Yazar "Ugur, Mukden" seçeneğine göre listele

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    Amplitude and Frequency Estimation of Power System Signals Using Independent Component Analysis
    (IEEE, 2013) Uzunoglu, Cengiz Polat; Ugur, Mukden; Turan, Faruk; Cekli, Serap
    In this paper independent component analysis (ICA) method for amplitude and frequency estimation of distorted power system signals is proposed. In order to protect system and keep it in safe operation mode the amplitude and frequency estimation should be conducted accurately. Transient disturbances of the power system may reduce estimation performance due to the distortion strength. In this study white noise and pulse noise which are very common for power systems, are used to contaminate power system signal. Thus, the proposed method is employed to decompose noise from system signal and hence to improve the efficiency of the estimation. Computer simulations have been carried out for the performance analysis of the ICA method and the comparison of the results of the proposed method with the conventional filters are displayed by using mean square error (MSE) values.
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    Location estimation of partial discharge-based electromagnetic source using multilateration with time difference of arrival method
    (SPRINGER, 2018) Gulnihar, Kaan; Cekli, Serap; Uzunoglu, Cengiz Polat; Ugur, Mukden
    In high-voltage systems partial discharges (PD) may occur due to the degradation of insulation materials in addition to different scenarios such as material properties, construction, setup and operation conditions. Especially for a power transformer, the degradation of inner insulation may prevent regular operation and hence cause failure. In a long time period even low-level PD activity may cause degradation on the insulator. If the deterioration caused by the PD is detected in an early phase, potential damage may be prevented. Due to the complex and close structure of power transformers and other high-voltage systems, it is not easy to estimate the exact location of a PD. This study proposes a novel approach to detect and analyze an artificial PD in a laboratory room setup, which is especially designed for simulation of possible PD source in a large scale structure such as power transformer. Electromagnetic (EM) PD sensors are commonly used to detect electromagnetic pulses emitted from PD sources. In this work, the time differences of arrivals (TDOA) which are obtained from PD signals are subjected to multilateration technique to estimate the exact location. A novel energy level method is introduced to overcome correct TDOA extraction problem. Cramer-Rao bound (CRB) is used for calculation of the minimum achievable estimation error of proposed method. In order to display the accuracy of location estimation, CRB and the mean square error graphics of the estimated location parameters are given for the comparison.
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    MODELLING OF CHAOTIC SURFACE TRACKING ON THE POLYMERIC INSULATORS WITH HIDDEN MARKOV MODELS
    (IEEE, 2014) Uzunoglu, Cengiz Polat; Cekli, Serap; Ugur, Mukden
    In this study, chaotic surface tracking patterns observed on polymeric high voltage (HV) outdoor insulation materials were investigated and simulated The polymeric samples are tested according to the IEC 587 Inclined Plane Tracking Test Standard Since the chaotic surface tracking patterns manifests smutty and disordered images, they are preprocessed and purified by image processing tools. Internal and external effects may severely decrease insulation performance. In order to examine external effects, samples are subjected to moisture and vibration effect Polymer samples are investigated by their fractal dimension which is a prominent tool for analyzing chaotic images. To simulate these chaotic surface tracking patterns Hidden Markov Models (HMM) are used
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    Monofractal and Multifractal Analysis of Discharge Signals in Transformer Pressboards
    (UNIV SUCEAVA, FAC ELECTRICAL ENG, 2018) Cekli, Serap; Uzunoglu, Cengiz Polat; Ugur, Mukden
    Pressboards are commonly used as insulating materials employed in electrical connections of transformers. Pressboards are typically made from vegetable fibers, which contain cellulose. The proper operation of power transformer depends mainly on constant monitoring of insulation materials against failure. Due to the complex and close structure of power transformers, it is very challenging task to detect failure and hence possible location of degradation of pressboard internally. Generated discharge signals may result in breakdown of system insulation and system failure. In this study, the investigation of insulation degradation is fulfilled by analyzing discharge signals and simultaneously produced acoustic signals during discharges. For this purpose, a test setup is used for investigating discharge signals of pressboard samples under different electrical stresses. This paper proposes monofractal and multifractal analysis of discharge and acoustic signals of pressboards. The Higuchi's method is an effective monofractal analysis tool for measurement of fractal dimension of self-affine signals, which is proposed for online monitoring of discharge signals of pressboards. In order to investigate obtained discharge signals with accelerated fluctuations effectively, multifractal detrended fluctuation analysis is proposed for these signals, which exhibit nonlinear behavior.
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    Statistical analysis of induced magnetic fields on oil-impregnated insulation pressboards
    (SPRINGER, 2020) Atalar, Fatih; Uzunoglu, Cengiz Polat; Cekli, Serap; Ugur, Mukden
    Electrical insulation materials are highly exposed to electrical network-based electric and magnetic fields in power systems. In electrical fields, electrical insulation materials are prone to breakdown and cause failure. A transformer failure which is related to pressboard insulation may lead to total breakdown and hence system malfunction in a total manner. In this study, a test setup is used to conduct discharge tests for pressboards in different thicknesses where main interest is originated magnetic fields on the pressboards. These tests are fulfilled with spherical and rod electrodes in transformer oil where magnetic field sensors are used to acquire discharge-based magnetic field data. By investigating high-voltage stresses with different levels, possible breakdown voltage of a pressboard is predicted and statistically analyzed. In addition to magnetic field measurements, discharge current measurements are taken; however, contrary to conventional studies, this study assesses magnetic field data which are dependent on the thickness of pressboard insulation. For different voltage levels (13 kV and 22 kV for different stress levels), magnetic field measurements and current waveforms are obtained by using magnetic field sensors and high-speed oscilloscope. Magnetic field time series signals are subjected to wavelet analysis, and wavelet coefficients are obtained. Rather than utilizing time series current signals or time series magnetic field signals, wavelet coefficients of magnetic field signals are taken into consideration as a novel approach. These coefficients are processed by multifractal analysis, and finally, the integrity of the pressboard is determined as in proper mode (no failure) or in breakdown mode.