Research on Partial Discharge Signal Detection Based on UHF Sensor Array and Eigenvalue Behavior Analysis

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.

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.

The most common pre-breakdown phenomenon in high voltage (HV) equipment is partial discharge (PD). The presence of PD activity is a warning indicator that the insulation deterioration would worsen over time, leading to catastrophic failure if the associated defects are not addressed early. Ultra-high frequency (UHF) has been proposed as a non-conventional method for detecting PD because of its resilience to external noise, high sensitivity, and online measurement potential. The UHF detection approach uses sensors to detect electromagnetic waves emitted by the PD source. The sensor is thus one of the most important parts of UHF PD detection. Microstrip patch antenna is widely used as a UHF sensor due to its merits of simple structure, compact profile, low cost, and ease of fabrication. This project deals with the development of the microstrip antenna for PD detection. To evaluate the antennas' performance, five antennas are built and simulated using Computer Simulation Technology (CST) Microwave Studio software. Return loss, voltage standing wave ratio (VSWR), and surface current are among the simulated results. These five antennas operate in the range of at least 80% of UHF and have the acceptable size that can be installed on the dielectric window or mounted on the flange.

The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.

To improve the reliability of partial discharge (PD) monitoring in smart grid equipment, this paper proposes a hybrid denoising method for ultrahigh frequency (UHF) PD signals based on wavelet decomposition and singular value decomposition (SVD). First, the original UHF signal is decomposed using a five-level discrete wavelet transform (DWT) with the db4 wavelet. The Hilbert transform and moving average smoothing are then applied to the detail component with the highest energy to extract the signal envelope and accurately locate the PD event. Next, SVD is used to denoise the identified PD segment, with K-means clustering applied to classify singular values and determine the optimal number to retain. Compared with conventional methods such as DWT, empirical mode decomposition (EMD), and SVD-only approaches, the proposed method achieves a higher noise suppression ratio (NSR) while preserving the signal’s key features and spectral details. Experimental results from a 110 kV GIS field test demonstrate the method’s effectiveness in suppressing noise and enhancing PD signal clarity, making it well suited for high-fidelity diagnostics in intelligent power equipment monitoring systems.

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.

GIS (Gas insulated switching gear) is the power equipment with excellent dielectric strength and with economical value in high confidence and stability. Recently the reliability of GIS equipment has been questioned for its long usage, and the importance of the partial discharge on-line diagnosis system has been recognized. A partial discharge (PD) detection system is an effective means for the monitoring and evaluating the dielectric condition of Gas Insulated switchgear (GIS). The ultra-high-frequency (UHF) PD detection technique can detect and locate PD sources from the inside of GIS by detecting electromagnetic wave emitted from the PD source. Therefore, a real-time diagnostic system using UHF detection method was developed for this application. However, the signal of partial discharge occurring in SF6 gas is very weak and susceptible to external noises which mainly consist of PD in the air. Thus, it is very important to distinguish signal from the external noise signals. Unfortunately, these external noise signals and the partial discharge signals have very similar characteristics. For this reason, the external noise signal is not easy to remove. Therefore to solve this problem, we need the signal processing method to distinguish partial discharge signals from the external noise signals for the improvement of signal to noise ratio (SNR) and sensitivity. In this paper, we proposed the internal signal processing method to remove the external noise signals with built-in preamplifier and frequency conversion circuit.

Denoising is a crucial step in the localization of partial discharge (PD) in a substation. In this paper, a novel multi-resolution generalized S-transform (GST) denoising algorithm for precise localization of PD in substations is proposed. The algorithm denoises the PD signal received by the ultra-high frequency (UHF) sensor in two steps. First, the GST with the high frequency resolution is used to analyze the PD signal, and a GST filter is designed to filter out periodic narrowband noises. Then, the S-transform (a particular case of GST) is applied to analyze the PD signal, and the white Gaussian noise is suppressed according to the statistical characteristic difference between the noise and effective signal. Finally, the denoised PD signal is obtained and applied to PD localization. The simulations results show that the proposed algorithm can effectively suppress noise and can extract accurate time delay data from the denoised PD signal. The experiment results show that, compared with the wavelet transform, the localization error of the proposed algorithm is the smallest, which is 1.59m. The proposed algorithm can realize the precise localization of PD.

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.

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.

Time delay estimation is the basis of the localization algorithm for partial discharge (PD) based on time difference in GIS, and it is the key to the accuracy of localization. In this paper, the theory of fractional Hilbert transform for time delay estimation is described. Based on fractional Hilbert transform, a method for estimating the time delay of GIS ultra high frequency partial discharge is proposed, and the numerical implementation steps of the algorithm are given. According to the abnormal partial discharge signals found in the switch gate during the inspection of certain 220kV GIS equipment, high frequency electromagnetic wave is collected by the high speed oscilloscope. According to the time delay of the signal, the space position of the partial discharge source is located from the the disk type insulator pouring hole 1.25 meters. After the disintegration of the switch air chamber, the distance between the discharge source and the pouring hole is 1.3 meters. The error of the partial discharge source localization based on fractional Hilbert algorithm is significantly lower than that of the bi-spectrum and the frequency domain time delay estimation algorithm.

Partial discharge (PD) in insulators will not only lead to the gradual degradation of insulation performance but even cause power system failure in serious cases. Because there is strong noise interference in the field, it is difficult to accurately locate the position of the PD source. Therefore, this paper proposes a three-dimensional spatial localization method of the PD source with a four-element ultra-high-frequency (UHF) array based on improved wavelet packet dynamic threshold denoising and the generalized cross-correlation algorithm. Firstly, considering the field noise interference, the PD signal is decomposed into sub-signals with different frequency bands by the wavelet packet, and the corresponding wavelet packet coefficients are extracted. By using the improved threshold function to process the wavelet packet coefficients, the PD signal with low distortion rate and high signal-to-noise ratio (SNR) is reconstructed. Secondly, in order to solve the problem that the amplitude of the first wave of the PD signal is small and the SNR is low, an improved weighting function, , is proposed, which is based on the self-power spectral density of the signal and is adjusted by introducing an exponential factor to improve the accuracy of the first wave arrival time and time difference calculation. Finally, the influence of different sensor array shapes and PD source positions on the localization results is analyzed, and a reasonable arrangement scheme is found. In order to verify the performance of the proposed method, simulation and experimental analysis are carried out. The results show that the improved wavelet packet denoising algorithm can effectively realize the separation of PD signal and noise and improve the SNR of the localization signal with low distortion rate. The improved weighting function significantly improves the estimation accuracy of the time difference between UHF sensors. With the sensor array designed in this paper, the relative localization error is 3.46%, and the absolute error is within 6 cm, which meets the requirements of engineering applications.

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.

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.