Fault Location and Incipient Fault Detection in Distribution Cables

A set of fault location algorithms for underground medium voltage cables, two incipient fault detection schemes for distribution cables and a state estimation method for underground distribution networks are developed in this thesis. Two schemes are designed to detect and classify incipient faults in underground distribution cables. Based on the methodology of wavelet analysis, one scheme is to detect the fault-induced transients, and therefore identify the incipient faults. Based on the analysis of the superimposed fault current and negative sequence current in time domain, the other scheme is particularly suitable to detect the single-line-to-ground incipient faults, which are mostly occurring in underground cables. To verify the effectiveness and functionalities of the proposed detection algorithms, different fault conditions, various system configurations, real field cases and normal operating transients are examined. The simulation results have demonstrated a technical feasibility for practical implementations of both schemes. Based on the methodology of the direct circuit analysis, a set of location algorithms is proposed to locate the single phase related faults in the typical underground medium voltage cables. A large number of complex nonlinear equations are effectively solved to find the fault distance and fault resistance. The algorithms only utilize the fundamental phasors of three-phase voltages and currents recorded at single end, normally at substation. The various system and fault conditions are taken into account in the development of algorithms, such as effects of shunt capacitance, mutual effects of metallic sheaths, common sheath bonding methods and different fault scenarios. The extensive simulations have validated the accuracy and effectiveness of the proposed algorithms. In order to extend the proposed fault location algorithms to underground distribution networks, a state estimation algorithm is developed to provide the necessary information for the location algorithms. Taking account of the complexity and particularity of cable circuits, the problem of the state estimation is formulated as a nonlinear optimization problem that is solved by the sequential quadratic programming technique. The simulation studies have indicated that the proposed fault location scheme incorporating with the state estimation algorithm can achieve good performance under different load and fault conditions

Evaluation of Hosta Cultivarsfor Resistance to Petiole Rot

Petiole rot of hosta, caused by the soilborne fungi Sclerotium rolfsii and S. rolfsii var. delphinii, appeared first in the southern United States. The rapid spread of petiole rot in the Midwest U.S. during the past decade has caused increasing concern among wholesale producers, retailers, and buyers

Recent Developments in Stem Cells from Human Exfoliated Deciduous Teeth in the Treatment of Non-oral Diseases

Stem cells from human exfoliated deciduous teeth (SHEDs) are odontogenic stem cells with strong proliferation ability and multilineage differentiation potential. Due to their easy accessibility and limited ethical concerns, SHEDs have great potential in stem cell therapy. There is still a lack of literature on SHEDs in the treatment of non-oral diseases. This article reviews the latest developments in the use of SHEDs to treat diseases of the nervous, digestive, cardiovascular, urinary, immune, endocrine and respiratory systems. This review will inform the clinical application of SHEDs in non-oral diseases

SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention. Despite the tremendous advancements of deep learning in fixed-scale SAR image despeckling, these methods still struggle to deal with large-scale SAR images. To address this problem, this paper introduces a novel despeckling approach termed Region Denoising Diffusion Probabilistic Model (R-DDPM) based on generative models. R-DDPM enables versatile despeckling of SAR images across various scales, accomplished within a single training session. Moreover, The artifacts in the fused SAR images can be avoided effectively with the utilization of region-guided inverse sampling. Experiments of our proposed R-DDPM on Sentinel-1 data demonstrates superior performance to existing methods.Comment: 5 pages, 5 figure