2026-10-12T07:31:56Z https://www.scidb.cn/oai

10.57760/sciencedb.10185 2024-07-11T19:31:13Z

2024-07-11 Infrared Thermal Image Dataset of High Voltage Electrical Power Equipment under Different Operating Conditions doi:10.57760/sciencedb.10185 en Recognizing high voltage power equipment in electrical substations is the fundamental platform for effective condition monitoring of electrical power system. It enables proper identification and analysis of anomalies within the equipment, especially when in operation. The result such investigation can be applied for effective real-time measurement, control and protection schemes in the network. The use of visual images for this purpose would be limited during poor lighting conditions. However, Infrared (IR) images of the equipment are invariant to poor illumination condition. Hence, we have acquired the thermographic images of the high voltage power equipment using the portable professional FLIR C5 Infrared camera at different times of the day and load conditions. The dataset contains 5 categories of high voltages equipment common to most air-insulated electrical power substation at 132kV level, namely: circuit breakers, power transformers, surge arresters, disconnectors, and wave traps. The number of IR images for each class of equipment are: circuit breakers 203, power transformers 178, surge arresters 181, disconnectors 180, and wave traps 153. The IR images are 640 x 480 pixel RGB images captured using the rainbow color palette and properly segmented in labeled folders. The color bar in each IR image identifies the thermal range used during its acquisition. The dataset can be used for implementing novel research in computer vision based deep learning models, especially in object recognition, identification, fault classification or detection algorithms. The thermal profile of the equipment in the dataset could be applied for detection of hotspots and other related anomalies. infrared inspection; condition monitoring; thermography; image classification; object detection; computer vision ; fault detection; hotspot detection; deep learning; power equipment monitoring; object classification; anomaly detection Ezechukwu Kalu Ukiwe Steve A. Adeshina Jacob Tsado Bukola Babatunde Adetokun PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset Science Data Bank