DC dielectric characteristics and conception of insulation design for DC GIS

Hasegawa, T.; Yamaji, K.; Hatano, Masayuki; Aoyagi, H.; Taniguchi, Yukimasa; Kobayashi, Akio

doi:10.1109/61.544257

Cited by 27 publications

(10 citation statements)

References 2 publications

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“…Kurrer et al [18] observed that mobile particles generate high partial discharge magnitude and high amplitude of UHF signal under AC voltages. Hasegawa et al [19] showed the use of acoustic emission (AE) technique and UHF method to identify Partial discharge in GIS operated under DC voltages, to check the reliability design of 500 kV DC GIS. Figure 6 shows characteristic variation in frequency content of UHF signal generated by the partial discharge formed due to particle movement in SF 6 gas under DC voltages, at different pressure.…”

Section: Analysis Of Levitation Voltagementioning

confidence: 99%

Understanding the partial discharge activity of conducting particles in GIS under DC voltages using the UHF technique

Sarathi

Umamaheswari

2009

Int Trans Elec Energy Syst

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SUMMARYThe major cause of failure of DC-GIS is due to presence of foreign particles causing partial discharges in the insulation structure. The particle movement in gas insulated system (GIS) radiates electromagnetic waves and the bandwidth of the signal lies in the range 1-2 GHz. Increase in applied DC voltage/pressure has not altered the frequency content of the ultra high frequency (UHF) signal generated due to partial discharge formed by particle movement. The UHF sensor could recognize the breakdown of sulfur-hexa-fluoride (SF 6 ) gas under DC and Lightning impulse voltages and the frequency content of the signal captured by the UHF sensor lies up to 500 MHz. Mounting UHF sensor in GIS could allow one to classify internal partial discharges from breakdown, at the time of testing/during operation.

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Section: Analysis Of Levitation Voltagementioning

confidence: 99%

Understanding the partial discharge activity of conducting particles in GIS under DC voltages using the UHF technique

Sarathi

Umamaheswari

2009

Int Trans Elec Energy Syst

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“…Given the connection of new large-scale energy and the rapid development of HVDC transmission technology and flexible HVDC technology, DC gas-insulated equipment (DC-GIE) has attracted significant attention because of its technological advantages in improving system reliability and reducing space occupation [1][2][3][4][5][6]. Pure SF 6 gas has stable chemical properties and is not easily decomposed.…”

Section: Introductionmentioning

confidence: 99%

“…Pure SF 6 gas has stable chemical properties and is not easily decomposed. However, under the effect of the partial discharge (PD), spark discharge, arc discharge, overheating, and other factors, SF 6 gas is decomposed into various low-fluoride sulfides (such as SF 5 , SF 4 , SF 3 , SF 2 , and SF). These low-fluoride sulfides then react with the trace air and moisture that inevitably exist in the DC-GIE to produce stable decomposition products, such as sulfuryl fluoride (SO 2 F 2 ), thionyl fluoride (SOF 2 ), thionyl tetrafluoride (SOF 4 ), sulfur dioxide (SO 2 ), carbon tetrafluoride (CF 4 ), carbon dioxide (CO 2 ), hydrogen fluoride (HF), and hydrogen sulfide (H 2 S) [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Tang

Yang

et al. 2017

Energies

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Four typical types of artificial defects are designed in conducting the decomposition experiments of SF 6 gas to obtain and understand the decomposition characteristics of SF 6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF 6 in diagnosing the type and severity of insulation fault in DC SF 6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF 6 gas and generate five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentration, effective formation rate, and concentration ratio of SF 6 decomposed components can be associated with the PD types. Furthermore, back propagation neural network algorithm is used to recognize the PD types. The recognition results show that compared with the concentrations of SF 6 decomposed components, their concentration ratios are more suitable as the characteristic quantities for PD recognition, and using those concentration ratios in recognizing the PD types can obtain a good effect.

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“…In order to obtain the subset S m+1 , another feature must be selected from {S n -S m } and combined with S m to form S m+1 , which also meets mRMR principle. Therefore, the (m + 1)-th feature should satisfy Equation (10) or (11).…”

Section: Mrmr Principlementioning

confidence: 99%

“…DC-GIE received much attention due to the rapid development of high-voltage DC (HVDC) transmission and flexible HVDC technologies [6][7][8][9][10][11]. However, when partial discharge (PD) occurs inside the equipment, this phenomenon can decompose SF 6 and generate a series of low-fluorine sulfides (such as SF 5 , SF 4 , SF 3 , and SF 2 ).…”

Section: Introductionmentioning

confidence: 99%

Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect

Tang

Yang

et al. 2017

Energies

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Abstract:The decomposition characteristics of a SF 6 gas-insulated medium were used to diagnose the partial discharge (PD) severity in DC gas-insulated equipment (DC-GIE). First, the PD characteristics of the whole process were studied from the initial PD to the breakdown initiated by a free metal particle defect. The average discharge magnitude in a second was used to characterize the PD severity and the PD was divided into three levels: mild PD, medium PD, and dangerous PD. Second, two kinds of voltage in each of the above PD levels were selected for the decomposition experiments of SF 6 . Results show that the negative DC-PD in these six experiments decomposes the SF 6 gas and generates five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentrations and concentration ratios of the SF 6 decomposed components can be associated with the PD severity. A minimum-redundancy-maximum-relevance (mRMR)-based feature selection algorithm was used to sort the concentrations and concentration ratios of the SF 6 decomposed components. Back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to diagnose the PD severity. The use of C(CO 2 )/CT 1 , C(CF 4 )/C(SO 2 ), C(CO 2 )/C(SOF 2 ), and C(CF 4 )/C(CO 2 ) shows good performance in diagnosing PD severity. This finding serves as a foundation in using the SF 6 decomposed component analysis (DCA) method to diagnose the insulation faults in DC-GIE and assess its insulation status.

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DC dielectric characteristics and conception of insulation design for DC GIS

Cited by 27 publications

References 2 publications

Understanding the partial discharge activity of conducting particles in GIS under DC voltages using the UHF technique

Understanding the partial discharge activity of conducting particles in GIS under DC voltages using the UHF technique

Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect

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