This paper presents a novel technique to diagnose frequently encountered power quality (PQ) disturbance cases both single and mixed event types. Adopting existing approach to emulate signals containing different types of PQ disturbance events, a specially designed hardware setup has been developed in the laboratory. Captured signals from the developed event generator have been analyzed to extract useful information for diagnosis of various PQ disturbances. In the proposed method, a signal tracker capable of continuously tracking normal level of the signal has been employed to extract actual disturbance within it. A suitably designed detrended fluctuation analysis tool has been effectively used to diagnose the PQ disturbances. Implementation of the proposed technique shows the effectiveness in differentiating PQ events distinctly without much involving conventional analytical tools that result in minimum computational burden as compared to the existing methods. To establish the effectiveness and to get acceptability, the proposed method has been rigorously tested on significant number of captured signals containing different types of PQ disturbances and a satisfactory outcome has been observed.

The usage of polymeric insulators in transmission and distribution overhead lines for the last few decades has increased extensively. Silicone Rubber (SiR) material is used enormously to make High Voltage (HV) polymeric insulators. The SiR Insulator provides insulation protection for overhead HV lines. The life span of the SiR insulators is shortened due to the aging effect. The progressive aging ultimately leads to the failure of the insulation system as well as the power supply. Therefore, a regular condition assessment of SiR insulators can help prevent the power system’s failures. A comprehensive study of the various types of aging processes of SiR insulators is reported in this article. Also, the comparative analysis of SiR insulators’ physical, chemical, and electrical properties under the influence of aging is reported here. The usefulness, limitation and difficulties confronted in the existing methodologies are also reported in this article. This review article would be helpful to implement in future research for applying the HV overhead line insulator.

This paper proposes an advanced technique for condition assessment of outdoor insulators based on its surface leakage current through employing Detrended Fluctuation Analysis (DFA). Analysis of surface leakage current of in-service insulator can be useful for assessing its surface condition. In the proposed technique, a tracker signal has been developed based on the fundamental component of surface leakage current to extract the distortions from it which carries significant information about contamination level. DFA has then been applied on the extracted distortions of leakage current to estimate its contamination level. In order to investigate experimentally, surface leakage current of 11 kV disc insulator (contaminated at different level) has been captured at different applied voltage level. The proposed technique has been applied on the captured leakage current to estimate contamination level. Results show that, employment of the proposed technique provides satisfactory outcomes regarding assessment of insulator condition which in turn improves the reliability of power system

Epoxy nano-composites along with inorganic nanoparticles as filler, can exhibit excellent properties, such as ameliorated mechanical strength, improved thermal conductivity, enhanced dielectric strength, and reduced dielectric dissipation factors. Therefore, epoxy-based insulation is employed in power transformers, motors, generators, converter transformers, gas-insulated switchgear, and gas-insulated transmission lines. This article aims to review the dielectric and thermal properties of epoxy nano-composites with incorporated inorganic nano-fillers. Proper dispersion of nanoparticles inside the polymer matrix can effectively improve the dielectric strength and thermal properties of epoxy insulation. Excessive dispersion of nano-fillers can cause agglomeration and defects between the fillers and matrix, resulting in insulation problems such as higher dielectric loss and reduced dielectric strength. It is considered that the dispersion of nanoparticles in proper ratio can effectively improve the dielectric properties of epoxy insulation. Therefore, an augmented perception of the nanoparticle–epoxy interfaces in nano-composites can enable a pragmatic analysis of the desired properties, opening new arrays for application.

In this article, a deep learning based automated framework has been proposed to sense minor stator winding inter-turn fault in an induction motor without having load level information. For this purpose, an experimental setup has been configured in laboratory through which three phase current signal of a 3-ph squirrel cage induction motor (IM) has been recorded at different minor stator winding inter-turn fault, different loading level and external voltage unbalance condition. In this proposed framework, three phase stator current profiles firstly are converted to 1-D Park’s vector modulus signal using Park’s transformation and Extended Park’s Vector Approach. Thereafter, Park’s vector modulus of three phase stator currents have been encoded into RGB image using recurrence plot technique and have been fed to a customized convolutional neural network module (RPCNNet) for detection of minor stator winding inter-turn fault severity. The recognition performance of the RPCNNet is also compared with four benchmark convolutional neural network (CNN) models namely “VGG16”, “AlexNet”, “ResNet50” and “DenseNet201” respectively. From the result, it is observed that RPCNNet has shown comparable performance and offers significantly reduced computational cost compared to the benchmark CNN models. Proposed framework has also outperformed the existing approaches by a significant margin and shown promising result under both noiseless and noisy environment. The proposed framework is also capable of differentiating minor stator winding inter-turn fault condition from unbalanced supply voltage condition. Hence, proposed framework is very much suitable for practical application.

In this article, an efficient technique has been proposed to estimate the contamination level of overhead polymeric insulators. Deposition of contamination on polymeric insulator surface, is a serious issue as it often results in the flashover and even insulator failure. For estimating the severity of contamination level, surface leakage current (SLC) signals of a 11kV polymeric insulator with contaminated surface has been analyzed in time-frequency domain through hyperbolic window stockwell transform (HST). HST is more flexible than classical stockwell transform. Also, HST can able to handle both the low and high frequencies adequately. Considering the advantage, HST has been used here to estimate contamination degree from SLC signature. HST analysis of SLC signal returned a 2d complex time-frequency HS matrix. The complex time-frequency HS matrix has been separated into magnitude and phase spectrum. Based on the phase and magnitude spectrum, 15 statistical features, namely HST features has been extracted. Thereafter, 5 relevant HST features have been selected through least absolute shrinkage and selection operator (LASSO) feature selection technique. Finally, these relevant HST features are fed to four machine learning classifiers for estimation of contamination degree. It has also been observed that, the proposed framework method offered better classification accuracy compared to other standard time-frequency analysis and existing methods available in literature.