Detecting substation equipment in aerial infrared images is a critical task in automatic visual fault inspection for power systems. However, the uneven spatial distribution of objects, unmanned aerial vehicle (UAV) vi...
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Detecting substation equipment in aerial infrared images is a critical task in automatic visual fault inspection for power systems. However, the uneven spatial distribution of objects, unmanned aerial vehicle (UAV) viewpoint variations, object scale variations, and rare computational resources pose significant challenges for substation equipment detection. This article proposes a substation equipment detection network (SEDNet) for UAV automatic power inspection. First, a long-distance feature capture (LDFC) attention is proposed to guide the network to focus on learning features in important regions and to capture the information that it is prone to miss at the border of an image. Moreover, a lightweight, GS cross-stage partial (GSCSP) structure is proposed to improve the speed of feature processing. Second, a multilayer receptive field feature enhancement module (MRFFEM) is constructed to extract diverse, fine-grained features of objects using multiple convolutional branches. Last, to effectively detect multiscale objects, we propose a concatenation and reorganization enhanced feature pyramid module (CREFPM). The experimental results demonstrate the effectiveness of SEDNet on a multiclass, substation equipment, infrared image dataset, achieving a detection accuracy of 99.2% and a real-time detection speed of 105.3 frames/s, which outperforms state-of-the-art models in terms of mAP. SEDNet successfully meets the requirements for accurate and real-time substation equipment inspection in complex scenarios.
In addition to increasing penetration of distributed generation(DG),the distribution system power flow may be significantly impacted by direction and *** paper proposes a method for optimal placement of wind DG consid...
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In addition to increasing penetration of distributed generation(DG),the distribution system power flow may be significantly impacted by direction and *** paper proposes a method for optimal placement of wind DG considering the unbalanced operation of distribution *** objective function includes static voltage stability index,three-phase unbalance index,system reliability index,and DG investment *** untransposed distribution lines and unbalanced load are modelled,and corresponding static voltage stability index and system reliability considering DG penetrations are *** expected and stochastic daily distributed generation and demand profiles in four seasons are calculated to improve the *** solve this multi-objective optimization model,a fuzzy membership function is used to integrate the four individual objectives,and a sensitivity-based method is proposed to solve the model *** study on IEEE 13-bus distribution 3-phase networks and 123-node test feeder successfully verifies the performance of the proposed approach.
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