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In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

In this letter, a methodology is proposed for automatically (and locally) obtaining the shape factor c for the Gaussian basis functions, for each support domain, in order to increase numerical precision and mainly to avoid matrix inversion impossibilities. The concept of calibration function is introduced, which is used for obtaining c. The methodology developed was applied for a 2-D numerical experiment, which results are compared to analytical solution. This comparison revels that the results associated to the developed methodology are very close to the analytical solution for the entire bandwidth of the excitation pulse. The proposed methodology is called in this work Local Shape Factor Calibration Method (LSFCM). Index Terms − improved numerical precision, matrix inversion difficulties, optimum shape factor calculation, radial point interpolation method (RPIM). I. INTRODUCTION One of the most used numerical methods for solving Maxwell's equations in time domain is the finite-difference (FD) technique, on which the finite-difference time-domain method (FDTD) is based [1], [2]. Meshless methods, such as the Radial Point Interpolation Method (RPIM), have become an important alternative to solve numerically problems involving partial differential equations [3], [4], [5], [6], due to the fact it provides greater geometric flexibility [7], [8] than FD-based methods. This kind of methdology employs a set of points for representing the analysis region, instead of grids. The field components are locally interpolated by using subgroups of points, called support domains [3] (Fig. 1).

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A finite-difference time-domain formulation based on piecewise linear recursive convolution method and on thin material sheets technique is developed for modelling terahertz graphene antennas and some other photonic components. The graphene sheets are modelled by specific recursive equations obtained for tangential electric field components allowing one to apply easily voltage or current sources between the sheets. The effective conductivity of graphene sheets in Yee's threedimensional lattice is calculated and used in simulations. A bowtie-like geometry is investigated, aiming at resonance tuning. The developed numerical formulation is validated by comparison of results with data published in literature.

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

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