Studying Three Phase Supply in School

The power distribution of nearly all major countries have accepted 3-phase distribution as a standard. With increasing power requirements of instrumentation today even a small physics laboratory requires 3-phase supply. While physics students are given an introduction of this in passing, no experiment work is done with 3-phase supply due to the sheer possibility of accidents while working with such large powers. We believe a conceptual understanding of 3-phase supply would be useful for physics students with hands on experience using a simple circuit that can be assembled even in a high school laboratorys

STUDY ON CHARACTERISTICS OF TRIPLEN HARMONICS PRODUCED BY SYNCHRONOUS GENERA TOR FLOWING THROUGH TRANSFORMER UNDER VARIOUS LOAD CONDITIONS

The existence of triplen hrumonics in equipments has been recognized dated back in the 19th century. Even though many discovery has been made regarding the behaviour of triplen hrumonics in electrical equipments, many scope still needed to be studied and researched. Thus, the objective of this paper focuses on the characteristics of triplen hrumonics produced by synchronous generator flowing through various transformer winding configurations subjected to variety of load conditions. The scope of experiment for this study covers on the laboratory experiments using lab-scaled salient pole synchronous generator that connects directly to the transformer and load. The transformer has four winding configurations and the load can varies in different resistance and inductance connections in a circuit. The study also covers the PSCAD modeling software. The results obtained from PSCAD modeling can be analyzed and also compare with the laboratory experiments that have been conducted. The results and findings from the lab experiments and PSCAD modeling are to be discussed and thus, able to come up with an appropriate conclusion in the characteristics of triplen hrumonics produced by synchronous generator flowing through various transformer winding configurations under various load conditions. In conclusion, this study provides a very important know ledge of trip len hrumonics current propagation through transformer

Zero phase sequence voltage injection for the alternate arm converter

The Alternate Arm Converter (AAC) is a voltage source converter being developed as an alternative to the Modular Multilevel Converter (MMC) for HVDC power transmission and reactive power compensation. Each Arm of the converter contains high voltage series IGBT Director Switches and full-bridge cells, which enables the VSC to ride through AC and DC network faults. This paper describes how the AAC can be optimised by modulating the converter terminal voltages with zerophase sequence triplen harmonic components. The optimisation reduces the ratio of the number of the full-bridge cells compared to the simpler Director Switches which offers a valuable improvement in footprint and efficiency

Faster Depth-Adaptive Transformers

Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences. In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth, namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation. We conduct experiments on the text classification task with 24 datasets in various sizes and domains. Results confirm that our approaches can speed up the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive approaches.Comment: AAAI-2021. Code will appear at: https://github.com/Adaxry/Adaptive-Transforme