Research on high voltage capacitor partial discharge detection with portable oscillating wave circuit

Research on characteristics of acoustic signal of typical partial discharge models

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.

Partial discharge (PD) detection is essential for evaluating insulation health in overhead lines with covered conductors (CC), particularly within smart grid systems. However, since transient PD signals require high-sampling-rate equipment for capture and analysis, PD detection faces the challenge of efficiently extracting sparse PD features from long signals. To address these challenges, this paper proposes a novel LWPET framework for PD detection. Specifically, we design the Legendre multi-wavelet channel attention (LWCA) module and the Legendre multi-wavelet spatial attention (LWSA) module in the multi-wavelet multi-scale frequency domain to effectively extract PD features. This approach fully leverages the multi-regularity of LW, which closely matches the physical characteristics of PD faults, to enable in-depth PD pattern mining and also enhances the analytical capacity of the feature extraction model through attention mechanisms. Additionally, the Legendre multi-wavelet patch embedding (LWPE) module is introduced to address the difficulty of long-sequence representation learning. This module extracts key patch-level features using LWCA and LWSA, and then feeds the refined features into a Transformer Encoder for further PD feature extraction. Experiments on the VSB dataset demonstrate that LWPET achieves high accuracy (MCC 0.822, precision 0.866, recall 0.800) and efficiency, demonstrating its potential for real-time PD detection in smart grids.

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.

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.

Capacitor voltage transformers (CVTs) composed of aluminum foil/oil-impregnated paper insulation system have been operated over 30 years in substations. For the condition monitoring and diagnosis of aged CVTs, partial discharge (PD) detection is expected. However, PD characteristics from PD inception and progress to breakdown (BD) have not yet been clarified. Thus, PD characteristics of a fundamental insulation model composed of aluminum foil/oil-impregnated paper insulation systems are discussed in this paper. Experimental results under a fixed applied voltage revealed that PD characteristics can be classified into 3 periods. In Period 1, PD signals were intermittently generated mainly around the peak phase of applied voltage. In Period 2, PD signals were continuously generated around zero crossing (void discharge) to peak phase. In Period 3, PD signals larger than 10,000 pC were generated. Together with the above electrical PD measurement, the optical measurement of PD light emission images, bubble behavior, and their time transmission were also carried out in order to discuss the mechanism of PD progress.

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.

Partial discharge (PD) detection is an important technique for assessing the health of high voltage power transformers. A partial discharge signal within an oil-filled power transformer may reach a winding first, then travel along the winding to the bushing core bar. The bushing, acting like a capacitor, can transfer the high frequency components of the partial discharge signal to its tap point. Previous published work has shown that these high frequency components at the bushing tap point can be detected using a suitable sensor and transmitted to remote digital equipment for on-line analysis. In a high voltage substation there is often excessive electrical noise which may corrupt any measured PD signal from the bushing tap point as the bushing acts as an antenna. If signal transmission is implemented using standard coaxial cables this can result in further corruption of the PD signal. The use of optical transmission techniques therefore has clear advantages not only through improved noise immunity but also because it realises electrical isolation and improved operator safety. This paper details the application of an electrooptic modulator to generate transmission signals over polarization maintaining optical fibre from the measurement point. The feasibility of this approach has been investigated using PD signals measured at the tap point of a 60 kV bushing, two different PD sources have been used and in both cases it was possible to detect PD activity above 40pC.

Partial discharge (PD) detection has garnered considerable attention due to its critical role in condition assessment and fault prediction of power system equipment. However, effcient and accurate acquisition of PD signals remains a significant technical challenge. Among mainstream technologies, the ultra-high frequency (UHF) detection method plays a key role in capturing PD signals. This work introduces a Rydberg atom-based electric field sensing approach for PD detection, employing a three-photon excitation scheme in Cesium vapor. Four representative PD types are investigated: void, floating, particle, and corona, revealing the ability of the nonmetallic atomic sensor to capture their distinct time-domain characteristics and periodic behaviors at the power frequency. In addition, an antenna-waveguide-vapor cell coupling configuration is proposed. This electro-optical to opto-electronic conversion mechanism allows the sensor to achieve long-range detection while preserving favorable signal-to-noise ratios. Although the sensor’s limited instantaneous bandwidth leads to pulse broadening in high-speed signals, its theoretical high sensitivity and wide operating bandwidth highlight its potential as a complement to traditional metal-antenna solutions. These findings provide a robust foundation for implementing quantum-enhanced PD monitoring in practical power engineering scenarios.

For specific circuits the accuracy of online partial discharge (PD) detection and location (PD-OL) systems is impaired by complications in the cable circuit configuration, such as ring-main-units (RMUs) along the cable under test. For maximum sensitivity matched filtering is applied for extracting PD pulses from the recorded signal. The matched filters are based on a series of prior online system identification measurements and a standard cable model. Especially, with an RMU along the cable, the matched filters only approximate the real waveforms due to signal distortion. This results in sub-optimal detection sensitivity. This paper describes automated procedure for updating the matched filter bank based on measured (large) PD signals. The algorithm analyzes detected pulses to judge whether the current templates perform optimally. If not, a new template is created increasing the PD detection sensitivity and the location accuracy, and also improving the recognition of recurring disturbances. The method is tested on signals measured on a live cable connection.

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.

Partial discharge (PD) detection is crucial for ensuring the reliability and longevity of high-voltage insulation systems, as undetected PD can lead to catastrophic failures. This paper presents a novel hybrid detection system integrating Convolutional Neural Networks (CNN) using the ResNet architecture for feature extraction and k-Nearest Neighbors (KNN) for classification. The proposed model achieves an impressive 98.79% accuracy, 99.41% precision, and 98.74% F1-score, significantly outperforming traditional methods such as SVM and CNN-SVM hybrids. The system demonstrates robust noise resistance, maintaining a detection rate above 98%, even in adverse operational conditions. Experimental validation on both synthetic and real-world PD datasets confirms the model’s scalability, computational efficiency, and ability to classify PD signals with minimal false positives and negatives. Additionally, it quantifies critical PD characteristics, achieving a mean true charge of 19.73 pC and a mean apparent charge of 3.15 pC, enabling improved insulation diagnostics. This work establishes a new benchmark in PD detection by addressing key challenges such as noise interference, real-time monitoring, and fault classification accuracy, paving the way for industrial deployment in high-voltage equipment monitoring systems. Future research will be directed toward extending the applicability of the model to other types of electrical insulation faults, enhancing its real-time performance, and integrating it into industrial monitoring systems for comprehensive fault diagnostics. These advancements position the proposed hybrid model as a pivotal tool for improving the safety and reliability of high-voltage insulation systems.

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

This paper deals with the improvement of performance of partial discharge (PD) detector by modification of Pi-Attenuator circuit and ultra wide band amplifier of impedance matching circuit. PD occurrence is the early indicator of power apparatus insulation failure. The detection method of this early symptom toward failure is indispensable. PD cause an electrical current in the ultra wide band (UWB) frequency up to several GHz. In order to obtain overall spectrum of PD signal, the bandwidth of PD measurement system should be wider than the bandwidth of PD signal. One of the methods to measure PD signals in UWB is by using impedance matching circuit (IMC) as a coupling device in the PD measurement system. The IMC consists of attenuator circuit and UWB amplifier. The impedance of the IMC should be the same as the impedance characteristic of the coaxial and the internal cables of the oscilloscope to optimize the power transfer generated by PD current. The performance of PD detector is improved by modification of pi-attenuator and wide band amplifier of IMC. The circuit design is conducted by simulation model using Advanced Design System 2014 (ADS2014) software to identify the circuit characteristics by performing S-parameter simulation.

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.