Characterization of Phase Resolved Partial Discharge waveforms from instrument transformer using statistical signal processing technique

Partial discharge detection on power equipment using a magneto-resistive sensor

Research on characteristics of acoustic signal of typical partial discharge models

Noise has been a major problem in Partial Discharge (PD) measurement, hampering its effectiveness as a means of assessing insulation condition and of diagnosing faults in power plant insulation. The problem has become more evident in recent years as efforts are made to adopt on-line PD-detection for practical plant insulation condition monitoring. The majority of PD detection systems use Ultra High Frequency (UHF), High Frequency Current Transformer (HFCT), Acoustic Emission (AE) sensors or IEC60270 standard electrical contact systems. Despite great advances which have been achieved in hardware instrumentation over the past years, various forms of noise still affect PD measurements, irrespective of the type of measurement system employed. This paper presents the experience which the authors have accumulated in recent years dealing with noise in PD measurement using the four, aforementioned, measurement systems. Following a brief comparison of the requirement for signal analysis of the four PD measurement systems, the paper addresses the source of noise that often appears in practical applications. Denoising techniques such as the traditional digital filters, matched filters, discrete wavelet transforms and the second generation wavelet transforms will then be introduced and contrasted in terms of their distinct features, advantages and disadvantages. The most effective technique will then be proposed for the four PD measurement systems discussed. Real world data will be used to support the conclusions of the paper.

High voltage equipment can be damaged when there is a failure of the insulation. Failure would be indicated by the Partial Discharge (PD) phenomenon during the pre-breakdown condition. PD detection methods commonly used, such as thermography and acoustic emission (AE), still have some weaknesses. Based on these conditions, this study discusses the detection method of PD containing Ultra High Frequency (UHF) in air insulation using an antenna that can capture electromagnetic waves. The electrodes used are needle to plane. The variables used are the shape and distance of the electrode. Characteristics analyzed during Partial Discharge Inception Voltage (PDIV). The data retrieved would be processed so that it would display Phase Resolved Partial Discharge (PRPD). The results obtained from the antenna would be compared with the leakage current method using the High Frequency Current Transformer (HFCT). PDIV pattern can be observed in the PRPD analysis, and each electrode has the characteristics of PD at a specific phase angle. The dominant PD point electrodes occur at an angle of 331.2 ° to 342 °, the dominant PD box electrodes occur at an angle of 334.8 ° to 356.4 °, and the dominant PD blunt electrode occurs at an angle of 306 ° to 313.2 °. So, it can be concluded that the loop antenna can detect and observe PDIV patterns in air insulation.

The task of detecting and classifying Partial Discharge (PD) in switchgear remains challenging due to the presence of inherent noise and the subtle differences among various PD signals under different operational conditions. Traditional PD detection methods have primarily relied either on analyzing time-series waveforms or on employing Phase-Resolved Partial Discharge (PRPD) patterns, which utilize extensive statistical information for classification. However, these approaches are notably susceptible to variations in hardware configurations and signal-to-noise ratios (SNR), limiting their effectiveness in real-world applications. This paper proposes a novel approach that enhances PD detection through multimodal fusion of time-series signals and PRPD patterns. Our method innovatively integrates these modalities by leveraging the statistical features from PRPD to enrich the time-series data, thereby extracting features that are both hardware-robust and domain-invariant. This dual-modal fusion provides a deeper insight into the characteristics of PD, enabling better discrimination among different types of discharges. Furthermore, our strategy emphasizes the learning of domain-invariant features, ensuring the model’s flexibility across different hardware setups and environmental conditions. We conducted comprehensive experiments with data collected from multiple sensors under varied conditions, demonstrating the superiority of our method compared to existing methods. The results reveal a significant improvement in PD detection accuracy and robustness, highlighting the practical utility of our method in complex and varied field environments.

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.

Increasing need of cost effective identical sensors for partial discharge (PD) location prompted us to take up this research study. Four high-frequency current transformers (HFCT) were assembled and its performances with a used commercial sensor were compared with the known type of PD signals. In this paper, the responses due to surface, oil corona and air corona are analyzed. PD occurrence in 20 ms period and the shape of single PD with more sampled data points are characterized using seven techniques. The study indicates that HFCT with biflar winding is more sensitive in identifying different types of known PD. The number of turns and gauge of the used wire in HFCT play a role in increasing the sensitivity of PD detection.

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.

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.

At present, the partial discharge(PD) detection of high voltage power capacitor usually employs a resonant circuit which requires a very bulky reactor, and a PD detector with ultrasonic sensors. One of the problems with this approach is difficult experimental setup. Another is the limitation of PD signal analyze and get further valuable information, due to the properties of ultrasonic signal itself. This paper reports on a new user-friendly and effective way to carry out PD test of HV power capacitor. This system contains a supply module, a specially made sampling impedance and a PD pulse collector. The supply module, consists of high voltage DC power generator, protection resistance, damping resistance, semiconductor switch, which is integrated designed, characterized by small size, light weight, and low power consumption. The specially made sampling impedance which is employed to obtain PD signals could be applied to the detection of capacitor samples with different capacity, and the impedance can be directly connected to the end of the capacitor without the influence of large current signal. And the PD pulse collector is different from PD detectors under stable voltage, which needs to grasp the PD signal accurately in several cycles of the test loop oscillation, and the signal acquisition is required to be continuous. Compared with the previous approach, the oscillating wave circuit with specially made impedance reported in this paper, showed much more portability for large power capacitors PD detection, and the results provided chances for time and frequency domain spectrum analysis, which could be helpful to identify the PD type of HV power capacitors.

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.

Detection and classification of partial discharge (PD) signal in XLPE cable is very important to find out the root cause of insulation failure. The purpose of this work is to improve the accuracy and effectiveness in classifying various types of PD signals including corona, surface, and internal discharges in a noisy environment using machine learning (ML) and deep learning (DL) techniques. For ML models, statistical features such as skewness, kurtosis, standard deviation, and variance are extracted from the PD signals and used for training. In contrast, the DL model focuses on continuous wavelet transforms (CWT) based scalogram images which highlight the resolution of the signal’s energy and are used for feature extraction in the classification study. Instead of using traditional phase-resolved partial discharge (PRPD) plots, this work introduces a novel approach to converting PD signals to scalogram images using CWT. These scalogram images provide a visual representation of the frequency components of different PD types and how they change over time. The wavelet-based scalogram images are used as input features for the proposed convolutional neural network (CNN) and state-of-the-art (SOTA) DL models, along with shallow ML classifiers for model training, validation, and testing. Along with this k-fold cross-validation and hyperparameter tuning are also employed to enhance the overall performance of the model. The classification results demonstrate that the proposed CNN model achieves up to 97.33% recognition accuracy and less computational complexity as compared to various ML and DL models.

Maintenance of high voltage equipment is vital in order to detect an early partial discharge (PD) activity. PD based on IEC 60270 standard is defined as a localized electrical discharge that occurs between conductors and it only partially bridges the insulation. Nowadays, a commercial high-frequency current transformer (HFCT) has been used widely as a current transducer to detect and measure the PD current. However, commercial HFCT is very expensive in the market and quite heavy due to a large size. This paper presents a development of low-cost HFCT using ferrite core with a few benefits such as low cost, lightweight and also has a wide operating bandwidth which is suitable in detecting PD. Due to it is an iron-cored, it has also offered a higher sensitivity which makes the PD measurement more accurate. Two configurations of the developed HFCT with the different number of turns at the secondary side which are 7 turns and 12 turns were presented to analyze the higher sensitivity for PD detection. Based on the obtained results of PD measurement between two configurations of developed HFCT, the developed HFCT with the lower number of turn had more appropriate results as it provided higher sensitivity and wider bandwidth.

The detection of partial discharge (PD) in transformers is of great significance for preventing insulation defects from developing into insulation failures. Transformer partial discharge detection technology includes pulse current method, radio frequency measurement method, UHF method, ultrasonic detection method, gas chromatography, and optical detection method. Among them, the optical detection method has the characteristics of strong anti-electromagnetic interference ability, and the signal can be transmitted and received without loss. The optical detection method has unique advantages. In this paper, an optical signal measuring device for partial discharge inside the transformer is designed and fabricated, the typical insulation defect inside the transformer is simulated, and the optical signal characteristics of typical partial discharge are obtained. Compared with the pulse current method, it is found that the magnitude of partial discharge can be characterized by the integral of the optical signal. The characteristic of the optical signal phase resolved partial discharge (PRPD) spectrum of typical insulation defects is obtained. The PRPD spectrum of the optical signals of different defects is obviously different. It is feasible to draw the PRPD spectrum and identify the defect types according to the optical signal, which can be further applied to the identification of internal faults in transformers.

Partial discharge (PD) can be used to predict insulation failures in power transformers. Accurate detection of particular PD types has a significant role in anticipating forthcoming outages. However, the noise encountered with PD measurements negatively affects the detection accuracy. In this paper, we propose a robust PD detection technique that is immune to noise through efficient frequency domain-based feature extraction from acoustic emission signals. The PD spectrum is first obtained using Fourier transform and then, the low frequency band of 0.05–0.15MHz is used as a representative feature vector. Finally, four different classifiers are used to examine the PD detection accuracy. Experimental results on a benchmark dataset verify the robustness of the proposed method for PD detection, as it achieves 100% classification accuracy for clean PD signals and up to 99.62% for noisy PD signals.