Signal Recognition Method of Power Cable Oscillating Wave Partial Discharge Detection Based on Support Vector Machine

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

The analysis of partial discharge (PD) signals has been identified as a standardized diagnostic tool in monitoring the condition of different electrical apparatus nowadays. In this paper, we propose a novel data-driven approach to detect PD pulses in power cables using one-dimensional convolutional neural networks (CNNs), a successful deep neural network approach. Applying this deep learning method, an end-to-end framework has been proposed considering the propagations of PD signals and noises in power cables. The proposed method uses PD pulses as input, automatically extracts meaningful features for waveforms of PD pulses, and finally detects PD. Most of the existing methods, which use traditional classifiers, such as support vector machines (SVMs) and multi-layer perceptron (MLP) have mainly focused on improving feature representation and extraction manually for this task. However, the proposed CNN-based detection algorithm captures important latent features for waveforms of PD pulses with the help of its automatic feature extraction capability from raw inputs. Our experimental results show that the proposed method is better than conventional SVMs and achieves 97.38% and 93.23% detection accuracies in end-to-end settings based on our theoretical model-generated and empirical real-world PD signals, respectively.

Today the sensitive measurement of partial discharge (PD) signals in XLPE cable can be an important element for quality assurance and diagnostics in the factory and during the on-site test procedure. The results clearly indicate that PD analysis of XLPE cable is an excellent online tool to assess its dielectric condition. However, as the serious interference in-field, the acquired PD signals may be interrupted by periodic interferences and white noises, easily. This paper describes an intelligent morphological filter algorithm to remove the periodic pulse interference and white noise contained in the PD signals. The algorithm consists of two stages of morphological processing. The performance of the algorithm is evaluated with simulated and real PD signals. Compared with classical wavelet, there are substantial improvements in the indexes of average error, root mean square (rms) error and maximum error, especially in reducing distortion of the baseline waveform.

The feature extraction and pattern recognition techniques are of great importance to assess the insulation condition of gas‐insulated switchgear. In this work, the ultra‐high‐frequency partial discharge (PD) signals generated from four types of typical insulation defects are analysed using S‐transform, and the greyscale image in time‐frequency representation is divided into five regions according to the cutoff frequencies of TEm1 modes. Then, the three low‐order moments of every subregion are extracted and the feature selection is performed based on the J criterion. To confirm the effectiveness of selected moment features after considering the electromagnetic modes, the support vector machine, k‐nearest neighbour and particle swarm‐optimised extreme learning machine (ELM) are utilised to classify the type of PD, and they achieve the recognition accuracies of 92, 88.5 and 95%, respectively. In addition, the results show that the ELM offers good generalisation performance at the fastest learning and testing speeds, thus more suitable for a real‐time PD detection.

Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN.

Partial Discharge (PD) detection in High Voltage (HV) equipment is an essential technique for assessing the insulation condition in the power system apparatus. The presence of PD is one of the critical indications for the defect and deterioration of the insulating material. Effective signal processing techniques are required to acquire, analyse, and classify PD signals into a specific fault type. The PD measurement device is prone to be affected by various noise sources, which leads to misinterpretation of the PD signal. This research focuses on developing a suitable algorithm for denoising PD signals and proposes a novel feature extraction process for classifying PD signals. The signal decomposition-based denoising algorithms in various engineering disciplines have become popular, and such techniques are being applied to PD signals. A novel PD signal denoising using adaptive decomposition, optimized by entropy and statistical parameters is proposed. Furthermore, a novel entropy-driven PD signal classification method is proposed. Initially, the PD signal is decomposed using various adaptive decomposition methods to get Intrinsic Mode Functions (IMFs). Then, IMFs are analysed using statistical or information theory measures such as Kurtosis, Mutual Information (MI) and Dispersion Entropy (DE). The IMFs are classified as noise, noise–dominant and signal IMFs, based on Kurtosis and MI values. The DE value of IMFs is used to select the regularisation parameter of Group-Sparse Total Variation (GSTV). The noise–dominant IMFs are subjected to the GSTV method, and the signal IMFs are added to the output vector to reconstruct the denoised output. The noisy IMFs are discarded for further processing. The proposed methods are applied under different input signal-noise ratios, lengths, and noise sources. The proposed denoising methods with the results and findings are presented and discussed, along with the performance metrics related to the filters. In addition, Tsallis entropy and a novel Generalized Gaussian Distribution – Refined Composite Multiscale Dispersion Entropy (GGD–RCMDE) based features are proposed for the time series signal. The suggested feature extraction methods are applied to the PD dataset to extract the features from various PD signals such as ‘corona’, ‘surface’ and ‘void’. A novel preprocessing method followed by the min-redundancy max-relevance (mRMR) feature selection method is applied to select sensitive features. A multiclass classifier model is trained using the most sensitive features to classify PD faults. The robust experimental analysis of the feature extraction method was carried out and reported. The performance metrics are computed using the results obtained through the experiments. The effectiveness of the proposed denoising and classification methods was presented with a suitable discussion and conclusion.

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation‐based thresholding (ECBT), is introduced. The proposed thresholding function is capable of conserving a significant portion of the original signal energy, while the threshold value is determined based on the relative difference between the original and noisy signal energies. The proposed method is first applied to PD signals contaminated with different levels of simulated noise. Results show that ECBT produces a denoised PD signal with higher signal‐to‐noise ratio (SNR) and less distortion than PDs produced by the existing wavelet methods. Then, ECBT is modified to address actual PD signals corrupted with real noise, where a robust SNR estimation method is derived to estimate the noise level embedded in the measured PD signals. The denoised PD signals indicate that the proposed method yields higher reduction in noise levels than other methods.

Aiming at the shortcomings of traditional hard threshold method and soft threshold method, this paper puts forward a wavelet denoising method based on wavelet entropy and improved threshold function. First, the noisy partial discharge (PD) signals are processed by wavelet decomposition, then the wavelet coefficients are processed by a new improved threshold function using adaptive selection of threshold based on wavelet entropy, finally the denoised signal can be got by reconstruction. The denoising results of typical simulation signals and the field signals reveal that this present method can effectively remove white noise.

Partial discharge (PD) detection is an important method to verify the reliability of the insulation structure of high voltage power electronics devices. Although there are many researches on PD detection inside power modules, they mainly focus on traditional electrical detection methods and 50 Hz working conditions, which do not meet the repetitive unipolar impulsive voltage conditions of power modules, and traditional methods cannot solve the electromagnetic interference (EMI) problem under working conditions. This paper proposes a novel approach to PD detection in power modules. This approach designs an optic fiber sensing (OFS) system based on optic theory, and conducts the detailed mathematical theoretical deduction. In addition, A partial discharge inception voltage (PDIV) signal processing method based on PD acoustic signal is proposed. Finally, the proposed approach is applied to a power module. The PD signal and PDIV is successfully captured without EMI interference and new insulation problems.

Partial discharge (PD) signals generated by defects in a transformer insulation can be captured through measurement instruments and they may be used, after preprocessing, to discriminate the PD Sources. Some of the artificial defect models, such as: corona, internal cavity and surface discharge in air are developed in the laboratory. These defect models are put in parallel under a high voltage stress. The PD signals stemmed from these sets of multiple PD sources are captured. In this paper six bandpass Alters (with two MPD 600 devices) are used for feature extraction of these signals. For PD signals discrimination, the Density-Based Spatial Clustering of Applications with Noise density (DBSCAN) method is employed with two freedom parameters which resulted in an accurate discrimination of types of partial discharge sources.

Partial Discharge (PD) in high-voltage electrical equipment can cause insulation deterioration and is one of the causes of major accidents in electrical equipment. Therefore, PD detection of electrical equipment is necessary for electrical safety production operations. In the PD detection of power equipment, since the acquisition system is often collected online, the signal received by the system includes a large number of random noise signals in addition to the PD signal, which makes it important to detect and distinguish the PD signals from the noise signals. Therefore, this paper proposes a method for detecting Ultra High Frequency (UHF) PD signals from random noise based on the eigenvalue behavior analysis of the PD signal covariance matrix and UHF sensor array. The experimental results show that the pure noise average discriminant value is 1.3. When there is PD in space, the mean value of discriminant values is 16.8, significantly higher than the set threshold value of 1.5, which verifies the effectiveness of the proposed method and provides theoretical basis for subsequent denoising processing of PD signals.

This article aims to study the pattern of partial discharge (PD) signals occurring on the insulators of high-voltage systems. A mother wavelet comparison and wavelet decomposition are presented to detect and locate PD signals by dividing them into three processes: 1) Signal test generation, employing RC and RLC impedance circuits whereby the output voltage pulses in the RC impedance circuit are expressed as damped exponential pulses (DEPs) and those of the RLC as damped oscillatory pulses (DOPs). White Gaussian noise (AWGN) is then added, which mimics the effect of many random processes in measurement systems. The concept involves applying noise to the original signal and removing it with wavelet transform using various wavelet templates such as Daubechies, Coiflet, Symlet, and biorthogonal to separate the signal components. The experiment results are then compared, and a performance evaluation performed using mean square error (MSE) as in the first two signals. 2) DEPs and DOPs are added with a sine wave to simulate a virtual measurement from a measuring instrument according to the superposition principle using a band-pass filter with the frequency range specified by the two elements to determine the frequency of the resulting PD. 3). The results of the PD signal experiments developed in the laboratory are also evaluated for efficiency by subjective measurement by a PD signal specialist. Therefore, partial discharge denoizing is evaluated using the mother wavelet as a preprocessing step for feature extraction and classification.

In on-line PD measurement, de-noising Partial Discharge (PD) signal from noisy environment with acceptable performance indices and also with minimum time cost is a major challenge. In this paper a new approach based on wavelet thresholding is proposed for de-noising on-line PD signal. This method first applied on two different type PD signals with White Gaussian Noise (WGN) and simulation results shows that new threshold function give better performance with less computing time as compare to other existing de-noising technique. Finally, a field detected signal is tested and the de-noised signal indicates that method is effective and efficient for this application also.

The joint time-frequency analysis has been recently applied to PD signal extraction. Its advantage is widely considered to be the capability of analyzing signals in time and frequency domains at multiple scales. This paper presents a study on recent advances in the application of joint time-frequency analysis techniques to extract PD signal from noise. The theoretical foundation and implementation of two representative joint time-frequency analysis techniques, namely discrete wavelet transform (DWT) and empirical mode decomposition (EMD) are detailed. Case studies using signals taken from PD measurements are presented.

In order to explore the mixing characteristics of ultra-high frequency (UHF) partial discharge (PD) signals in GIS, simulation tools and Antenna Theory concerning the electromagnetic field (XFDTD) have been adopted in investigating the mixed features of UHF signals in GIS environment. Simulation results and theoretical analysis in this paper demonstrate that convolution is the basic relationship between PD current signal and the single UHF PD signal; the PD mixing signals are the linear and instantaneous mixture of single PD signals and they are also the linear and convolution mixture of PD current signals. The reverse process of those mixing modes are also been discussed based on blind sources separation and find out that it is an over complete blind source separation problem to gain single UHF PD signal from mixed one. Moreover, it can be approximated to conventional blind source separation when the layout of ultra-high frequency sensors is compact.