Analysis of partial discharge sources in stator insulation system using variable excitation frequency

Nair, Ramesh P.; Vishwanath, Sumangala B.

doi:10.1049/iet-smt.2018.5666

Cited by 15 publications

(15 citation statements)

References 22 publications

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“…The presence of mica in epoxy composites delays thermoelectric aging of epoxy insulation and improves the thermal properties of the epoxy [ 28 ]. Moreover, some reports suggest that mica incorporation into epoxy adhesive enhances the compressive and bending strength [ 29 , 30 , 31 , 32 , 33 ]. Nevertheless, the presence of mica makes the crosslinking kinetics of the epoxy indeed complex and thereof affects the gelation behavior of epoxy resins in insulation usage.…”

Section: Introductionmentioning

confidence: 99%

Epoxy/Ionic Liquid-Modified Mica Nanocomposites: Network Formation–Network Degradation Correlation

et al. 2021

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We synthesized pristine mica (Mica) and N-octadecyl-N’-octadecyl imidazolium iodide (IM) modified mica (Mica-IM), characterized it, and applied it at 0.1–5.0 wt.% loading to prepare epoxy nanocomposites. Dynamic differential scanning calorimetry (DSC) was carried out for the analysis of the cure potential and kinetics of epoxy/Mica and epoxy/Mica-IM curing reaction with amine curing agents at low loading of 0.1 wt.% to avoid particle aggregation. The dimensionless Cure Index (CI) was used for qualitative analysis of epoxy crosslinking in the presence of Mica and Mica-IM, while qualitative cure behavior and kinetics were studied by using isoconversional methods. The results indicated that both Mica and Mica-IM improved the curability of epoxy system from a Poor to Good state when varying the heating rate in the interval of 5–15 °C min−1. The isoconversional methods suggested a lower activation energy for epoxy nanocomposites with respect to the blank epoxy; thus, Mica and Mica-IM improved crosslinking of epoxy. The higher order of autocatalytic reaction for epoxy/Mica-IM was indicative of the role of liquid crystals in the epoxide ring opening. The glass transition temperature for nanocomposites containing Mica and Mica-IM was also lower than the neat epoxy. This means that nanoparticles participated the reaction because of being reactive, which decelerated segmental motion of the epoxy chains. The kinetics of the thermal decomposition were evaluated for the neat and mica incorporated epoxy nanocomposites epoxy with varying Mica and Mica-IM amounts in the system (0.5, 2.0 and 5.0 wt.%) and heating rates. The epoxy/Mica-IM at 2.0 wt.% of nanoparticle showed the highest thermal stability, featured by the maximum value of activation energy devoted to the assigned system. The kinetics of the network formation and network degradation were correlated to demonstrate how molecular-level transformations can be viewed semi-experimentally.

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Section: Introductionmentioning

confidence: 99%

Epoxy/Ionic Liquid-Modified Mica Nanocomposites: Network Formation–Network Degradation Correlation

et al. 2021

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“…Faults of capacitors are caused by potential internal defects, poor wiring during installation, or forced damage or overload that accelerate their deterioration and reduce their electric insulation strength. This study performed an AC voltage withstand test for a power capacitor [21,22]. In the test, an autotransformer regulated the voltage for the power capacitor, so as to measure the partial discharge phenomenon in a low-voltage state.…”

Section: Power Capacitor Inspection Test Methodsmentioning

confidence: 99%

Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method

Sian

Wang

et al. 2021

IET Science Measure & Tech

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This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20-80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power-related fields in the future.

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“…To analyze PD signals, time-resolved PD (TRPD) and phase-resolved PD (PRPD) were studied. Using TRPD signals, the shape of the discharge pulse through timedomain, frequency-domain, and both time-and frequencydomain features were analyzed to determine preliminary estimates [10]- [15]. The time-domain analysis requires few computing resources and simple algorithmic interface with frequency-domain and time-and-frequency domain techniques [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Model for Unknown Partial Discharge Diagnosis in Gas-Insulated Switchgears Using Convolutional Neural Networks

et al. 2021

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Deep neural networks (DNNs) are widely used for fault classification using partial discharges (PDs) to evaluate various electrical apparatuses and achieve high classification accuracy pertaining to trained PD faults. However, there is a risk of false alarm in the case of untrained PD faults because it is difficult for DNNs to predict data that were not included in the training process. In this paper, we research classification problems of unknown classes using PDs in gas-insulated switchgears (GISs) and propose a deep ensemble model to obtain the confidence of output probability and determine thresholds to detect unknown fault classes. The proposed model was verified by real-world phase-resolved PD (PRPD) experiments using online ultra-high frequency (UHF) PD measurement systems. The experimental results show that the proposed model achieves better unknown detection performance for the untrained PD faults and retains the classification performance for the trained PD faults.INDEX TERMS Fault diagnosis, convolutional neural network (CNN), ensemble model, partial discharges (PDs), gas-insulated switchgear (GIS).

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Analysis of partial discharge sources in stator insulation system using variable excitation frequency

Cited by 15 publications

References 22 publications

Epoxy/Ionic Liquid-Modified Mica Nanocomposites: Network Formation–Network Degradation Correlation

Epoxy/Ionic Liquid-Modified Mica Nanocomposites: Network Formation–Network Degradation Correlation

Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method

Deep Ensemble Model for Unknown Partial Discharge Diagnosis in Gas-Insulated Switchgears Using Convolutional Neural Networks

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