Identification of single and multiple partial discharge location within a power apparatus using Random Forest classifier

Research on characteristics of acoustic signal of typical partial discharge models

Acoustic partial discharge (PD) detection method has been developed as an important non-destructive testing technique adopted for monitoring the material processes and for testing, detection, and characterization of any defect in an electrical apparatus like power transformers. This method has advantages like the placement of several sensors on the outside of the tank allows a localization of PD source and measurements can be performed on-line and on-site. The paper describes a combination of UHF/electrical and acoustic method because combination of methods more increases plausibility of PD activity than pure acoustic method. This method can find PD sources with higher sensitivity especially for hidden PD defects. An experimental validation has been done on the transformer insulation model using an acoustic sensor at first in this paper. The study was complemented with measurements on transformers in test field.

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

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.

This paper presents a novel self-sensing partial discharge (PD) detection method for power transformers that leverages the transformer's iron core as an ultra-high frequency (UHF) sensor. The method aims to address the limitations of traditional PD detection techniques, which either require intrusive internal sensors or are susceptible to external interference. The experiments conducted on a 220kV transformer model with a suspension discharge defect model demonstrate the high detection sensitivity and anti-interference performance of the proposed method. The results show that the iron core can effectively sense PD UHF signals, providing a reliable and practical approach for on-site PD detection in transformers. This PD detection method solves the problem that the internal UHF sensor has security risks while the external sensor is susceptible to interference. It provides a new method for PD detection of transformers in field.

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.

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.

The main problem in power transformers is the degradation of the isolation, and partial discharges are a major contribution to this shortcoming. Partial discharges inside medium-and high-power transformers generate ultrasonic pressure waves which are usually detected by using acoustic piezoelectric sensors placed outside the transformers. In this paper we describe an optical fibre interferometric sensor we are developing for the detection and location of partial discharges by measuring inside the power transformers. This non-intrusive optical fibre sensor allows a high level of sensitivity to be achieved for the typical small pressure variations created by partial discharges, specially if compared with the results obtained by acoustic piezoelectric external sensors. We present interferometrically detected levels of partial discharge. These results indicate that the developed optical fibre sensor is able to detect a partial discharge in the range of 103 pC that produces an optical phase shift of about 2 rad with a resolution of 30 pC in charge means. Finally, the wavelet analysis is used to identify the acoustic patterns instead of the more commonly used Fourier analysis. When transforming the measured signal to the frequency domain, the temporal information disappears. But the acoustic signals really contain numerous transitory pieces of information to be considered. By looking at a Fourier-transformed signal it is impossible to state when a particular event took place. The wavelet analysis helps to solve this problem, and some results are presented here to show this.

This paper describes an on-line partial discharge (PD) detection technique in power cables. The technique uses a digital spectrum analyzer with a high-frequency preamplifier and several types of high-frequency inductive sensors. In extruded and impregnated cables, the PD detection is made in the VHF range. The suitability and the sensitivity of the VHF technique were checked against two other conventional PD detecting methods (Pulse Phase Analyzer and Fast Digital Scope) by performing PD measurements in EPR-insulated cable under laboratory-controlled conditions. The suitability and sensitivity of the VHF method were as good as the two conventional PD detection method. The VHF method runs used to conduct on-site PD measurement in several power cables. These measurements were conducted while the cables were in service. Special care was given to distinguish between external electromagnetic noise and electromagnetic signals produced by the PD activities inside the cable insulation.

<div class="section abstract"><div class="htmlview paragraph">Partial discharge (PD) detection has been always a fundamental tool, potentially, for the design, quality control, commissioning, and reliability monitoring for the of insulation systems. The word “potentially” stems from the objective consideration of the intrinsic limitations suffered by the existing partial discharge, PD, measurement technologies, especially the need of experts to interpret results and the lack of clear correlation between PD-related quantities, and the condition-based maintenance approach. On the whole, a thorough revision of insulation systems design procedures and of the tools to evaluate aging and failure risk is needed, especially in components of electrical assets which are critical in terms of reliability, resilience, and safety. This paper focuses on critical asset components, such as ships, aircrafts, aerospace, and any type of vehicles, where the coming electrification is significantly increasing nominal voltage, power density and efficiency, and where power electronics is the operative core. A new design approach, PD-free and reliability redundant, is briefly discussed, highlighting the importance to develop an innovative PD detection and analytics approach where measurements and interpretation are fully automatic and unsupervised, and experts are replaced by artificial intelligence. The aim is to remove the adverb “potentially” applied to the usefulness of PD detection and monitoring. The application of this innovative PD technology to measurements on dedicated test objects, able to incept either internal or surface discharges, or both, show how the capability to identify automatically the source typology of PD can help both in insulation system design and in devising health-condition monitoring systems for the asset components.</div></div>

Partial discharge (PD) measurement allows detection of insulation defects in various electric power apparatus before an insulation failure occurs. It can also improve the reliability of the insulation performance of apparatus, and support rational and economical design, lifetime prediction, and nondestructive inspection. Historically speaking, PD detection methods have evolved in each category of apparatus and power cables. However, there does not seem to exist what we call universality in PD detection technology that is available to all kinds of power equipment. This article specifically describes features, similarities, and differences of partial discharge measurement and diagnosis technologies for electric power apparatus used in transmission and distribution. The main task of this article lies in not simply reviewing PD measurement and diagnosis techniques but trying a cross-equipment evaluation. First, this article deals with similarities and differences among partial discharge detection techniques for typical power apparatuses including gas insulated switchgear, oil-immersed transformers, and power cables. In particular electrical measurement technology and electromagnetic measurement technology are compared selectively. The second subject is to describe signal-processing technologies, and discuss similarities and differences among equipment in terms of noise removal, location identification, and foreign particle identification. Finally, judgment methods and judgment criteria for equipment diagnoses as a whole are analyzed and described. These discussions are expected to clarify the positioning of the partial discharge measurement technologies for various equipment among all electric power apparatus, and support technological development in the future.

This paper proposes two alternative methods for online partial discharge (PD) detection and localization of a 6.2/7 kV XLPE insulated medium voltage (MV) power cable. The artificial defect as surface discharge was made in the middle of the cable at the joint connecting between two of 10 m length cables with 50 sq.mm diameter. Therefore, the total length of the cable is 20 m. In addition, the corona discharge was also made at the end of cable termination. The PD measurement was performed under uncontrollable noises with limitations of cable characteristics. UHF and HFCT sensors together with their supplemented techniques as Trending method for UHF sensor as well as Time Domain Reflectometry (TDR) technique for HFCT sensor are applied in the experiment in order to identify and locate the PD types. The results for PD identification and localization of both UHF and HFCT sensors are satisfied.

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) detection is an effective method for evaluating the insulation state of power equipment. If partial discharge is not detected in time, it will cause insulation damage of power equipment, which will affect the safe and stable operation of the power system. With the development of modern society and science and technology, the application of Internet of Things technology in electric power operation is more and more extensive, and the electromagnetic environment of electric power operation site has become more complicated. Therefore, it is necessary to study the partial discharge detection technology of power equipment based on spectrum analysis in the complicated and strong electromagnetic environment of substation.

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.