Saibal Chatterjee

This work reports the results of experimental study of moisture migration from cellulose paper, supplied by ABB India Ltd, into six insulating oils under closely regulated test environment. mineral oil (MO), synthetic ester (MIDEL 7131), natural ester (MIDEL eN1215), cottonseed oil, peanut oil, and sesame oil were typically used for the experimentation. First, cellulose paper specimens were dried in oven to constant mass. Next, they were humidified homogeneously to 3.5%, and finally immersed in 500 g of each oil type. The moisture evolution was monitored over 168 h. The moisture in oil was recorded using a Vaisala handheld sensor and moisture in cellulose paper was obtained through destructive gravimetric analysis. The analysis of transient moisture was made using a first-order exponential model. From the analysis, the characteristic time constant ( τ ) and an estimated effective diffusion coefficient ( D ) were derived based on a one-dimensional Fick’s law formulation. The test results show considerable differences in moisture transport among the oils. The fastest moisture equilibration ( τ = 13–16 h) was found in ester oils whereas MO exhibited moderate progression ( τ = 22.7 h). The vegetable oils, on the other hand, showed the slowest transition ( τ = 30–40 h). Estimated diffusion coefficients ranged from 12.2 × 10 ⁻¹³ m ² s ⁻¹ for MIDEL 7131 to ∼3.9 × 10 ⁻¹³ m ² s ⁻¹ for sesame oil. These results provide quantitative assessments of moisture transport behaviour in oil–paper insulation systems. They contribute to improved modelling of moisture dynamics in transformer insulation and also support informed selection of insulating oils for transformer applications.

This research investigated the influence of insulating (Al2O3), semiconducting (TiO2), and conducting (Fe3O4) Nanoparticles (NPs) on the breakdown strength of Nomex-based polymeric Nanocomposites (NCs). Seven samples were fabricated and thermally aged at 145 °C for 600 h to simulate accelerated degradation. Fourier Transform Infrared Spectroscopy (FTIR) was conducted to unaged, 300 h, and 600 h aging durations to examine the chemical alteration and understand the NP-induced resistance of meta-aramid polymeric chains against the thermal aging. The AC Breakdown Voltage (ACBDV) was measured every 100 h of aging, and the results were analyzed using the Weibull distribution to extract the scale parameters, shape parameters, and p-values. Cumulative breakdown probabilities at 1%, 10%, and 50% risk levels were evaluated to assess the impact of different NPs on the breakdown mechanism. After 600 h of aging, the incorporation of 1 wt% Al2O3 improved the BDV of pristine Nomex by 28.2%. At a 50% risk probability, BDV enhancements of 63.16% (Al2O3), 48.89% (TiO2), and 42.39% (Fe3O4) were achieved, demonstrating the superior performance of NP-reinforced Nomex insulation under thermal stress..

Electrical energy controlled through the Internet is an innovative distributed technology that enables decentralized energy systems, termed the “Internet of Energy (IoE)”. The rapid evolution of cutting-edge technologies in distributed “Federated Learning (FL)” over blockchain architectures and smart contracts has unlocked new possibilities for implementing decentralized Energy through Internet services. This paper scrutinizes and classifies the latest advancements that leverage Blockchain, Smart Contracts, and Federated Learning (FL) for the IoE. The proposed system establishes a continuous information-gathering network to monitor and facilitate the sharing of distributed renewable energy, particularly in geographically challenging regions. Solar photovoltaic (PV) systems, pico-hydropower, and wind energy are some renewable sources. To enable efficient data collection and processing, a “Raspberry Pi” microcontroller board is employed, serving as an “Edge Node (EN)”. This node consists of an inbuilt WLAN (Wi-Fi) module, an output terminal voltage sensor, and load current sensors, ensuring essential support for the power generation unit. These Internet of Things (IoT)-based nodes are strategically positioned within a Wireless Sensor Network (WSN), contributing to the IoE ecosystem. The proposed models illustrate diverse ways Blockchain, Automated Digital Contracts, and Federated Learning can seamlessly integrate to enhance the primary domains of IoE, specifically decentralized energy-generating systems. Additionally, this network introduces a decentralized collaborative model training approach to predict the green power generation capacity of distributed hybrid renewable energy-generating units.

This study proposes an advanced method for sensing the aging state of epoxy nanocomposite insulation. For this purpose, five types of epoxy nano-composites (EPNC) samples (i.e. pure epoxy, epoxy with alumina or titania having concentration of 1wt% or 2wt%) have been prepared in the laboratory. The prepared test samples were undergone through accelerated electro-thermal aging (at 6 kV, 50-Hz and 100°C temperature) for 600 hours. After every 100 hours of aging duration, different lightning impulse voltage (both standard and non-standard) were applied on those test samples and corresponding impulse response current were recorded using the developed experimental arrangement. From the recorded signal, three aging-sensitive parameters are identified i.e. tail-time (T_t), peak impulse current (I_P), and area under the curve (A). Thereafter, those three parameters are correlated with the aging state of the insulation through three novel empirical relations. Finally, the effectiveness of the proposed empirical relationships is validated using a separate set of laboratory-prepared EPNC insulations having known aging state. The analysis of the validation results shows that the T_t, I_P, and A can sense the aging state of EPNC with maximum errors of 2.55%, 4.03%, and 3.60%, respectively. It is yet to be mentioned here that; this advanced methodology for assessing insulation aging state that is less susceptible to noise interference and consume less measurement time. Hence, this method leverages the analysis of three newly identified aging-sensitive parameters extracted from current responses under impulse voltage testing for establishing the empirical relations with aging.

This article presents a novel approach to accurately measure moisture levels in power transformer oil-paper insulation by analyzing dielectric response currents under standard and nonstandard lightning impulse (NSLI) voltages. Laboratory-prepared samples emulating transformer paper insulation with varying moisture levels are impregnated in different oils (mineral, vegetable, or their nanofluids) and are subjected to lightning impulse testing. Response currents are recorded, and transfer functions are evaluated by examining applied voltage and response current. Five coefficients (three from the numerator, ${a}2$ , ${a}1$ , and ${a}0$ , and two from the denominator, ${b}1$ and ${b}0$ ) are found sensitive to moisture content variations. Therefore, empirical relationships between these coefficients and paper moisture content (%pmc) are developed and are validated using newly prepared samples with different moisture contents, yielding maximum errors below 2.75% for NSLI and 3.5% for SLI. These findings offer valuable insights into transformer insulation conditions, enabling timely preventive measures to mitigate transformer failure.