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Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance. [ABSTRACT FROM AUTHOR]

New and emerging demand for polyurethane (PU) continues to rise over the years. The harmful isocyanate binding agents and their integrated PU products are at the height of environmental concerns, in particular PU (macro and micro) pollution and their degradation problems. Non‐isocyanate poly(hydroxy urethane)s (NIPUs) are sustainable and green alternatives to conventional PUs. Since the introduction of NIPU in 1957, the market value of NIPU and its hybridized materials has increased exponentially in 2019 and is expected to continue to rise in the coming years. The secondary hydroxyl groups of these NIPU′s urethane moiety have revolutionized them by allowing for adequate pre/post functionalization. This minireview highlights different strategies and advances in pre/post‐functionalization used in biobased NIPU. We have performed a comprehensive evaluation of the development of new ideas in this field to achieve more efficient synthetic biobased hybridized NIPU processes through selective and kinetic understanding. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is a tool for evaluating mechanical integrity of transformer's core and winding. However, several studies have reported that FRA is also sensitive to other parameters such as temperature, moisture content and presence of oil. Since some of these parameters are critical in the insulation degradation process, it is interesting to further investigate the possibility of using FRA for insulation assessment. In this paper, the insulation conditions of three field transformers are investigated using FRA. This is performed after statistical analysis on the response suggested no mechanical damage on the transformers. The responses are further analyzed to determine the percentage of change of winding capacitance. This is achieved by comparing resonance frequencies between the responses. The percentage of change represents the amount of degradation in the insulation. This is because as the insulation condition degrades, the response is shifted towards lower frequencies. This new investigation demonstrates the applicability of FRA to estimate the insulation degradation. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Wind energy is one of the most common types of renewable energy resource. Due to its sustainability and environmental benefits, it is an emerging source for electric power generation. Rapid and random changes of wind speed makes it an irregular and inconsistent power source when connected to the grid, causing different technical problems in protection, power quality and generation dispatch control. Due to these problems, effective intermittent smoothing approaches for wind power output are crucially needed to minimize such problems. This paper reviews various intermittent smoothing approaches used in smoothing the output power fluctuations caused by wind energy. Problems associated with the inclusion of wind energy resources to grid are also briefly reviewed. From this review, it has been found that battery energy storage system is the most suitable and effective smoothing approach, provided that an effective control strategy is available for optimal utilization of battery energy system. This paper further demonstrates different control strategies built for battery energy storage system to obtain the smooth output wind power. [ABSTRACT FROM AUTHOR]