50 research outputs found
A Study on the Phase-asynchronous PD Diagnosis Method for Gas Insulated Switchgears
Gas-insulated switchgear(GIS) is one of the most important power facilities and a valuable asset in a power system for providing stable and reliable electrical power. It has been in operation for more than 45 years due to its high reliability with low failure rate. Although GIS has a low-maintenance requirement, its failure caused by partial discharge(PD) leads to considerable financial loss.
The ultra-high frequency(UHF) method is an effective tool to detect insulation defects inside GIS and widely used for on-line and on-site diagnosis. It is also less sensitive to noise as well as better for PD detection compared to other measurement methods. Most of utilities, laboratories, and countries perform the PD detection using narrow-band or wide-band frequency ranges and classify types of PDs by conventional methods with a phase angle of the voltage applied to power equipment. In many cases of on-site PD measurement in the field, however, it is difficult to classify types of PDs due to the phase-asynchronous PD signals.
This thesis described a new method of PD diagnosis which can classify types of PDs without phase information of the voltage applied to GIS. The 327 cases of on-site measurement data were collected from 2003 to 2015. The statistical analysis of collected on-site measurement data was performed according to voltage classes, maintenance results, defect causes, and defect locations. From the statistical analysis, the most frequent PD and noise types were a floating element and an external interference, respectively.
To develop the new method of PD diagnosis which is applicable to the on-site PD diagnosis without phase synchronization, the features were extracted to classify defect types using the representative data of 82 cases, including 66 PD and 16 noise cases. The features consisted of 5 frequency and 6 phase parameters. The 5 frequency parameters were the number of distribution ranges, maximum value, ranges of first and second peak value, peak differences between first and second peak value, and density levels. 6 phase parameters were the number of phase groups, overall distribution ranges or not, the distribution ranges of each group, density levels, peak differences between first and second group, and shapes. 82 cases of representative data were selected through the review of data validation and analyzed using the designed 11 feature parameters, from which 5 effective parameters were extracted to identify the defect types using the decision tree-based technique by 4 steps: the number of groups in phase parameters(first step), shapes in phase parameters(second step), the number of distribution ranges & density levels in frequency parameters(third step), and ranges of first and second peak value in frequency parameters(fourth step). As a result, the decision tree-based diagnosis algorithm was able to classify types of 6 PDs and 4 noises and 77 of 82 cases were exactly classified. The diagnosis performance of new method proposed in this thesis therefore had an accuracy rate over 94% and was able to diagnose almost every type of defect.
The new method also was applied to on-site GIS diagnosis in South Korea and Malaysia to verify its reliability. In two cases, portable and on-line UHF PD systems were installed without phase synchronization, and the defect cause and location inside GISs were inspected visually by on-site engineers after on-site PD measurement. The two cases were analyzed by the new method based on decision-tree based diagnosis algorithm and results of the new method were identical to results of internal inspection. From the results, the new method of PD diagnosis proposed in this thesis is quite useful to classify various defect types using the phase-asynchronous PD signals in the on-site measurement.Contents โ
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Lists of Figures and Tables โ
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Abstract โ
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Chapter 1 Introduction 1
Chapter 2 Partial Discharges 8
2.1 PD Classification 8
2.2 Typical PD sources in GIS 17
2.3 Technical methods and strategies for PD diagnosis 23
2.4 PD analysis methods 33
Chapter 3 Data Acquisition and Analysis 39
3.1 Statistical analysis 41
3.2 Feature extraction 50
Chapter 4 New Method of PD Diagnosis 84
4.1 New PD diagnostic algorithm 84
4.2 Case studies in Korea and Malaysia 86
Chapter 5 Conclusions 95
References 98Docto
์ฑ๊ธด ์ง์ ์ด์ฐ๋์ ๋ฌธ์
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์๋ฆฌ๊ณผํ๋ถ, 2012. 8. ์ฒ์ ํฌ.์ด์ฐ๋์ ๋ฌธ์ ๋ ํ๋ ๊ณต๊ฐํค ์ํธ์ ์์ด ๊ฐ์ฅ ์ค์ํ ์ํ์ ๊ธฐ๋ฐ ๋ฌธ์ ์ ํ๋์ด๋ค. ์๋ง์ ์ํธ ์์คํ
๊ณผ ํ๋กํ ์ฝ๋ค์ด ์ด์ฐ๋์๊ฐ ์ด๋ ต๋ค๋ ๊ฐ์ ํ๊ฒ ์ค๊ณ ๋ฐ ์ ์๋๊ณ ์์ผ๋ฉฐ ์ด๋ฌํ ์ฐ๊ตฌ๋ ํ๋ฐํ๊ฒ ์งํ๋๊ณ ์๋ค.
์ด์ฐ๋์ ๊ธฐ๋ฐ ์ํธ ์์คํ
์ ํจ์จ์ฑ์ ์ง์์น ์ฐ์ฐ ์๋์ ์ง๊ฒฐ๋๋ค. Hoffstein๊ณผ Silverman์ ์ด์ฐ๋์ ๋ฌธ์ ๊ฐ ์ ์๋ ๊ตฐ์์ ๋น ๋ฅธ ์ง์์น๊ณผ ์์ ์ฑ์ ๋ณด์ฅํ๊ธฐ ์ํด ํด๋ฐ ์จ์ดํธ๊ฐ ์์ ์ง์๋ค์ ๊ณฑ(์ฑ๊ธด ์ง์ ๊ณฑ)์ ์ฌ์ฉํ ๊ฒ์ ์ ์ํ์๋ค. ํนํ GF(2^n)์์์ ์ ๊ณฑ์ฐ์ฐ ๊ทธ๋ฆฌ๊ณ Koblitz ํ์ด ๊ณก์ ์์์ ๋ ๋ฐฐ ์ฐ์ฐ์ ๊ฐ๊ฐ์ ๊ตฐ ์ฐ์ฐ๋ณด๋ค ํจ์ฌ ๋น ๋ฅด๊ธฐ ๋๋ฌธ์ ์ฑ๊ธด ์ง์ ๊ณฑ์ ์ฌ์ฉํ๋ฉด ์ฐ์ฐ์ ๋งค์ฐ ๊ฐ์ํ์ํฌ ์ ์๋ค.
๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ์ฑ๊ธด ์ง์ ๊ณฑ ์ด์ฐ๋์ ๋ฌธ์ ์ ์์ ์ฑ์ ๋ถ์ํ๋ค. ํ์ฌ์ ์ฑ๊ธด ์ง์ ๊ณฑ ์ด์ฐ๋์ ๋ฌธ์ ์ ์์ ์ฑ ๋ถ์์ ์ฑ๊ธด ์ง์ ์ด์ฐ๋์ ๋ฌธ์ ์ ๋ถ์ ๊ธฐ๋ฒ์ ์์กดํ๊ณ ์๋๋ฐ ์ด๋ก๋ถํฐ๋ ๋ณธ๋ ๋ฌธ์ ์ ์ ํํ ์์ ์ฑ์ ์ธก์ ํ ์ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ฑ๊ธด ์ง์ ๊ณฑ ์ด์ฐ๋์ ๋ฌธ์ ์ ์์ ์ฑ์ ๋ถ์ํ๊ธฐ ์ํด ๋งค๊ฐํ๋ ๋ถํ ์์คํ
์ ์ด์ฉํ์ฌ์ฑ๊ธด ์ง์ ๊ณฑ ์ด์ฐ๋์ ๋ฌธ์ ๋ฅผ ๊ณต๊ฒฉํ๋ ํจ์จ์ ์ธ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ํ์ฌ๊น์ง ์๋ ค์ง ์๊ณ ๋ฆฌ์ฆ ์ค ๊ฐ์ฅ ๋น ๋ฅธ ์๊ฐ ์์ ์ฑ๊ธด ์ง์ ๊ณฑ ์ด์ฐ๋์ ๋ฌธ์ ์ ํด๋ฅผ ์ฐพ๋๋ค. ์ค์ฆ์ ์ธ ์๋ก์จ Coron, Lefranc ๊ทธ๋ฆฌ๊ณ Poupard๊ฐ CHES 2005์์ ์ ์ํ GPS ์ธ์ฆ ์คํด์ ๋น๋ฐํค์ Hoffstein๊ณผ Silverman์ด ์ ์ํ (2,2,11)-์ง์์ ๋ํด ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ฉํ์ฌ ๊ฐ๊ฐ์ ๋ํด 2^{61.82} ๊ทธ๋ฆฌ๊ณ 2^{53.02} ๋ฒ์ ๊ตฐ ์ฐ์ฐ์ ์ฌ์ฉํ์ฌ ๋น๋ฐํค๋ฅผ ๋ณต๊ตฌํ ์ ์์์ ๋ณด์ธ๋ค.The discrete logarithm problem is one of the most important underlying mathematical problems in contemporary public key cryptography. Under the assumption that the problem is infeasible, a great number of cryptosystems have been constructed and researches in this area are still underway actively.
The efficiency of cryptosystems based on the discrete logarithm problem primarily relies on the speed at which exponentiation can be performed. On this line of research to address the issue Hoffstein and Silverman suggested the use of low Hamming weight product exponents to accelerate group exponentiation while maintaining the security level. Taking low Hamming weight product exponents, computation costs on GF(2^n) or Koblitz elliptic curves can be reduced significantly, where the cost of squaring and elliptic curve doubling is much lower than that of multiplication and elliptic curve addition, respectively.
In the thesis we focus our concern on the security analysis of the discrete logarithm problem of low Hamming weight product exponents. The current estimate on the security of the problem mainly depends on the approaches for the case of low Hamming weight exponents, which does not fit into the product form well.
We come up with parameterized splitting systems to resolve this problem. We show that it yields an efficient algorithm for the discrete logarithm problem of low Hamming weight exponents with lower complexity than that of any previously known algorithms.
To demonstrate its application, we attack the GPS identification scheme modified by Coron, Lefranc, and Poupard in CHES 2005 and Hoffstein and Silverman's (2,2,11)-exponents. The time complexity of our key recovery attack against the GPS scheme is 2^{61.82}, which was expected to be 2^{78}. Hoffstein and Silverman's (2,2,11)-exponent can be recovered with a time complexity of 2^{53.02}, which is the lowest among the known attacks.1. Introduction
2. The Low Hamming Weight Discrete Logarithm Problem
3. The Low Hamming Weight Product DLP
4. Parameterized Splitting Systems
5. A New Algorithm from Parameterized Splitting Systems
6. Cryptanalysis
7. Conclusion and Open ProblemsDocto
A Study on the Acoustic Detection of Partial Discharge in Insulation Oil
This thesis describes the frequency spectrum, positioning and propagation characteristics of the acoustic signals produced by the partial discharge(PD) in applying an acoustic detection technique for on-line insulation diagnosis of oil filled transformers.
Some defects were simulated by needle-to-plane, plane-to-plane and floating electrode systems. The acoustic signals were analyzed using a wideband AE sensor with frequency ranges of 100[kHz]โผ1[MHz] and narrowband AE sensor with resonant frequency of 140[kHz].
A decoupler and an amplifier were designed to detect and amplify only the acoustic signals. The decoupler separates the acoustic signal from the DC source without any distortion, and the amplifier produces a gain of 40[dB] in frequency ranges of 11[kHz]โผ4[MHz] at -3[dB].
From the experimental results, it was confirmed the frequency spectrums of the acoustic signals were 50[kHz]โผ180[kHz] for the needle-to-plane, 50[kHz]โผ200[kHz] for the plane-to-plane and 50[kHz]โผ150[kHz] for the floating electrode system. Using the three AE sensors, the positioning error of the PD was calculated to be within 3[%]. Also, output voltage, as a function of applied discharge magnitude, was linear, while the measured sensitivity of this experimental configuration was 23.65[mV/pC].
Therefore, this paper concludes that the acoustic detection technique for on-line insulation diagnosis of oil filled transformers could be used reliably.๋ชฉ ์ฐจ โ
ฐ
๊ทธ๋ฆผ ๋ชฉ์ฐจ โ
ฒ
Abstract โ
ด
์ 1 ์ฅ ์ ๋ก 1
1.1 ์ฐ๊ตฌ๋ฐฐ๊ฒฝ 1
1.2 ์ฐ๊ตฌ๋ชฉ์ ๋ฐ ๋ด์ฉ 2
์ 2 ์ฅ ์ด ๋ก 4
2.1 ์ ์ฐ์ดํ ๋ฐ ๋ถ๋ถ๋ฐฉ์ 4
2.2 ์ ์ฐ์ง๋จ๊ธฐ์ 11
์ 3 ์ฅ ์คํ์ฅ์น 19
3.1 ์ ๊ทน๊ณ 19
3.2 ๊ฒ์ถํ๋ก 21
์ 4 ์ฅ ์คํ ๋ฐ ๋ถ์ 25
4.1 ์ฃผํ์ ์คํํธ๋ผ 25
4.2 ๋ถ๋ถ๋ฐฉ์ ์ ์์นํ์ 29
4.3 ์ํฅ์ ํธ์ ์ ํํน์ฑ 33
์ 5 ์ฅ ๊ฒฐ ๋ก 39
์ฐธ ๊ณ ๋ฌธ ํ 41
๊ทธ๋ฆผ ๋ชฉ์ฐจ
๊ทธ๋ฆผ 2.1 ๋ด๋ถ๋ฐฉ์ 6
๊ทธ๋ฆผ 2.2 ์ฐ๋ฉด๋ฐฉ์ 7
๊ทธ๋ฆผ 2.3 ์ฝ๋ก๋๋ฐฉ์ 7
๊ทธ๋ฆผ 2.4 ๋ถ๋ถ๋ฐฉ์ ์ ๋ฑ๊ฐํ๋ก 8
๊ทธ๋ฆผ 2.5 ์ฐ๋ฉด๋ฐฉ์ ์ ๋ฐ์ 8
๊ทธ๋ฆผ 2.6 ๋ถ๋ถ๋ฐฉ์ ๋ฐ์ํจํด 9
๊ทธ๋ฆผ 2.7 ์ ์ค๊ฐ์ค๋ถ์๋ฒ 13
๊ทธ๋ฆผ 2.8 ์ ๊ธฐ์ ๊ฒ์ถ๋ฒ 14
๊ทธ๋ฆผ 2.9 ์ํฅ๊ฒ์ถ๋ฒ 15
๊ทธ๋ฆผ 2.10 ์ํ์ ์
์ฌํ, ๋ฐ์ฌํ ๋ฐ ๊ตด์ ํ 18
๊ทธ๋ฆผ 3.1 ์ ๊ทน๊ณ์ ๊ตฌ์ฑ 20
๊ทธ๋ฆผ 3.2 ์ญ๊ฒฐํฉํ๋ก 21
๊ทธ๋ฆผ 3.3 ์ญ๊ฒฐํฉํ๋ก์ ์ฃผํ์ ์๋ต 22
๊ทธ๋ฆผ 3.4 ์ฆํญํ๋ก 22
๊ทธ๋ฆผ 3.5 ์ฆํญํ๋ก์ ์ฃผํ์ ์๋ต 23
๊ทธ๋ฆผ 3.6 ์ํฅ๊ฒ์ถ์์คํ
24
๊ทธ๋ฆผ 4.1 ์ํฅ๊ฒ์ถ ์คํ๊ณ์ ๊ตฌ์ฑ 25
๊ทธ๋ฆผ 4.2 ์ํฅ์ ํธ์ ์ 27
๊ทธ๋ฆผ 4.3 ์ฃผํ์ ์คํํธ๋ผ 28
๊ทธ๋ฆผ 4.4 AE์ผ์์ ์์น 30
๊ทธ๋ฆผ 4.5 ๋ถ๋ถ๋ฐฉ์ ์ ์์นํ์ 31
๊ทธ๋ฆผ 4.6 ์ํฅ์ ํธ์ ๊ฒ์ถ 32
๊ทธ๋ฆผ 4.7 ๊ต์ ์คํ๊ณ์ ๊ตฌ์ฑ 33
๊ทธ๋ฆผ 4.8 ๊ต์ ํ์คํํ์ ์ 34
๊ทธ๋ฆผ 4.9 ๋ฐฉ์ ์ ํ๋๊ณผ ์ถ๋ ฅ์ ์์ ๊ด๊ณ 35
๊ทธ๋ฆผ 4.10 ์คํ๊ณ์ ๊ตฌ์ฑ 35
๊ทธ๋ฆผ 4.11 ๊ฑฐ๋ฆฌ์ ๋ฐ๋ฅธ ์ ์ค ์ํฅ์ ํธ ์ธก์ 36
๊ทธ๋ฆผ 4.12 ๊ฒ์ถํํ์ ์ 37
๊ทธ๋ฆผ 4.13 ๊ฑฐ๋ฆฌ์ ๋ฐ๋ฅธ ์ํฅ์ ํธ์ ๊ฐ์ 3
POSTECH Molecular graphics system development and Ab initio studies of water clusters
Maste
์๋ง๊ธ ๊ฐ์ฒ์ฌ์ ์ ๊ฒฝ์ ์ ํ๋น์ฑ ์ฌํ๊ฐ : ๋ด๋ถ๋งค๋ฆฝ์ผ๋ก ๋ฉธ์ค๋ ์ฐ๋ฆผ์ํ๊ณ์ ๊ฐ์ ์ฌ์ฉ๊ฐ์น๋ฅผ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ํ๊ฒฝ๋ํ์ :ํ๊ฒฝ๊ณํํ๊ณผ,2002.Maste
(A) study of Benjamin Briffen`s song cycle, [Seven sonnets of Michelangelo]
ํ์๋
ผ๋ฌธ(์์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :์์
ํ๊ณผ(์ฑ์
์ ๊ณต),2010.2.Maste
Pseudomonas putida Biotype B detosteroid isomerase์ and characterization of ketosteroid isomerase from pseudomonas putida biotype B
Maste
Bayesian single change point detection in a sequence of multivariate normal observations
A Bayesian method is used to see whether there are changes of mean, covariance, or both at an unknown time point in a sequence of independent multivariate normal observations. Noninformative priors are used for all competing models: no-change model, mean change model, covariance change model, and mean and covariance change model. We use several versions of the intrinsic Bayes factor of Berger and Pericchi (Berger, J.O. and Pericchi, L.R., 1996, The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association, 91, 109-122 Berger, J.O. and Pericchi, L.R., 1998, Accurate and stable Bayesian model selection: the median intrinsic Bayes factor. Sankkya Series B, 60, 1-18.) to detect a change point. We demonstrate our results with some simulated datasets and a real dataset.This work was supported by Grant No. R02-2000-00024 from the Basic Research Program of the Korea Science Engineering Foundation
์ฐ๊ตฌ๊ฐ๋ฐ๋น ์ง์ถ์ก์ด ๊ธฐ์ ๊ฐ์น์ ๋ฏธ์น๋ ์ํฅ์ ๊ดํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ฒฝ์ํ๊ณผ ๊ฒฝ์ํ์ ๊ณต,2000.Maste