Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources (original) (raw)
Related papers
Energies, 2016
In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.
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.
Partial Discharge Pattern Classification Using the Fuzzy Decision Tree Approach
IEEE Transactions on Instrumentation and Measurement, 2005
Partial discharge (PD) measurement is a proven flaw detection technique for finding cavities that are defects in the insulating material. In this paper, a novel approach for the classification of cavity sizes, based on their maximum PD charge transfer-applied voltage (1Q-V) characteristics using a fuzzy decision tree system, is proposed. The (1Q-V) partial discharge patterns for different cavity sizes are represented by features extracted from their pulse shapes, and the classification rules are directly extracted from the data using the decision tree. The decision rules obtained from the decision tree are then converted to the fuzzy IF-then rules, and the back-propagation algorithm is utilized to tune the parameters of the membership functions employed in the fuzzy classifier. The neuro-fuzzy classification technique is shown to provide successful classification of void sizes in an easily interpretive fashion.
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.
2020
Incomplete release (PD) designs are critical instrument for the finding of high voltage (HV) protection frameworks. Human specialists can find conceivable protection absconds in different portrayals of the PD information. One of the most broadly utilized portrayals is stage settled PD (PRPD) designs. So as to guarantee dependable activity of HV hardware, it is vital to relate the noticeable measurable attributes of PDs to the properties of the imperfection and at last to decide the kind of the deformity. 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.
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.
A neuro-fuzzy approach for the detection of partial discharge
IEEE Transactions on Instrumentation and Measurement, 2001
Dielectric surfaces exposed to partial discharges (PD) undergo aging, which is reflected by changes in the discharge pulse form. An approach is described in which fuzzy logic and neural networks are used in conjunction with the wavelet transform to identify the parameters in the PD pulse form for the purpose of classifying the aging phenomena due to partial discharge degradation.
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.
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).
Pattern Recognition Of Partial Discharge By Using Simplified Fuzzy Artmap
2010
This paper presents the effectiveness of artificial intelligent technique to apply for pattern recognition and classification of Partial Discharge (PD). Characteristics of PD signal for pattern recognition and classification are computed from the relation of the voltage phase angle, the discharge magnitude and the repeated existing of partial discharges by using statistical and fractal methods. The simplified fuzzy ARTMAP (SFAM) is used for pattern recognition and classification as artificial intelligent technique. PDs quantities, 13 parameters from statistical method and fractal method results, are inputted to Simplified Fuzzy ARTMAP to train system for pattern recognition and classification. The results confirm the effectiveness of purpose technique.