Recently, nanofluids are introduced in the power utilities for the dielectric performance enhancement of power transformer oil. The compactness and fault improvement of the power transformers has resulted in the necessity of next-generation insulating fluid with inflated dielectric properties. This paper investigates the dielectric performance of the novel ester oil-based magnetic nanofluids with different nanoparticles under different electric stress conditions. Two biodegradable fluids used as the base liquid for the experiment are synthetic ester oil and natural ester oil while three magnetic nanoparticles used to synthesize nanofluids are iron (II, III) oxide, cobalt (II, III) oxide, and iron phosphide. The concentrations of the nanoparticles are varied to optimize the characteristics of the prepared nanofluids. The dielectric breakdown and relative permittivity of the nanofluids have been experimentally investigated. Further, the breakdown predictions have been performed using Weibull statistical distribution-based regression analysis. The experimental results show the improvement in the characteristics of prepared nanofluids with the change in nanoparticle concentration as compared with the host fluids.

The continuous monitoring of power system devices is an important step towards keeping such capital assets safe. Partial discharge (PD) based measurement tools provide a reliable and accurate condition assessment of power system insulations. It is very common that voids or cavities exist in every solid dielectric insulation medium. In this paper, different voids are modeled and analysed using an advanced finite-element (FE)-based computational algorithm. The proposed approach can be used to measure PD in cavities and voids regardless of their shape and size. The frequentative scripting approach with finite element modeling (FEM) provides the results which satisfy all PD conditions. PD behavior has been studied and analysed through PD statistical features obtained from the simulated PD signal. A comparative study between simulated and experimental results is also performed to validate the accuracy of the proposed modeling approach. To further improve the accuracy of the model, the influence and optimization of certain free parameters required for modeling has been carried out. The paper shows how PD is greatly influenced with the shape and size of voids in dielectric insulators. The research realized in this paper further provides the PD statistical features and its variation with respect to void shape and size. These features are utilized for the assessment of the insulation quality. The variation in PD parameters with respect to voltage level is also presented and discussed.

This study deals with internal defects existing or occurs in cable insulation due to stress over its operation. The most popular tool for identifying and assessing insulation-based flaws is partial discharge (PD) analysis for every power cable and solid insulant. Characterization of defects is of utmost significance for overall degradation intensity and possible deterioration evaluation. In this article, a machine learning-based diagnostics scheme is proposed to identify and characterize PD signals formed by different internal sources in solid insulation. The internal discharge sources are differently shaped and sized voids created in polymeric insulation. A dissimilar shaped cavity produces distinct PD patterns. The PD signal is recorded and denoised using the wavelet analysis to remove unwanted consistent noise interference efficiently. The feature matrixes are formed by implementing features obtained from statistical operators and phase-resolved PD (PRPD) signal characteristics based on different sizes. The proposed scheme is efficient with forms of support vector machines (SVMs) and ensemble algorithm tools to achieve the high accuracy of defect identification and classification. The accuracy band of the proposed machine learning-based diagnosis to identify and characterize the defect scale is from 96% to 92%.