A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.
The hot deformation behavior of Pt−10Ir alloy was studied under a wide range of deformation parameters.At a low deformation temperature(950−1150℃),the softening mechanism is primarily dynamic recovery.In addition,dynamic recrystallization by progressive lattice rotation near grain boundaries(DRX by LRGBs)and microshear bands assisted dynamic recrystallization(MSBs assisted DRX)coordinate the deformation.However,it is difficult for the dynamic softening to offset the stain hardening due to a limited amount of DRXed grains.At a high deformation temperature(1250−1350℃),three main DRX mechanisms associated with strain rates occur:DRX by LRGBs,DRX by a homogeneous increase in misorientation(HIM)and geometric DRX(GDRX).With increasing strain,DRX by LRGBs is enhanced gradually under high strain rates;the“pinch-off”effect is enhanced at low strain rates,which was conducive to the formation of a uniform and fine microstructure.
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
