Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment (original) (raw)
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Wavelet Analysis and Classification for Partial Discharges
This paper presents a new method for insulating breakdown detection. The main idea consists in conceiving a general method, adaptable to all sort of insulating material subject to partial discharges (PD) phenomena. The first step is the analysis of the available recorded experimental PD parameters. The obtained information is correlated with the results of the wavelet transform decomposition method. Subsequently, interpreting the results computed on the experimental data set helps to decide whether and where the method should be employed, and to establish the premises for a further functioning states classification method, which will be used within a decision algorithm.
Neural Network Based Recognition of Partial Discharge Pattern
Partial Discharge (PD) monitoring and analysis has become imperative for utilities as well as for equipment manufacturers as it causes deterioration of insulation systems in high voltage (HV) electrical equipment. The analysis of PD includes detection, recognition & classification of PD using various advanced mathematical tools & techniques. In the artificial intelligence, Neural network methodology is one of the most popular and widely used for the analysis of PD. This work represents the generation of the partial discharge like signal using the MATLAB 7.9 software and the recognition of generated signals by artificial neural network technique. The obtained PD pattern represents the characteristics of Partial discharge signal and the discrete spectrum interference signal with it. The variants of these signals are taken as samples for the training of the neural network. The offline recognition of the PD signal has been done.
Partial Discharge Classification Using Neural Networks and Statistical Parameters
Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted from the raw 3-D PD patterns are statistically analyzed for the neural-network-based (NN-based) classification system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the classification ability is investigated on 120 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results.
Auto-detection of partial discharges in power cables by discrete wavelet transform
Electrical Engineering in Japan, 2005
One of the serious problems that can occur in power XLPE cables is destruction of insulator. The best and conventional way to prevent this is ascertaining partial corona discharges occurring at small voids in organic insulators. However, there are some difficulties in detecting those partial discharges due to the existence of external noise in detected data, whose patterns are hardly identified at a glance. For this reason, there have been a number of researches into detecting partial discharges by employing a neural network (NN) system, which is widely known as a system for pattern recognition. We have been developing an NN system for auto-detection of partial discharges, and have input numerical data of the waveform itself and obtained appropriate performance. In this paper, we employed the discrete wavelet transform (DWT) to acquire more detailed transformed data in order to use them in the NN system. Employing the DWT, we were able to express the waveform data in timefrequency space, and achieved effective detection of partial discharges by the NN system. We present herein the results using DWT analysis for partial discharges and noise signals which we obtained. Moreover, we present results out of the NN system which dealt with those transformed data.
IJERT-Partial Discharge Pattern Recognition of HV GIS by using Artificial Neural Networks
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/partial-discharge-pattern-recognition-of-hv-gis-by-using-artificial-neural-networks https://www.ijert.org/research/partial-discharge-pattern-recognition-of-hv-gis-by-using-artificial-neural-networks-IJERTV3IS110433.pdf Partial Discharge (PD) monitoring and analysis has become imperative for utilities as well as for equipment manufacturers as it causes deterioration of insulation systems in high voltage (HV) electrical equipment. The analysis of PD includes detection, recognition & classification of PD using various advanced mathematical tools & techniques. In the artificial intelligence, neural network methodology is one of the most popular and widely used for the analysis of PD. This work represents the generation of the partial discharge like signal using the MATLAB software and the recognition of generated signals by artificial neural network technique. The obtained PD pattern represents the characteristics of Partial discharge signal and the discrete spectrum interference signal with it. The variants of these signals are taken as samples for the training of the neural network. The PD recognition system works as an expert PD recognition software tool for identifying the type of defect that causing the Partial discharge during testing and service conditions. The expert system will reduce the time in finding out the root cause in the event of testing and in the service it will reduce the time to repair and keep GIS back into normal service conditions. Keywords-Partial discharge(PD), Gas insulated switchgear (GIS), neural network(NN), pattern recognition, phase resolved partial disharge (PRPD).
Wavelet transform processing applied to partial discharge evaluation
Journal of Physics: Conference Series, 2012
Partial Discharge (PD) is characterized by high frequency current pulses that occur in high voltage (HV) electrical equipments originated from gas ionization process when damaged insulation is submitted to high values of electric field [1]. PD monitoring is a useful method of assessing the aging degree of the insulation, manufacturing defects or chemical/mechanical damage. Many sources of noise (e.g. radio transmissions, commutator noise from rotating machines, power electronics switching circuits, corona discharge, etc.) can directly affect the PD estimation. Among the many mathematical techniques that can be applied to de-noise PD signals, the wavelet transform is one of the most powerful. It can simultaneously supply information about the pulse occurrence, time and pulse spectrum, and also de-noise in-field measured PD signals. In this paper is described the application of wavelet transform in the suppression of the main types of noise that can affect the observation and analysis of PD signals in high voltage apparatus. In addition, is presented a study that indicates the appropriated mother-wavelet for this application based on the cross-correlation factor.
IEEJ Transactions on Electrical and Electronic Engineering, 2016
In high‐voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To inv...
Analysis of Partial Discharge Source Using Artificial Neural Network
2020
Partial Discharge (PD) patterns are an important tool for the diagnosis of High Voltage (HV) insulation systems. Human experts can discover possible insulation defects in various representations of the PD data. One of the most widely used representations is phase-resolved PD (PRPD) patterns. In order to ensure reliable operation of HV equipment, it is vital to relate the observable statistical characteristics of PDs to the properties of the defect and ultimately to determine the type of the defect. In this work, we have detected PD source using Artificial Neural Network (ANN) tool in Matlab software.
International journal of engineering research and technology, 2019
Partial discharge (PD) patterns are significant tool for the diagnosis of high voltage (HV) insulation systems. Human experts can discover possible insulation defects in various representations of the PD data. One of the most widely used representations is phase-resolved PD (PRPD) patterns. In order to ensure reliable operation of HV equipment, it is crucial to relate the observable statistical characteristics of PDs to the properties of the defect and ultimately to determine the type of the defect. In present work, we have obtained and analysed combined use of PRPD patterns (φ-q), (φ-n) and (n-q) using statistical parameters such as skewness and kurtosis for two patterns viz. (φ-q) and (φ-n) along with mean, standard deviation, variance, skewness and kurtosis for (n-q) to detect type of PD and we have verified the obtained results by providing obtained statistical parameters as an input for training of artificial neural network (ANN) in MATLAB tools.
Journal of Control, Automation and Electrical Systems, 2018
This paper presents a statistical learning method capable of classifying the incidence level of partial discharges in power transformers. By using the results from acoustic emission measurements, it is possible to detect the presence of partial discharges inside the equipment, allowing the qualitative health monitoring of the transformer's insulation. Therefore, the use of a Bayesian Network is proposed, combined with a Compact Genetic Algorithm tailored for solving mixed integer programming problems, for discretization of the continuous metrics extracted from acoustic emission measurement. Comparing the results with Multilayer Perceptron Neural Network and Decision Tree and after a suitable amount of runs of the algorithm, it was verified that the Bayesian Networks presented superior results.