Investigation of electrical properties of field grading materials based ZnO microvaristors

Field grading material based microvaristors are widely used to minimise the localised field enhancement which could trigger corona and partial discharges in high voltage equipment. In this research, two different microvaristor powders, A and B are composed of silicone rubber matrix at certain filler concentration. The fabrication process of this composite is done by mixing the insulating matrix with filler using high shear mixer, follow by degassing and curing. The additional procedure, heat treatment, is introduced to avoid the agglomeration risk. Such challenges during running this process are well discussed. The composites are subjected to three high voltage tests, alternating current (AC), direct current (DC) and impulse. The non-linearity behaviour of these composite is characterised as dependent on the microvaristor loading, material properties and voltage application. The microscopic evaluations are conducted to examine the effect of material properties toward the electrical properties of composites. An 11 kV polymeric insulator has been modelled and simulated in COMSOL® platform. The performance of this insulator is assessed under a number of simulation scenarios. The potential voltage and field profile of this insulator are identified. Such improvement of field distribution along the insulator is required, therefore the electrical properties of field grading material is adopted into numerical simulation. The introduction of microvaristor material with an appropriate switching characteristic has led to a substantial improvement in the electric field and heat distributions along the insulator profile

Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Sign language gesture recognition with bispectrum features using SVM

Wi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectram features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Voltage balancing for active power filter using dual-frequency multicarrier modulation with balanced H-bridge control

In this paper, a switching control method integrated with voltage balancing feature is presented to manage operation of single-phase 5-level cascaded H-bridge (CHB) inverter as parallel-connected active power filter (PAPF), and at the same time maintain its voltage balance. CHB inverters are always preferred for PAPF due to their dominating features of modularity, flexibility and least components count. However, inherent voltage unbalance issues of CHB inverter which is due particularly to unequal switching operation across its individual H-bridge module have always been the most troublesome when handling CHB inverter. For consistent voltage balance across each H-bridge module, proportional-integral (PI) controller for voltage balancing control and multicarrier sinusoidal pulse-width modulation (MCSPWM) for switching control have commonly been integrated together. However, these conventional approaches do not directly address the issue of unequal switching. Hence, in this work, effort is performed to allow each individual H-bridge module to operate with balance switching pattern, by evenly distributing the PWM switching signal to them. Design concept of the proposed method is modeled in MATLAB-Simulink platform. To critically assess its effectiveness, comparative analysis involving two types of highly nonlinear rectifier loads and distorted grid is performed. The presented simulation findings confirmed effectiveness of the proposed method in maintaining voltage balance of the 5-level CHB inverter when mitigating harmonics under adverse grid

Electrical characterisation of ZnO microvaristor materials and compounds

One of the most challenging design aspects of high voltage equipment is to ensure adequate electrical field distribution and controlled magnitudes within the insulation regions of the equipment to ensure integrity and long term satisfactory in-service performance. Electrode geometry can be designed to improve distribution and lower peak magnitudes of electrical fields. However, this is not sufficient in many cases. In order to influence the distribution of the field within the insulation medium and its surroundings, a number of techniques were introduced for bushing, cable terminations and electrical machines. These include semi-conducting and specialist grading materials. In recent years, the development of zinc oxide (ZnO) microvaristors has allowed a further opportunity for field control, in particular, in outdoor insulators and cable terminations applications. This paper gives an overview of the fabrication process of ZnO microvaristors loaded grading compound materials for electrical field control and details an experimental approach to characterize the material. Further analysis was carried out to obtain the electrical properties of the material, such as conductivity and permittivity as a function applied voltage. An application to outdoor insulators is proposed and modelled