• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.6
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• pp.527-533
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• 2003

Recently, need of power stability and reliability has been increased as the spread of the sensitive electronic loads. Therefore, the power quality(PQ) problems become the hot Issue of public interest in these days. Many kinds of algorithms have been studied for the monitoring of PQ problems using the monitoring algorithms with RMS or FFT scheme, but it is still not enough to measure all of the PQ problems. This paper proposed the application of discrete wavelet transform(WT) for the PQ problems monitoring with the Introduction of WT theory, and the design of PQ monitoring data acquisition system Is described with some experimental results to verify the validity of the proposed PQ monitoring algorithm.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.88-90
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• 2003

In this paper, an algorithm to compensate the distorted signals due to CT(Current Transformer) saturation is suggested. Firstly, WT(Wavelet Transform) is used to detect a start point and an end point of saturation. Filter banks which can be easily realized in real-time applications are employed in detecting CT saturation. Secondly, least-square curve fitting method is used to restore the distorted section of the secondary current. Fault simulations are performed on a power system model using EMTP(Electromagnetic Transient Program). A series of test results indicate that WT has superior detection accuracy and the proposed algorithm which shows very stable features under various levels of remanent flux is also satisfactory.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.51-52
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• 2011

본 논문에서는 WT(Wavelet Transform)와 SVD(Singular Value Decomposition)기법을 결합한 WSVD(Wavelet Singular Value Decomposition)를 사용하여, 송전계통에서 부하 탈락 시 나타나는 특성 및 외란검출의 유효성을 분석하였다. WSVD 방식을 이용한 외란검출을 모의하기 위해 EMTP-RV를 이용하여 부산 및 경남 일부지역 345kV급 송전계통을 모델링하였고, 이 계통에서 부하 탈락을 모의하였다. WSVD의 계산은 MATLAB을 통해 수행하였으며, 이 결과를 바탕으로 전력계통에서 부하 탈략량의 변화에 따른 특징을 분석하였다

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.244-245
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• 2011

In this paper, the algorithm for detecting load shedding based on Wavelet Singular Value Decomposition(WSVD) is proposed. WSVD is method of signal processing which combine Wavelet Transform(WT) and Singular Value Decomposition(SVD) to analyze transients in power system. 345kV Busan transmission system is modeled by EMTP-RV and simulations according to successive change of load capability are conducted. This paper analyzes characteristics of WSVD by using simulation results and proposes algorithm for detecting load shedding.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.640-641
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• 2007

In this paper, the discrete wavelet transform (DWT) was applied a fault detection of a synchronous generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a multi-level decomposition (MLD). The proposed algorithm of a fault detection of a generator using Daubechies WT (wavelet transform) was executed with a C language for the commend line function and for the real time realization after analyzing MATLAB's graphical interface.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.66-66
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• 2007

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.204-207
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• 2002

The current paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm In the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3D-SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.96-102
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• 2005

Acoustic Emission(AE) technique has been applied to not only material characterization evaluation but also on-line monitoring of the structural integrity. The AE source location technique is very important to identify the source, such as crack, leak detection. Since the AE waveforms obtained from sensors are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analysis of the transient wave-form. In this study, we have divided the region of interest into a set finite elements, and calculated the arrival time differences between sensors by using the velocities at every degree from 0 to 90. A new technique for the source location of acoustic emission in fiberboard plates has been studied by introducing Wavelet Transform(WT) do-noising technique. WT is a powerful tool for processing transient signals with temporally varying spectra. If the WT de-noising was employed, we could successfully filter out the errors of source location in fiberboard plates by arrival time difference method. The accuracy of source location appeared to be significantly improved.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.49-62
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• 1994

Look direction bias in a single look SAR image can often be misinterpreted in the geological application of radar data. This paper investigates digital processing techniques for SAR image data integration and compensation of the SAR data look direction bias. The two important approaches for reducing look direction bias and integration of multiple SAR data sets are (1) principal component analysis (PCA), and (2) wavelet transform(WT) integration techniques. These two methods were investigated and tested with the ERS-1 (VV-polarization) and CCRS*s airborne (HH-polarization) C-SAR image data sets recorded over the Sudbury test site, Canada. The PCA technique has been very effective for integration of more than two layers of digital image data. When there only two sets of SAR data are available, the PCA thchnique requires at least one more set of auxiliary data for proper rendition of the fine surface features. The WT processing approach of SAR data integration utilizes the property which decomposes images into approximated image ( low frequencies) characterizing the spatially large and relatively distinct structures, and detailed image (high frequencies) in which the information on detailed fine structures are preserved. The test results with the ERS-1and CCRS*s C-SAR data indicate that the new WT approach is more efficient and robust in enhancibng the fine details of the multiple SAR images than the PCA approach.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.695-706
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• 2014

Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.