Explore the vast range of titles available.

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

This paper comprehensively presents an approach for modeling form wound coils of a motor driven by an inverter, with focus on the electric stresses on the coil insulation. A 10 kV SiC testbed for medium voltage form wound coils was developed to support and validate the modeling techniques discussed. A finite element analysis (FEA) model of the motor coil is presented using COMSOL 6.1. The FEA model was used to determine parameters for an electrical model based on the multi-conductor transmission line theory. The linking of these models allows for a rapid analysis of the electrical stresses the insulation can be exposed to. An experimental method for model validation using the empirical transfer function estimation (ETFE) approach to find the impedance response of the testbed for comparison to the proposed electrical model is presented and employed. The paper also uses the model to analyze the impact of insulation delamination and voids and to demonstrate the implementation of a metric called insulation state of health monitoring for both healthy and damaged coils.

Conductive particles are one of the most important defects which can greatly degrade the performance of gas-insulated metal-enclosed switchgear (GIS). Many efforts have been made to clarify the influence on the withstand voltage, understand the flashover mechanism, and build a comprehensive model to describe the particle-initiated flashover. In this study, a partial discharge (PD) signal detected through a photomultiplier (PMT) and recorded by a high-speed data acquisition (DAQ) system was used to analyze the discharge development of a conductive particle-contaminated GIS insulator under constant high AC voltage. An additional PMT was used as a reference to eliminate the dark count of the PMT and the data collection method of a DAQ system was optimized to capture the pulse waveform of each PD to obtain detailed physical information. Spectra of the PD pulse amplitude over pulse width, PD counts within various amplitude ranges over time and phase resolved partial discharge (PRPD) patterns of the PDs in different stages are obtained through the captured PD waveforms. Characteristics of the PDs from the application of the high AC voltage up to the flashover of the insulator were then analyzed, and it was found that the features of the PDs in the near-flashover stage were significantly different to the previous stages.

This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firstly, by constructing the pull rod defects dataset and training the YOLOv5s network, along with commonly used object detection algorithms in industrial defect detection, the feasibility of deep learning networks for insulation pull rod defects detection is explored. Secondly, the trained model is combined to build an intelligent detection device for pull rod defects, integrating insulation pull rod image acquisition and defect detection into a unified system. The research results demonstrate that the YOLOv5s network can quickly and accurately detect pull rod defects. On the test set constructed in this paper, the detection performance metric mAP@0.5:0.95 of the trained model reached 54.7%. Specifically, the mAP@0.5 score was 86.9% at a threshold of 0.5. The detection speed FPS reached 169.5, significantly improving the detection efficiency and accuracy compared to traditional object detection algorithms. By establishing an organic connection between the image hardware acquisition device and the deep learning network, the existing problems of inefficient detection and difficult storage of detection results in pull rod defects detection methods are effectively addressed. This research provides new insights for detecting insulation pull rod defects.

Using finite element numerical modeling, it is shown on mock-ups of solid insulation of electrical equipment with defects in the form of an air cylindrical cavity that the use of the concept of “defect capacity” is incorrect, since the boundaries of the defective region do not coincide with the equipotential field lines. The lack of consideration of the actual configuration of the electric field in the mock-up leads to unacceptably large errors in calculating the electric-field strength in the defect and, as a result, to erroneous estimates of the PD occurrence voltage. The calculation of the PD occurrence voltage according to the avalanche-streamer transition condition correctly reproduces the effect of the defect size, which is confirmed by the measurement results. The discrepancy between the calculated and experimental results reaches 12%, which requires additional research and refinement of the calculated ratios.

Fault diagnosis based on the partial discharge (PD) recognition has been widely applied on a gas-insulated line breaker (GILB) and gas-insulated switchgear (GIS) as a reliable online condition monitoring method. This paper dealt with insulation defect diagnosis based on a Random Forest (RF) algorithm with an optimized feature selection method. Four different types of insulation defect models, such as the free-moving particle (FMP) defect, the protrusion-on-conductor (POC) defect, the protrusion-on-enclosure (POE) defect, and the delamination defect, were prepared to simulate representative PD single pulses and PRPD patterns generated from the GILB. The PD signals generated from defect models were detected using the PRPD sensor which can detect phase-synchronized PD signals with the applied high-voltage (HV) signals without the need for additional equipment. Various statistical PD features were extracted from PD single pulses and PRPD patterns according to four kinds of PD defect models, and optimized features were selected with respect to variance importance analysis. Two kinds of PD datasets were established using all statistical features and top-ranked features. From the experimental results, the RF algorithm achieved accuracy rates exceeding 92%, and the PD datasets using only half of the statistical PD features could reduce the computational times while maintaining the accuracy rates.

Partial discharges (PD) are initiated in electrical equipment during various points of the equipment’s lifecycle. The intensity of PD defects rises continuously with time, which can lead to insulation degradation and reduced operational life of the electrical equipment. The optimum feature selection of PD signals captured, from different insulation defects, can enhance the classification accuracy of PD defects and facilitate better visualization of PD parameters for electric motor (EM) insulation monitoring and diagnostics. This paper presents a hybrid approach, based on Maximize Relevancy and Minimize Redundancy (mRMR) and random forest (RF), for the optimum feature selection and classification of PD signals in EMs containing multiple defects. For this purpose, four PD defects are developed in the EMs insulation under laboratory conditions, and 800 PD signals are acquired using a conventional IEC-60,270 experimental platform. The severity of these defects is determined and investigated based on PD characteristic parameters. Several features of both PD sweep signals and conventional PD pulses are extracted. Consequently, the mRMR feature selection technique is implemented to select the significant features of the detected PD signals. To establish the plausibility of this technique, several other feature selection algorithms, including RefliefF, Gini Index (GI), and Information Gain (IG), are introduced for the same datasets. The performance of all these feature selection algorithms is validated using three commonly used classification techniques such as RF, support vector machines (SVM), and k-nearest neighbors (k-NN). In summary, the results show that the combination of mRMR and RF proves to be the most effective feature selection algorithm for the classification of insulation defects in EMs, achieving an accuracy of 99.875%. This accuracy is significantly better than other feature selection and classification techniques and indicates its potential for application to other power system components.

With the increasing demand for power supply reliability, online monitoring techniques for cable health condition assessments are gaining more attention. Most prevailing techniques lack the sensitivity needed to detect minor insulation defects. A new monitoring technique based on the harmonic anomaly feature analysis of the shield-to-ground current is introduced in this paper. The sensor installation and data acquisition are convenient and intrinsically safe, which makes it a preferred online monitoring technique. This study focuses on the single-core 10 kV distribution cable type. The research work includes the theoretical analysis of the cable defect’s impact on the current harmonic features, which are then demonstrated by simulation and lab experiments. It has been found that cable insulation defects cause magnetic field distortion, which introduces various harmonic current components, principally, the third-, fifth-, and seventh-order harmonic. The harmonic anomaly features are load current-, defect type-, and aging time-dependent. The K-means algorithm was selected as the data analysis algorithm and was used to achieve insulation defect prediction. The research outcome establishes a solid basis for the field application of the shield-to-ground harmonic current monitoring technique.

A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, “PD Time”, associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain.

During the manufacturing process of oil-immersed current transformers, metallic particles may become embedded in the insulation wrapping, and the resulting electric field distortion is one of the primary causes of failure. Historically, the shape of metallic particles has often been simplified to a standard sphere, whereas in practice, these particles are predominantly irregular. In this study, ellipsoidal and flaky particles were selected to represent smooth and angular surfaces, respectively. Using COMSOL Multiphysics® (version 6.2) software, a three-dimensional simulation model of an oil-immersed inverted current transformer was developed, and the influence of defect position and size on electric field characteristics was analyzed. The results indicate that both types of defects cause electric field distortion, with longer particles exerting a greater influence on the electric field distribution. Under the voltage of a 220 kV system, elliptical particles (9 mm half shaft) lead to the maximum electric field intensity of main insulation of up to 45.1 × 106 V/m, while the maximum field strength of flaky particles (length 30 mm) is 28.9 × 106 V/m. Additionally, the closer the particles are to the inner side of the main insulation, the more significant their influence on the electric field distribution becomes. The findings provide a foundation for fault analysis and propagation studies related to the main insulation of current transformers.