Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Intelligent fault location for low voltage distribution networks

Paper describing intelligent fault location for low voltage distribution networks

Transfer function characterization for HFCTs used in partial discharge detection

High frequency current transformers (HFCTs) are widely employed to detect partial discharge (PD) induced currents in high voltage equipment. This paper describes measurements of the wideband transfer functions of HFCTs so that their influence on the detected pulse shape in advanced PD measurement applications can be characterized. The time-domain method based on the pulse response is a useful way to represent HFCT transfer functions as it allows numerical determination of the forward and reverse transfer functions of the sensor. However, while the method is accurate at high frequencies it can have limited resolution at low frequencies. In this paper, a composite time-domain method is presented to allow accurate characterization of the HFCT transfer functions at both low and high frequencies. The composite method was tested on two different HFCTs and the results indicate that the method can characterize their transfer functions ranging from several kHz to tens of MHz. Results are found to be in good agreement with frequency-domain measurements up to 50 MHz. Measurement procedures for using the method are summarized to facilitate further applications

Application of k-means method to pattern recognition in on-line cable partial discharge monitoring

On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information. Results are presented to show that the proposed methodology is capable of recognising patterns of PD activity in on-line monitoring applications for both single-phase and three-phase cables and is also effective technique for rejecting interference signals

Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics

10.1155/2019/2108423Complexity2019210842

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Proteins that contain a functional Z-DNA-binding domain localize to cytoplasmic stress granules

Long double-stranded RNA may undergo hyper-editing by adenosine deaminases that act on RNA (ADARs), where up to 50% of adenosine residues may be converted to inosine. However, although numerous RNAs may undergo hyper-editing, the role for inosine-containing hyper-edited double-stranded RNA in cells is poorly understood. Nevertheless, editing plays a critical role in mammalian cells, as highlighted by the analysis of ADAR-null mutants. In particular, the long form of ADAR1 (ADAR1(p150)) is essential for viability. Moreover, a number of studies have implicated ADAR1(p150) in various stress pathways. We have previously shown that ADAR1(p150) localized to cytoplasmic stress granules in HeLa cells following either oxidative or interferon-induced stress. Here, we show that the Z-DNA-binding domain (Zα(ADAR1)) exclusively found in ADAR1(p150) is required for its localization to stress granules. Moreover, we show that fusion of Zα(ADAR1) to either green fluorescent protein (GFP) or polypyrimidine binding protein 4 (PTB4) also results in their localization to stress granules. We additionally show that the Zα domain from other Z-DNA-binding proteins (ZBP1, E3L) is likewise sufficient for localization to stress granules. Finally, we show that Z-RNA or Z-DNA binding is important for stress granule localization. We have thus identified a novel role for Z-DNA-binding domains in mammalian cells

Nanoscale broadband transmission lines for spin qubit control

The intense interest in spin-based quantum information processing has caused an increasing overlap between two traditionally distinct disciplines, such as magnetic resonance and nanotechnology. In this work we discuss rigourous design guidelines to integrate microwave circuits with charge-sensitive nanostructures, and describe how to simulate such structures accurately and efficiently. We present a new design for an on-chip, broadband, nanoscale microwave line that optimizes the magnetic field driving a spin qubit, while minimizing the disturbance on a nearby charge sensor. This new structure was successfully employed in a single-spin qubit experiment, and shows that the simulations accurately predict the magnetic field values even at frequencies as high as 30 GHz.Comment: 18 pages, 8 figures, 1 table, pdflate

Acute kidney disease and renal recovery : consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of > 90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD

Supergravity loop contributions to brane world supersymmetry breaking

We compute the supergravity loop contributions to the visible sector scalar masses in the simplest 5D `brane-world' model. Supersymmetry is assumed to be broken away from the visible brane and the contributions are UV finite due to 5D locality. We perform the calculation with N = 1 supergraphs, using a formulation of 5D supergravity in terms of N = 1 superfields. We compute contributions to the 4D effective action that determine the visible scalar masses, and we find that the mass-squared terms are negative.Comment: 12 pages, LaTeX 2