Pattanadech, Norasage
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Preferred name
Pattanadech, Norasage
Alternative Name
Pattanadech, N.
Main Affiliation
Email
norasage.pa@kmitl.ac.th
12 results
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Item type:Publication, Dielectric response analysis for cable joint problems in medium voltage underground cable system(2020-01-01); This paper represents the investigation of dielectric response analysis of a cable system composed of a medium voltage XLPE underground cable, a heat-shrink cable joint and a heat-shrink cable termination. A 12/20(24) kV single core XLPE underground cable with the nominal cross-section area of 240 mm<sup>2</sup>, three pieces of 22 kV cable joints, and eight pieces of 22 kV cable termination were prepared for the experiment. Then, polarization and depolarization current (PDCs) measurement were performed. The experiments were divided into two parts. The first part was composed series of three tests, A — C, and the second part consisted series of two tests, D — E, as follows: Test A examined dielectric response of a 6 m healthy XLPE underground cable, Test B examined dielectric response of a 6 m healthy XLPE underground cable with two healthy cable terminations, Test C examined dielectric response of a two of 3 m healthy XLPE underground cable with healthy cable terminations connected by a healthy cable joint, Test D used the test object similarly as case C but the cable joint was contaminated with copper particles, and Test E used the test object similarly as case C but the cable joint was contaminated with tiny pieces of water blocking. From the experiment, the PDCs and the derived dielectric parameters such as geometrical capacitance, insulation resistance, polarization index, depolarization index, charge difference curve and charge ratio curve were analyzed. The PDCs and the derived parameters were found different for each case of study as details in this paper. Besides, the charge ratio proposed by authors is a valuable parameter for the diagnosis of insulation system of high voltage equipment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dielectric response analysis of mineral oil immersed transformer, Natural Ester(FR3) immersed transformer, and palm oil immersed transformer(2019-06-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Online PD Measurement by Detecting the Pulsed Compensating Current in High Voltage Equipment(2022-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Verification of HF Signal Transmission via Grounding System for Online PD Measurement(2022-01-01); ; Nimsanong, PhethaiThis paper discusses signal transmission in the grounding system. During online partial discharge (PD) measurement for the high voltage (HV) apparatus, various PD pulse currents recharge the dielectric of HV apparatus when a PD occurs. One of the noise typologies that affects PD patterns is called the cross-talk, that is, discharges that appear in the measured point due to coupling from another point. In order to study the cross-talk signal, the two PD measuring circuits which are circuit no.1 and circuit no.2, on the same grounding system in the HV laboratory were performed. The distance between two PD measuring circuits is about 20 meters. The artificial PD models were utilized to generate the PD signal injecting to the test circuit no.1, while the cross-talk signals were investigated in circuit no.2. The HFCT sensors were used to measure the PD signal in circuit 1 and the cross-talk signal in circuit 2. The measurement results found that the cross-talk signal transmission in grounding systems provides a bipolar PRPD pattern, and the pulse waveform shows an oscillation shape. Moreover, onsite PD measurements of the generator steam turbine were studied. It is noticeable that the knowledge about cross-talk phenomena in grounding systems can help the PD verification and location for online PD measurement. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of noise signals on partial discharge classification models(2015-01-26); Nimsanong, PhethaiThis document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated and measured in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF, and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of experimented data was used as a training data for the PD classification model. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. Besides, noise signals were generated with computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed KNN, PNN, PCA-KNN and PCA - PNN model can predict PD patterns without noise signal with the accuracy 100%. Noise signals with amplitude of 20% or more of peak value of PD signal have obviously influence the accuracy of PD pattern classification. The combination of PCA with KNN model can improve the ability of PD classification compared with KNN PD classification model. However, it seems that PCA provided negative effect when PCA was combined with PNN model and evaluated with the mixed-noise PD signals. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Partial discharge classification using principal component analysis combined with self-organizing map(2015-01-26); Nimsanong, PhethaiThis document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with self-organizing map (SOM) for a classification purpose, so called, PCA-SOM model compared with SOM model to classify partial discharge pattern (PD) into four categories listed as corona at high voltage side, corona at low voltage side, surface discharge, and internal discharge. PD signals were investigated by using ultra high frequency (UHF) measurement technique. 12 independent parameters, skewness and kurtosis of each period of the measured electromagnetic signal, were calculated. 80 experiments in total were performed. PCA-SOM PD classification model was constructed. Then, 60% of the experimented data was used as a training data for the PD classification model. Another 40% experimented data was utilized to evaluate the performance of the designed PD classification model. Besides, noise signals were generated with a computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed SOM model and PCA-SOM model can predict PD patterns without noise signal with the accuracy 100% of classification. The prediction ability of SOM for PD classification models decreased sharply when this model was tested by the mixed-PD signals with the noise level of 30% of the peak value of the PD signal. Whereas the PCA-SOM provided some degree accuracy reducing for PD pattern classification when it was verified with such data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Bandwidth Verification of VHF Antenna and Apply for Partial Discharge Measurement(2022-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dielectric Response Analysis of Mineral Oil Immersed Transformer Insulation during Manufacturing Process(2019-06-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Partial discharge classification using learning vector quantization network model(2015-01-26); Nimsanong, PhethaiThis paper represents a partial discharge (PD) classification technique by using learning vector quantization (LVQ) network model. LVQ model was not only implemented with the PD test data but also combined with the principal component analysis (PCA), so called, PCA-LVQ for a data reduction. In this research work, both LVQ model and PCA-LVQ model were investigated. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models both LVQ and PCA-LVQ models. Besides, noise signals were generated with computer program for testing the efficiency of these models. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed LVQ model can predict PD patterns without noise signal with the accuracy 100% whereas the PCA-LVQ model provided more than 87.5% accuracy of PD classification. The prediction ability of LVQ PD classification models decreased sharply especially for CHV when this model was tested by the mixed noise-PD signals. Whereas the prediction accuracy of PCA-LVQ model was more robust to the noise signals than LVQ model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of training methods on the accuracy of PCA-KNN partial discharge classification model(2015-01-26); Nimsanong, PhethaiThe aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.