble learning network simultaneously classifies and detects fire/smoke regions by integrating a classification neural network, an object detection neural network, and a cumulative alarm. This novel architecture significantly improves fire detection accuracy and reduces false alarm rates. Second, multimodal information precisely localizes overheat and fire/smoke regions, enabling the FDSS to automatically aim and extinguish fires by controlling the pan and tilt system. Third, a graph-based neural network accurately classifies the affected components in the nacelle using point cloud data from the 3D LiDAR. This novel neural network for object classification provides sufficient information for the location of a fire accident. Field and virtual experiments conducted in a fire test room and virtual nacelle environments demonstrate the FDSS's effectiveness. Quantitative comparisons of three deep learning networks further highlight that these neural networks outperform other state-of-the-art deep learning models. Consequently, the FDSS provides a cost-effective and autonomous surveillance solution, enhancing the safe operation of wind turbines with advanced technologies in the fourth industrial revolution.