Chaotic analysis of partial discharge (CAPD) as a novel approach to investigate insulation degradation caused by the various defects

2002 ◽  
Author(s):  
Yunsok Lim ◽  
Jeonseon Lee ◽  
Wonjong Kang ◽  
Jayoon Koo
Keyword(s):  
Partial Discharge ◽  
Novel Approach ◽  
Insulation Degradation

scholarly journals Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
S. Venkatesh ◽  
S. Gopal ◽  
K. Kannan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Clustering Algorithms ◽  
Research Work ◽  
Experimental Studies ◽  
Multiple Sources ◽  
Graph Theoretic ◽  
Large Sets ◽  
Novel Approach

Partial discharge (PD) is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

Design of Diagnosis System for Insulation Degradation by Using Neurofuzzy Model

2000 ◽  
Vol 4 (5) ◽  
pp. 368-372
Author(s):  
Yigon Kim ◽  
◽  
Yang Hee Jung ◽  
Yong Chul Bae
Keyword(s):  
Numerical Simulation ◽  
Early Warning ◽  
Partial Discharge ◽  
Data Acquisition System ◽  
Electrical Equipment ◽  
New Method ◽  
Insulation Aging ◽  
Insulation Degradation ◽  
On Line ◽  
Data Design

Insulation aging diagnosis provides early warning of electrical equipment defects that helps avoid loss from unexpected production line shutdown. Since relations of insulation aging and partial discharge dynamics are nonlinear, it is very difficult to provide early warning in electrical equipment. This paper suggests a new method for diagnosing insulation aging that measures partial discharge on-line from DAS(Data Acquisition System) and acquires 2D patterns from analyzing it using wavelets. Using this data, design of a neurofuzzy model that diagnoses electrical equipment is investigated. Validity of the new method is confirmed by numerical simulation.

Assessment of Insulation Degradation in Rotating Machines

2006 ◽  
Vol 321-323 ◽  
pp. 582-585
Author(s):  
Hee Dong Kim
Keyword(s):  
Nondestructive Test ◽  
Partial Discharge ◽  
Dissipation Factor ◽  
Stator Winding ◽  
Current Increase ◽  
Increase Rate ◽  
Insulation Degradation ◽  
Ac Current ◽  
Current Dissipation ◽  
Winding Insulation

To assess the degradation of stator winding insulation in high voltage motor, a total of forty-eight coils were manufactured. These coils are classified into five groups and there are twelve strands in each coil. Four coil groups are artificial defects such as shorted turn of strand, internal separation between conductor and insulation, large void within insulation and removal of semi-conductive tape. The other coil group is normal. Before ac breakdown test was performed, nondestructive test that included ac current, dissipation factor and partial discharge (PD) was done with five coil groups. This paper carried out by comparing the results between nondestructive test and ac breakdown test. When the values of ac current increase rate, tip-up dissipation factor and PD magnitude are large, the insulation is occurred the deterioration. AC breakdown voltage is also low in degraded insulation.

Typical PD Type of GIS, Transformer, Switchgear and other Substation Equipment

2014 ◽  
Vol 1044-1045 ◽  
pp. 893-897
Author(s):  
Wei Dong Zhang ◽  
Qing Liu ◽  
Nan He ◽  
Jian Wen Wang
Keyword(s):  
High Voltage ◽  
Partial Discharge ◽  
Free Particle ◽  
Electrical Equipment ◽  
Surface Discharge ◽  
Spike Discharge ◽  
Insulation Degradation ◽  
Substation Equipment ◽  
Insulation Breakdown

Most high-voltage faults are caused by the insulation degradation of electrical equipment, while partial discharge is the main reason of insulation breakdown of electrical equipment. This paper introduces partial discharge types of GIS, transformer, switchgear and other substation equipment based on the study of partial discharge types and summarizes five types of partial discharge of substation equipment, namely: spike discharge, free particle discharge, insulation defects discharge, surface discharge and suspended click discharge.

Pattern Recognition for Partial Discharge in Power Transformer Based on Probabilistic Neural Networks

2013 ◽  
Vol 694-697 ◽  
pp. 2710-2714 ◽  
Author(s):  
Hai Kun Shang ◽  
Jin Sha Yuan ◽  
Yu Wang ◽  
Song Jin
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Power Transformer ◽  
Partial Discharge ◽  
Classification Performance ◽  
Characteristic Parameters ◽  
Probabilistic Neural Networks ◽  
Minimum Risk ◽  
Insulation Degradation ◽  
Transformer Fault

Partial discharge diagnosis plays an important role in condition monitoring of power transformer. Different discharge types cause varying degrees of effects on insulation degradation. Therefore pattern recognition for partial discharge is necessary for power transformer fault diagnosis. Probabilistic Neural Networks (PNN) is based on the theory of Bayesian minimum risk which perfectly fits for classification. In this paper PNN is employed for pattern recognition of partial discharge. 18 characteristic parameters are extracted from UHF partial discharge signals as the inputs of the PNN classifier. Results show that the PNN method gives fast and accurate classification performance.

scholarly journals A Novel Approach for Partial Discharge Measurements on GIS Using HFCT Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4482 ◽  
Author(s):  
Armando Rodrigo Mor ◽  
Luis Castro Heredia ◽  
Fabio Muñoz
Keyword(s):  
High Frequency ◽  
Partial Discharge ◽  
Measuring System ◽  
Very High Frequency ◽  
Radiated Energy ◽  
Novel Approach ◽  
Potential Applicability ◽  
Potential Benefits ◽  
Moving Particle ◽  
The Impact

This paper presents a novel measuring system for partial discharge (PD) measurements in Gas Insulated Systems (GIS) using high frequency current transformers (HFCT). The system is based on the measurement of the induced PD currents in the GIS enclosure. In opposition to the existing antenna technologies that measure the radiated energy in the very high frequency/ultra-high frequency (VHF/UHF) range, the proposed system measures the PD conducted currents in the high frequency (HF) range and below. The foundation of the measurements together with a detailed explanation of the sensor installed conveniently at the bolts of the GIS spacer are presented. An experimental study on the current distribution in the GIS enclosure is described to evaluate the impact of the sensor on the measurements. Laboratory experiments have been performed that show the suitability of this method to properly measure particle discharges caused by corona, surface and free moving particle discharges in SF6. Discharges in the range of 1 to 4 pC have been properly measured. An analysis to evaluate the performance of the method is shown, in comparison to VHF/UHF antenna measurements. The potential benefits of this novel technique rely on the small attenuation of PD signals in the GIS components in the HF range and sample rate reductions. Finally, a discussion on the potential applicability of present cluster and charge calculation techniques to the proposed PD GIS measurement using HFCT is presented.

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