Partial Discharge Signals Research Articles

Partial Discharge Location Algorithm Based on Total Least-Squares With Matérn Kernel in Cable Systems

Partial discharge (PD) location techniques are a useful tool for condition monitoring of electrical apparatus in power systems. However, the noisy PD measurements may significantly degrade the performance of location algorithms. This article deals with the PD location problem by using adaptive filtering techniques. Heretofore, scarce literature focuses on addressing the PD location based on such method. A novel adaptive algorithm, termed as total least-squares (TLS)-Matérn kernel (TLS-MK), is proposed. Benefiting from the merits of the Matérn kernel, the TLS model can effectively suppress the noise from the direct and reflected waves of the PD source. Meanwhile, the TLS-MK algorithm is used to estimate the time difference, which is used in the PD location. Moreover, the convergence behavior of the TLS-MK algorithm is analyzed. Simulations and experiments show that the proposed algorithm can enhance the location accuracy as compared to state-of-the-art methods for various PD signals.

Time-frequency Fusion Method via Convolutional Neural Network for Partial Discharge Classification

To improve the accuracy of partial discharge (PD) pattern recognition by jointing time-domain (TD) and frequency-domain (FD) information, a time-frequency (TF) fusion method via convolution neural network (CNN) is proposed in this paper. Firstly, PD signals are represented by PD waveform images and transformed into the envelope of variational mode decomposition-based Hilbert marginal spectrum (VHMS). Secondly, a fusion network, FuNet involving a 2-dimensional CNN (2D-CNN), a 1D-CNN, and a multilayer perceptron (MLP), is established to join TF information. In FuNet, the 2D-CNN inputted by PD waveform images and 1D-CNN adopted the envelope of VHMS of PD signal as its input are all improved by drawing on the complementary strengths of different convolution layers’ features. Then the MLP will fuse the extracted TD and FD features and classify the PD defects.

A Comparison of Double-End Partial Discharge Localization Algorithms in Power Cables

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD localization algorithms. The SC algorithms have shown that identifying the correlation bond between two cues instead of the peak of the PD signal in the MPD algorithm significantly increases the PD localization accuracy. The results show that the SCTM algorithm outperforms the MPD and SC algorithms in terms of accuracy.

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

Distributed Partial Discharge Locating and Detecting Scheme Based on Optical Fiber Rayleigh Backscattering Light Interference.

Optical fiber sensors are used for partial discharge detection in many applications due their advantage of strong anti-electromagnetic interference capability. Multi-point distributed partial discharge detection and location are important for electrical equipment. In this paper, a distributed partial discharge location and detection scheme based on optical fiber Rayleigh backscattering light interference is experimentally demonstrated. At the same time, the location and extraction algorithm is used to demodulate the partial discharge signal; furthermore, the high-pass filter is used to reduce the system low-frequency noise and environment noise. It is clear that the proposed system can detect a partial discharge signal generated by metal needle sensitivity, and the detectable frequency range is 0-2.5 kHz. We carried out 10 locating tests for two sensing units, the experimental results show that the maximum location error is 1.0 m, and the maximum standard deviation is 0.3795. At same time, the signal-to-noise ratio (SNR) of sensing unit 1 and sensing unit 2 are greatly improved after demodulation, which are 39.7 and 38.8, respectively. This provides a new method for a multipoint-distributed optical fiber sensor used for detecting and locating a long-distance electrical equipment partial discharge signal.

Partial Discharge Detection Technology for Switchgear Based on Near-Field Detection

In view of the fact that the partial discharge (PD) signal energy is mainly concentrated below hundreds of megahertz, the ultra-high frequency part of the energy is weak, and the interior space of the switchgear is narrow, this paper proposes a new method for PD detection of the switchgear based on near-field detection. Firstly, based on the principle of PD, the field characteristics of the signal in the switchgear are analyzed. After that, the probe is designed with an electric small loop structure. Based on its equivalent circuit, its measurement principle and amplitude frequency characteristics are analyzed. The influence of probe size and material on amplitude frequency characteristics is obtained by using simulation software High Frequency Structure Simulator (HFSS), and the probe parameters suitable for PD detection in the switchgear are determined. Finally, the performance of the probe is measured by network analyzer, and the PD signal is tested on the simulated PD test platform. The results show that the probe works in the frequency band of 10–200 MHz and can receive PD signals containing more energy information. In the operating frequency band, the reflection coefficient of the probe port is very large, and its interference to the signal near field is particularly small. The probe also has good frequency response characteristics, and the fluctuation in the frequency band is less than 5 dB, which can obtain more accurate PD signal characteristics in subsequent processing. In addition, the probe is passive, with dimensions of 166 mm in length, 104 mm in width, and 2 mm in thickness, which is suitable for placing in the switchgear with small internal space. The results of PD receiving test show that the probe can reflect the occurrence of PD remarkably and accurately.

Verification of Interpretability of Phase-Resolved Partial Discharge Using a CNN With SHAP

Deep neural networks can be used to distinguish partial discharge (PD) signals despite their complexity. This study analyzes the appropriateness of interpreting phase-resolved partial discharge (PRPD) signals using a convolutional neural network (CNN) through the Shapley additive explanation (SHAP) method. The generated PRPD signals were accumulated by applying AC voltage to four types of electrodes with a polyethylene sheet, followed by their conversion into scattered images to construct a classification model, CNN. The SHAP values for each pixel in the test images were then calculated. The result indicated that the pixels around the 0 V line retained high absolute SHAP values in every label, and the average of the summation of absolute SHAP values over all labels and all test images, which indicates the weight of each pixel, shows a similar tendency. Additionally, insight tests of the two CNN models were conducted, and the results showed that some structural defects could be detected by visualizing the SHAP values for each pixel. Finally, the verification of parameter-and-data vulnerability showed that SHAP has sufficient endurance against some types of instability in the data and model. Although the SHAP method lacks a perfect causal model because of its origin, the results imply that in appropriate use cases, weights on classifications of PD signals could be described by SHAP’s interpretability.

Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude–time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals.

Design of Bowtie Antenna With Rounded Edge and Middle-Sliced Modifications for UHF Partial Discharge Sensor

Partial Discharge (PD) detection using ultra-high frequency (UHF) method has been proven as more noise-resistant in GIS condition monitoring, compared to the conventional PD detection methods, where the common noise frequency range such as in the power plants is below UHF frequency range. However, noise in UHF range may occur at certain conditions. Thus, UHF sensor with high sensitivity is necessary to detect PD-induced electromagnetic wave in UHF range. This paper discusses the modified design of a bowtie antenna as UHF sensor and its performance in detecting PD in the air. The modification method is conducted through simulation using antenna design software, resulting in curved-edge and middle sliced bowtie antennas. The designed bowtie antenna has wider bandwidth of above 300 MHz, reflection coefficient lower than -10 dB, and the voltage standing wave ratio (VSWR) less than 2. During initial design, the antenna geometry including gap distance, wing radius, and flare angle are adjusted to obtain the optimum antenna characteristics. The final designed sensor is then fabricated on a printed circuit board (PCB) with FR4-epoxy substrate material and tested using Vector Network Analyzer (VNA). Furthermore, the modified antennas are also tested in the high voltage laboratory to detect PD in needle–plane electrode. The experiment results show that both modified bowtie antennas can successfully detect the PD signal with correct phase patterns, and that the middle-sliced modified bowtie antenna is more sensitive than the rounded edge modified bowtie antenna.

Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems

In modern power systems, condition based monitoring and diagnosis is essential to ensure the effective and reliable operation of different high voltage equipment (HVE). Compared to other monitoring techniques, partial discharges (PD) measurement is considered as a key method for assessing the insulation health condition. The benefits of PD condition monitoring of HVE can be extended by proper detection, identification, and interpretation of PD signal. Among both online and offline PD monitoring techniques, online PD monitoring is a very promising technique that assists in robust monitoring system which reduces the power failure incidents in power system components. Therefore, to understand recent developments and trends in theory and in practice, it is necessary to establish a holistic analysis of current online PD monitoring techniques for HVE in power systems. This paper presents an intensive literature review of current online PD monitoring techniques used for different high voltage electric components in power system. Finally, a smart PD monitoring techniques based on wireless sensor board is proposed. The proposed smart PD monitoring framework may be used to correctly estimate the insulation degradation in HVE and enhance the overall performance of power systems.

Improved demodulation method for fibre optic sensors applied in transformer partial discharge detection

AbstractThe 2 × 2 couplers, as a demodulation core of the fibre optic sensing system, have been extensively employed in fibre optic sensors for partial discharge (PD) detection of power equipment. However, the unstable demodulation sensitivity and low signal‐to‐noise ratio (SNR) of such a system are still unsatisfactory in engineering. This work uses a distributed feedback fibre laser as the sensor and employs a common asymmetric 2 × 3 coupler instead of a 2 × 2 coupler as the demodulation core to design a PD detection system. Then, we propose a new demodulation method for PD acoustic emission signals based on the Naval Postgraduate School method. On the PD detection platform, the performance of the conventional detection system and the proposed system is compared. The experimental results show that the proposed system has better reliability and can identify the PD signals in all instances. Furthermore, the average SNR of the new demodulation method is 6.11 dB higher than the traditional 2 × 2 coupler system and can be extended to most PD detection systems using fibre optic sensors.

A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold

When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.

Partial Discharge Monitoring Method in Switchgear with Bushing High-Voltage Sensor

10 kV and 35 kV switchgear are the key equipment in the power grid, and the safety of their insulation is related to the stability of the entire power grid. But the accuracy of the traditional partial discharge detection method is difficult to meet the detection requirements of the new SF6 insulated switchgear. This work analyzes the feasibility of using the high-voltage sensor in the bushing to detect partial discharge, and the analysis shows that the synchronous signal and the partial discharge signal can be obtained from the bushing sensor. Detection experiments were carried out using three typical partial discharge models, with both HFCT and a bushing sensor. The measurement results show that the bushing sensor can effectively detect the partial discharge in the switchgear and obtain a PRPD plot similar to HFCT. It is also found that adjusting the sampling resistance can change the partial discharge detection range of the bushing sensor.

Electrical Tree Initiation and Growth in LDPE Under Negative HVDC Superimposed With AC Ripples

Understanding the role of power quality in the aging of HVdc cable systems is critical to the reliable connection of offshore renewable energy sources, and hence global ambitions to reduce carbon emissions. This work investigates electrical treeing with a needle-plane geometry in low-density polyethylene (LDPE) under a high negative dc voltage superimposed with ac ripples (−60 kV dc ± 7 kV ac). Tree initiation showed behavior similar to that widely reported under pure power frequencies. Subsequent tree growth, however, was observed to be confined in a smaller area with limited length and width than seen under pure ac voltages, even after long periods of voltage application. Traditional 2-D imaging showed overlapped tree channels which developed to cover the whole area within the tree outline. A distinguishing tree shape resulted, which we have named a “slim bouquet” shape. The volume rendering from X-ray computed tomography (XCT) showed the structure had a 3-D fractal dimension greater than 2, considerably larger than its 2-D representation. PD signals during the growth had wing-like phase-resolved partial discharge (PRPD) clusters and signal phase concentrations between 10°–45° appeared after hours of growth. There was a comb-like appearance in maximum partial discharge (PD) magnitude variation, which is typical in pure high negative dc fields. Consideration is given to both space charge distribution controlled by high dc fields and continuous degradation by ac fields to explain the slim bouquet tree formation.

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

Pareto optimization of wavelet filter design for partial discharge detection in electrical machines

A reliable detection of partial discharges (PDs) in the insulation system of electrical machines is essential to prevent premature breakdowns. Wavelet filters (WFs) are often recommended for denoising this PD measurement data. However, filtering performance strongly depends on numerous settings, which impedes an optimum filter choice. We propose an approach to systematically select the best filter for denoising PD signals. First, representative PD pulses are determined from measurement data by an averaging and a singular value decomposition method. Next, realistic benchmark signals are generated for filter assessment. Further, all possible WF combinations from a set of different mother wavelet selection methods, decomposition trees, and thresholding approaches are evaluated. On the basis of two contradicting objectives, namely mean square error of noise intervals and pulse amplitude distortion, Pareto optimization is applied to choose optimal WF settings. The corresponding filters dominate several common WF proposals from previous literature.

A novel fault diagnosis method of power cable based on convolutional probabilistic neural network with discrete wavelet transform and symmetrized dot pattern

To accurately diagnose the XLPE power cable insulation fault, this research proposed a novel hybrid algorithm combined with Convolutional Probabilistic Neural Network (CPNN) based on Discrete Wavelet Transform (DWT) and Symmetrized Dot Pattern (SDP) analysis. First, it built seven different power cable insulation defect models to measure partial discharge signals of power cable insulation faults. Then, a discrete wavelet transform was used for noise filtering. The time-domain partial discharge signal was directly converted into the point coordinate feature image of visual polar coordinates by SDP analyses. Finally, the feature image was trained and recognized by CPNN. After the important feature information of the feature-image was extracted by convolution layer and pooling layer operations, it is applied to the power cable insulation fault state diagnosis system based on the rapid learning and highly parallel computing of Probabilistic Neural Network (PNN). The experimental results proved that the method proposed in this research could accurately diagnose the power cable insulation fault type and the recognition accuracy is higher than 96%. The proposed method has a short detection time and high diagnostic accuracy. This proves that it can be applied to detect the power cable insulation fault type.

Robust on-line partial discharge measurement of 35-kV cables in wind farms via dual sensors

Condition-based maintenance of cables in wind farms is critical for their operation reliability and stability. A promising solution to this task is to carry out on-line partial discharge measurement on the cables. However, impulse noise interferences from wind turbines or adjacent overhead lines could severely reduce the sensitivity of in field partial discharge measurement. Accordingly, this paper proposes a robust method to recognize the harmful partial discharge signal from the severe impulse noise. The method is based on joint analysis of the noisy partial discharge signals detected by two non-invasive partial discharge sensors, which are mounted on the double sides of the cable or on its terminal. The noiseless partial discharge signal and the impulse noise can be separated well via performing statistical clustering analysis on the pulse source location map or peak ratio vector obtained from the two detected noisy signal sequences. Finally, the effectiveness and feasibility of the proposed method is validated on two real partial discharge-affected 35-kV cables in an onshore wind farm.

A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition

Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance.

Small window cross-correlation accumulation time difference algorithm based on peak judgment method and generalized cross-correlation method

Based on peak judgment method and generalized correlation method, this paper proposes a new ultra high frequency partial discharge signal time difference algorithm. This algorithm avoids the defect that the traditional cross-correlation algorithm has a large correlation error for signals with lower amplitude. At the same time, by accumulating the correlation results of N groups of data, the influence of random interference of UHF PD signal on the correlation results is reduced. N times of correlation analysis were performed and correlation results were accumulated in a small area near the peak of the UHF PD signal, not only avoiding the deficiencies of the traditional peak judgment method that the peak value of the UHF PD signal is not easy to determine, but also minimizing the effect of PD white noise. In order to verify the effectiveness of the proposed algorithm, the time difference calculated by different algorithm are compared, and the results show that the proposed algorithm is superior to other existing algorithms.