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The significant challenges are fossil fuel dependence, climate change, and incremental energy cost in the twenty-first century. Looming environmental problems and growing concerns about the global energy crisis, individuals and organizations have sought new opportunities and technologies to meet the growing demand for clean and sustainable energy systems. Electrification of transportation system is a promising way to green transportation system and reduce climate change. In recent years, new energy electric vehicles (NEEVs) have become increasingly popular in the automotive industry and are poised to replace the internal-combustion engine (ICE) to protect environment from pollution. This paper provides an in-depth investigation into the present status, latest deployment, and future prospects in the implementation of NEEVs while introducing the subsystems and their components: electrification transportation degrees, power battery pack, electrical propulsion system, charring architecture, and international standards. Additionally, extending from limited knowledge and ability, the enabling technologies and prospects for future development of NEEVs are also presented in this paper. A total of 131 publications are arranged and appended for quick referencing. It is envisaged that researchers and engineers involved in these fields could find this paper very valuable and a one-stop source of rich information. [ABSTRACT FROM AUTHOR]

An oil-filled transformer should be able to operate for a long time with proper maintenance. One of the best diagnostic methods for oil-immersed transformer condition is dissolved gas analysis (DGA). However, there are times where the produce of stray gassing event might lead to fault indication in the transformer. Machine learning algorithms are used to classify the DGA data into normal condition and corresponding faults based on IEEE limits and Duval pentagon method. The algorithms that will be used include boosted trees, RUS boosted trees and subspace KNN, which belongs to the same ensemble group. Data resampling technique (SMOTETomek) is applied and shows further improvement on the accuracy of predictions by machine learning algorithms when deal with imbalance data. The algorithms are able to achieve the accuracy of 82.6% (boosted trees), 81.2% (RUS boosted trees) and 72.5% (subspace KNN), respectively, when validated with actual transformer condition. [ABSTRACT FROM AUTHOR]