A neuro-fuzzy approach for the detection of partial discharge (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.
Wavelet Transform Analysis of Partial Discharge Signals
Partial discharge (PD) signals are short transient pulses which occur randomly. For such non-stationary signals, the wavelet transform (WT) is more suitable than the traditional Fourier transform (FT) as it provides information in both time and frequency domains. This paper investigates the WT technique as applied to PD signals, with particular reference to discharge measurements in high-voltage power cables. Laboratory tests were carried out to obtain the signal characteristics of various insulation defects and compared with on-site measurement results. Also it is shown that the WT method can be used to suppress noise and improve the time resolution of the PD pulse signal.
DYNA, 2017
Este artículo presenta dos enfoques de reconocimiento de patrones usando huellas dactilares de descargas parciales como características de entrada para llevar a cabo la clasificación de patrones de DP. Un perceptrón multicapa (MLP) basado en el algoritmo de propagación hacia atrás y una máquina de soporte vectorial fueron entrenados para reconocer tres tipos de patrones de DP. Los resultados experimentales demostraron que los algoritmos pueden arrojar altas tasas de reconocimiento. De otra parte, la trasformada wavelet discreta (DWT) fue utilizada para eliminar el nivel de ruido presente en las DP como una etapa previa al proceso de clasificación. Diferentes wavelets madre fueron probadas a diferentes niveles de descomposición con el objeto de encontrar parámetros wavelet apropiados para obtener una mejor relación señal-ruido (SNR) y menos distorsión después del proceso de filtrado.
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.
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.
Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses
IEEE Transactions on Dielectrics and Electrical Insulation, 2010
In this work a new approach based on cross-wavelet transform towards identification of noisy Partial Discharge (PD) patterns has been proposed. Different partial discharge patterns are recorded from the various samples prepared with known defects. A novel cross-wavelet transform based technique is used for feature extraction from raw noisy partial discharge signals. Noise is a significant problem in PD detection. The proposed method eliminates the requirement of denoising prior to processing and therefore it can be used to develop an automated and intelligent PD detector that requires minimal human expertise during its operation and analysis. A rough-set theory (RST) based classifier is used to classify the extracted features. Results show that the partial discharge patterns can be classified properly from the noisy waveforms. The effectiveness of the feature extraction methodology has also been verified with two other commonly used classification techniques: Artificial Neural Network (ANN) based classifier and Fuzzy classifier. It is found that the type of defect within insulation can be classified efficiently with the features extracted from cross-wavelet spectra of PD waveforms by all of these methods with a reasonable degree of accuracy.
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.