IEEJ Transactions on Fundamentals and Materials
Online ISSN : 1347-5533
Print ISSN : 0385-4205
ISSN-L : 0385-4205
Volume 139, Issue 3
Displaying 1-12 of 12 articles from this issue
Special Issue on Recent Developments in Basic and Applied Researches on Electrical Discharge
Special Issue Paper
Paper
  • Konan Yamakawa, Masafumi Yashima, Tatsuki Okamoto
    2019 Volume 139 Issue 3 Pages 174-180
    Published: March 01, 2019
    Released on J-STAGE: March 01, 2019
    JOURNAL FREE ACCESS

    This paper describes an integral equation to calculate the stochastic fluctuation of partial discharge (PD) occurrence under sinusoidal voltage stress based on a simple PD model. For the simplicity of calculation we used a symmetric PD model for positive and negative PD characteristics with measurements of a symmetric electrode system. In this paper we made a progress in calculation using asymmetric PD characteristics using different PD parameters for positive and negative PDs. The stochastic behavior of PD fluctuation is assumed to arise from the fluctuation of PD delay time after the inception voltage and the fluctuation of PD inception voltage (PDIV) as before but different parameters for positive and negative ones. The authors solved the equation with numerical method and showed several φ-n, φ-q distribution patterns as before but more realistic characteristics.

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  • Takahiro Sato, Masafumi Fujita
    2019 Volume 139 Issue 3 Pages 181-187
    Published: March 01, 2019
    Released on J-STAGE: March 01, 2019
    JOURNAL FREE ACCESS

    This paper presents a topology optimization method using a greedy algorithm for submodular maximization. This method is based on a shape representation using the normalized Gaussian network. The weight coefficients of Gaussians are discretized to +1/-1, and then their values are greedily inverted. Hence, the computational cost of the present method is relatively smaller than that of evolutionary algorithms. The present method is applied to a magnetic shield optimization problem. It is shown that Pareto solutions can be obtained by the present method. In addition, it can be found from the numerical results that the stochastic greedy algorithm can effectively reduce the computational time compared with the conventional greedy algorithm. As a result, it is shown that a 3-D optimization problem with over 3000 design variables can be solved within acceptable computational time.

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