Partial discharge pulse propagation in power cable and partial discharge monitoring system

Partial discharge (PD) based condition monitoring has been widely applied to power cables. However, difficulties in interpretation of measurement results (location and criticality) remain to be tackled. This paper aims to develop further knowledge in PD signal propagation in power cables and attenuation by the PD monitoring system devices to address the localization and criticality issues. As on-line or in-service PD monitoring sensors commonly comprise of a high frequency current transformer (HFCT) and a high-pass filter, the characteristics of detected PD pulses depend on the attenuation of the cable, the HFCT used and the filter applied. Simulation of pulse propagation in a cable and PD monitoring system are performed, based on analyses in the frequency domain using the concept of transfer functions. Results have been verified by laboratory experiments and using on-site PD measurements. The knowledge gained from the research on the change in pulse characteristics propagating in a cable and through a PD detection system can be very useful to PD denoising and for development of a PD localization technique

Analysis of significant factors on cable failure using the Cox proportional hazard model

This paper proposes the use of the Cox proportional hazard model (Cox PHM), a statistical model, for the analysis of early-failure data associated with power cables. The Cox PHM analyses simultaneously a set of covariates and identifies those which have significant effects on the cable failures. In order to demonstrate the appropriateness of the model, relevant historical failure data related to medium voltage (MV, rated at 10 kV) distribution cables and High Voltage (HV, 110 kV and 220 kV) transmission cables have been collected from a regional electricity company in China. Results prove that the model is more robust than the Weibull distribution, in that failure data does not have to be homogeneous. Results also demonstrate that the method can single out a case of poor manufacturing quality with a particular cable joint provider by using a statistical hypothesis test. The proposed approach can potentially help to resolve any legal dispute that may arise between a manufacturer and a network operator, in addition to providing guidance for improving future practice in cable procurement, design, installations and maintenance

Mechanical Properties of Steel-FRP Composite Bars under Tensile and Compressive Loading

The factory-produced steel-fiber reinforced polymer composite bar (SFCB) is a new kind of reinforcement for concrete structures. The manufacturing technology of SFCB is presented based on a large number of handmade specimens. The calculated stress-strain curves of ordinary steel bar and SFCB under repeated tensile loading agree well with the corresponding experimental results. The energy-dissipation capacity and residual strain of both steel bar and SFCB were analyzed. Based on the good simulation results of ordinary steel bar and FRP bar under compressive loading, the compressive behavior of SFCB under monotonic loading was studied using the principle of equivalent flexural rigidity. There are three failure modes of SFCB under compressive loading: elastic buckling, postyield buckling, and no buckling (ultimate compressive strength is reached). The increase in the postyield stiffness of SFCB rsf can delay the postyield buckling of SFCB with a large length-to-diameter ratio, and an empirical equation for the relationship between the postbuckling stress and rsf is suggested, which can be used for the design of concrete structures reinforced by SFCB to consider the effect of reinforcement buckling

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Compressive Behavior of Sustainable Steel-FRP Composite Bars with Different Slenderness Ratios

This paper presents experimental studies on the compressive behavior of a sustainable steel-fiber reinforced composite bar (SFCB) under uniaxial compressive loading. The SFCB, combined with steel and fiber reinforced polymer (FRP), is expected to significantly enhance structural safety and sustainability. A new test method with LVDT and extensometer sensors was developed and verified through experiments to test the tensile and compressive behavior of the SFCB. Fifty-four specimens including SFCB and inner steel bar (ISB) with different slenderness ratios were tested. The test results indicated that the initial compressive elastic modulus of the SFCB was essentially the same as its initial tensile elastic modulus. The compressive yield load of the SFCB was essentially irrelevant to the slenderness ratio, and the ultimate compressive stress of the SFCBs varied inversely with the slenderness ratios. The squash load of the SFCB tended to be conservative for predicting the compressive yield load of the SFCB, while the equivalent critical global buckling load of the SFCB was much higher than its corresponding compressive yield load and ultimate load due to the inelastic buckling mechanism of the SFCB within the range of the equivalent slenderness ratios studied in this paper

RoiSeg: An Effective Moving Object Segmentation Approach Based on Region-of-Interest with Unsupervised Learning

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes