• KEPCO Journal on Electric Power and Energy
• /
• v.2 no.1
• /
• pp.43-47
• /
• 2016

This system is a diagnosis system that checks whether it causes a partial discharge of a power cable or not. It is to classify normal from abnormal-normal, PD (Partial Discharge) sound through analysis of RLCR (Relative Level Crossing Rate) and spectrogram energy algorithm. Partial discharge diagnostic system has a function that stores PD sound and analyzes the data. The wave shape of PD sound is similar to noise and is systematically generated by partial discharge. Therefore, in this paper, we could discreminate between normal and abnormal case using relative level crossing rate (RLCR) and spectrogram of frequency energy rate.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1996.11a
• /
• pp.283-286
• /
• 1996

A signal generated by partial discharge in low density Polyethylene (LDPE) and cross- linked polyethylene (XLPE) has been detected by ultrasonic sensors. The observed signal is analysed with the quantity of partial discharge, number of peaks, and energy ($\Sigma$y$^2$). It was found that signals between LDPE and XLPE are quite different and that the signal of XLPE has very long duration time and rising time, and that the quantity of partial discharge of XLPE is saturated. It was also found that tree retardant XLPE (TR-XLPE) has lower quantity of partial discharge, number of peaks, and energy than naturalal XLPE.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.3
• /
• pp.349-356
• /
• 2015

Remote monitoring partial discharge sensor, equipping with hybrid filter combining optical and electrical noise reduction capabilities and with signal integrating function to calculate total arc energy, was investigated. Hybrid filter showed insensitivity to fluorescent and incandescent lamps under simulated distribution panel condition. Signal integrating function showed selective detection capability corresponding to different arc energy levels, while convention arc sensor had difficulty to discriminate arc energy level due to bursty arc waveform and peak level detection characteristics. The sensor showed possibility for application to remote monitoring partial discharge sensor, since it detected arc energy level corresponding to normal open and close discharge in low voltage 100A MCCB at 2m distance.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.9
• /
• pp.1784-1789
• /
• 2009

This paper describes the measurement technology of the dissolved hydrogen gas due to partial discharge in oil using gas sensor. For higher resolution and less error in measurement of the dissolved hydrogen gas in oil, the sensor outputs with ambient temperature which affect the sensor output characteristics should be considered. The sensor output trends with ambient temperature and the properties of the dissolved hydrogen gas in oil with partial discharge characteristic were analyzed through the test results. It was indicated that the sensor peak and the base voltage with measuring time were affected by ambient temperature and the measurement errors of the sensor output by temperature were reduced by using the difference between the peak and the base voltage rather than just the peak voltage. In addition, the hydrogen gas sensor outputs were increased with the increase of partial discharge energy.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.157-163
• /
• 2017

This system is a diagnosis system that checks whether it causes a partial discharge of a power cable or not. PD(Partial Discharge) is detected by FBDS(Frequency Band Detection Sensor). That is, it means a acoustic sensor capable of detecting each frequency band. The wave shape of PD sound is similar to noise and is systematically generated by partial discharge. Therefore, in this paper, we could discriminate between normal and abnormal case using relative level crossing rate(RLCR) and spectrogram of frequency energy rate.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.1093-1101
• /
• 2015

Partial Discharge (PD) is a physical phenomenon, which causes defects and damages to the insulation. This phenomenon is regarded as the most important source of fault and defect in power transformers. Therefore, methods of high speed and precision are considered of special importance for the maintenance of transformers in localization of the origin of partial discharge. In this paper, the transformer winding is first modeled in a transient state by using RLC ladder network and multiconductor transmission line (MTL) models. The parameters of the two models were calculated by Ansoft Maxwell software, and the simulations were performed by Matlab software. Then, the PD pulses were applied to the models with different widths of pulses. With regard to the fact that the signals received after the application of PD had a variable frequency nature over time, and based on the wavelet transform and signal energy, a new method was presented for the localization of PD. Ultimately; the mentioned method was implemented on a 20 kV winding distribution transformer. Then, the performances of the models used in this paper, including RLC and MTL models, were compared in different frequency bands for the correct distinction of partial discharge location.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.2088-2093
• /
• 2004

Harmful elements from the exhaust gases are caused by incomplete combustion of mixture inside the engine cylinder and this abnormal combustion like misfire or partial burning is the direct cause of the air pollution and engine performance degradation. In this study, I obtain the shapes of spark, voltage and current generated when changing the experimental parameters such as grounded electrode shapes, electrode gap and the material of center electrodes. After that, I produce ignition energy by using the voltage and current and classify ignition energy into capacitive discharge energy and inductive discharge energy.

• KEPCO Journal on Electric Power and Energy
• /
• v.8 no.2
• /
• pp.119-126
• /
• 2022

A growing number of gas-insulated transformers in underground power substations in urban areas are approaching 20 years of operation, the time when failures begin to occur. It is thus essential to prevent failure through accurate condition diagnosis of the given facility. Various solid insulation materials exist inside of the transformers, and the generated decomposition gas may differ for each gas-insulated equipment. In this study, a simulation system was designed to analyze the deterioration characteristics of SF₆ decomposition gas and insulation materials under the conditions of partial discharge and thermal fault for diagnosis of gas-insulated transformers. Degradation characteristics of the insulation materials was determined using an automatic viscometer and FT-IR. The analysis results showed that the pattern of decomposition gas generation under partial discharge and thermal fault was different. In particular, acetaldehyde was detected under a thermal fault in all types of insulation, but not under partial discharge or an arc condition. In addition, in the case of insulation materials, deterioration of the insulation itself rapidly progressed as the experimental temperature increased. It was confirmed that it was possible to diagnose the internal discharge or thermal fault occurrence of the transformer through the ratio and type of decomposition gas generated in the gas-insulated transformer.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.31 no.4
• /
• pp.255-260
• /
• 2018

In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing ${\Phi}-q-n$ cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.1
• /
• pp.192-199
• /
• 2016

With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.