Insulation Degradation Research Articles

RUL prediction for AECs of power electronic systems based on machine learning and error compensation

Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system.

In situ electrical monitoring of SiO2/Si structures in low-temperature plasma using impedance spectroscopy

To investigate the electrical properties and degradation features of dielectric materials during plasma exposure, we developed an in situ impedance spectroscopy (IS) system. We applied the proposed system to monitor SiO2/Si structures exposed to Ar plasma. By analyzing the measured data based on an equivalent circuit model considering the plasma and SiO2/Si structures, we obtained the resistance (R) and capacitance (C) values for the SiO2 film and SiO2/Si interface. In a cyclic experiment of in situ IS and high-energy ion irradiation, we characterized dielectric degradation by ion irradiation based on the variations in the R and C values of the SiO2 film. A continuous in situ IS measurement revealed temporal variations in the electrical properties of the film and interface independently. The thickness-dependent degradation observed for the RC variation was analyzed and compared with the results of previous ex situ measurement studies. This study demonstrates that the in situ IS measurement technique is promising for monitoring plasma-assisted dry processes.

Development of an Implantable Capacitive Pressure Sensor for Biomedical Applications.

In this study, a subminiature implantable capacitive pressure sensor is proposed for biomedical applications. The proposed pressure sensor comprises an array of elastic silicon nitride (SiN) diaphragms formed by the application of a polysilicon (p-Si) sacrificial layer. In addition, using the p-Si layer, a resistive temperature sensor is also integrated into one device without additional fabrication steps or extra cost, thus enabling the device to measure pressure and temperature simultaneously. The sensor with a size of 0.5 × 1.2 mm was fabricated using microelectromechanical systems (MEMS) technology and was packaged in needle-shaped metal housing that is both insertable and biocompatible. The packaged pressure sensor immersed in a physiological saline solution exhibited excellent performance without leakage. The sensor achieved a sensitivity of approximately 1.73 pF/bar and a hysteresis of about 1.7%, respectively. Furthermore, it was confirmed that the pressure sensor operated normally for 48 h without experiencing insulation breakdown or degradation of the capacitance. The integrated resistive temperature sensor also worked properly. The response of the temperature sensor varied linearly with temperature variation. It had an acceptable temperature coefficient of resistance (TCR) of approximately 0.25%/°C.

Optimal allocation of cloud energy storage system in low-voltage distribution network

In low-voltage distribution networks, an uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) brings about intensive peak loads in distribution transformers’ daily load curves. Due to insulation degradations, this issue ends in transformers’ loss of life (LOL) and early aging. The notion of cloud energy storage system (CESS) with larger power and energy capacities enables consumers to have access to cheaper energy storage facilities. Thanks to CESS installation, semi-smart, controlled, and low-cost charging of PHEVs could be realized to relieve the transformer’s peak loads and reduce the peak-to-average (PAR) ratio of load curve. To do so, this paper develops an optimal planning model for siting and sizing of a CESS to accommodate the charge/discharge requirements of consumers who are willing to collaborate with CESS and hence, implementing a controlled charging of PHEVs. The proposed model considers the investment and operation costs of both CESS and distribution network. It also models the transformer LOL costs. The PHEVs, in private garages, are supposed to be connected to the grid through a smart module whose connection time and status is optimally scheduled and controlled through the operator of the CESS. The CESS charging/discharging is regulated such that the previously determined time targets of PHEV owners are satisfied. To assess performance of the proposed model, extensive numerical studies are carried out in terms of techno-economic indices say as PAR reduction, conducting energy arbitrage, power losses reduction, and etc. Results are discussed in depth.

Analysis of Space Charge Signal Spatial Resolution Determined with PEA Method in Flat Samples including Attenuation Effects

The constant development of the electrical engineering sector, especially in the transmission of electrical energy under high-voltage direct current (HVDC), requires research on new insulation materials and investigations of physical phenomena under ultrahigh electrical fields in solid dielectrics. One of the current problematic issues is the formation of space charge in HV insulation systems, which affects the operational electrical field distribution and can lead to faster insulation degradation. There are several problems that have to be considered before every space charge measurement, such as the attenuation and dispersion of sound waves in tested dielectric materials, reflections at the interfaces, and the spatial resolution of the measured charge profile. The spatial resolution is one of the most important technical factors of the PEA measurement stand. The spatial resolution, as it is assumed, depends on several factors, such as the width of the pulser and the pulse rise time, the thickness of the piezoelectric sensor, and the dispersion of the tested material. The article presents the laboratory measurement results of the impact of pulser parameters, such as pulse width and rise time, and sensor thickness on the equivalent thickness of the measured net charge layer corresponding to the resolution of the method. The dispersion in the tested LDPE material is also presented and analysed. The results show that with an increase in the pulser rise time, a higher resolution of the pea method can be achieved.

Modelling Electroluminescence Emission in Polymeric Material Using Dimensional Analysis Method

The Electroluminescence (EL) technique has garnered significant interest over time for its reliable outcomes that enrich our understanding of the onset of electrical degradation in polymeric material. There is growing interest in combining both the EL technique and space charge measurement methods to obtain a more profound knowledge of the degradation and ageing of insulation. The generation of charge carriers in polymeric materials is widely acknowledged to be a crucial aspect of EL, involving injection, de-trapping, and field dissociation processes. This research investigates the variables that influence how EL emissions behave, including applied voltage or electric field, applied electrical frequency, ageing of material, and selection of materials and gases employed. It was found that these factors can be helpful in predicting the process of electrical ageing in insulation to avoid any catastrophes that may occur. A mathematical approach relating these factors and the intensity of EL is proposed through the aid of Dimensional Analysis method. A close relationship is obtained that suggests this mathematical approach can be utilised as a tool to predict electrical ageing of insulation material.

Evaluation of space charge distribution under a divergent electric field by combining experiment and simulation

Under divergent electric fields, space charge is locally accumulated and controls the initiation and evolution of insulation degradation phenomena, such as electrical trees and partial discharge. The space charge profiles provide crucial information for evaluating the insulation performance. However, the existing space charge measurement methods are not directly applicable under divergent electric fields. In this paper, a method combining experiment and simulation is proposed to evaluate the space charge distribution around a needle electrode. An experimental system is established, and the acoustic waves are measured with a hydrophone placed on the symmetric axis. In order to obtain the space charge distribution, the space charge injection process is simulated using the bipolar charge transport model to reduce the freedom of the distribution. Then, calculations are made for the acoustic waves produced by the simulated space charge distributions. Using the signals without the contribution of internal charge, a correspondence between the experiment and simulation is established. For each experimental signal, the matched calculated signal is obtained using the zero-crossing time as a reference. A perfect match between them is presented after the amplitude compensation, which indicates that the space charge distribution in the experiment is identical to that in the simulation model.

Decision-Making Method for Mine Cable Insulation Monitoring and Grounding Fault Diagnosis

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not.

Smart Plug 2.0: A Solid-State Smart Plug Device Preventing Fire and Shock Hazards

Electrical faults cause fire or shock hazards in homes and offices. Flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. This article introduces a new Smart Plug 2.0 device which offers all-in-one protection against short-circuit, overload, arc, and ground faults in addition to the wireless power control and monitor function of conventional smart plug devices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">di/dt</i> events. Comparing to other solid-state circuit breaker designs which typically respond to overcurrent conditions only, Smart Plug 2.0 integrates additional protective functions into the same hardware structure with minimal cost penalty. A 120 V/10 A solid-state Smart Plug 2.0 prototype using power <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moseft</small> s is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.

Enhancement of the Platinum nanoparticles of Biochemistry properties using a Scanning Electron Microscope SEM

Dielectric constants, dielectric degradation, and alternating current conductivity were calculated based on the frequency and composition of the doping material. At higher frequencies, high AC conductivity and a low dielectric constant were observed. The 'Maxwell Wagner Model' can clarify the above hypothesis and may provide new insights into the fabrication of dialectically conceivable nanomaterials. The photocatalytic function of our samples has been investigated by studying the degradation of Rhoda mine B in UV visible light. The photocatalyst analysis provides a convenient way to determine your fate through various reutilizations.

Molecular Bridges Link Monolayers of Hexagonal Boron Nitride during Dielectric Breakdown

We use conduction atomic force microscopy (CAFM) to examine the soft breakdown of monocrystalline hexagonal boron nitride (h-BN) and relate the observations to the defect generation and dielectric degradation in the h-BN by charge transport simulations and density functional theory (DFT) calculations. A modified CAFM approach is adopted, whereby 500 × 500 nm2 to 3 × 3 μm2 sized metal/h-BN/metal capacitors are fabricated on 7 to 19 nm-thick h-BN crystal flakes and the CAFM tip is placed on top of the capacitor as an electrical probe. Current–voltage (I-V) sweeps and time-dependent dielectric breakdown measurements indicate that defects are generated gradually over time, leading to a progressive increase in current prior to dielectric breakdown. Typical leakage currents are around 0.3 A/cm2 at a 10 MV/cm applied field. DFT calculations indicate that many types of defects could be generated and contribute to the leakage current. However, three defects created from adjacent boron and nitrogen monovacancies exhibit the lowest formation energy. These three defects form molecular bridges between two adjacent h-BN layers, which in turn “electrically shorts” the two layers at the defect location. Electrical shorting between layers is manifested in charge transport simulations, which show that the I-V data can only be correctly modeled by incorporating a decrease in effective electrical thickness of the h-BN as well as the usual increase in trap density, which, alone, cannot explain the experimental data. An alternative breakdown mechanism, namely, the physical removal of h-BN layers under soft breakdown, appears unlikely given the h-BN is mechanically confined by the electrodes and no change in AFM topography is observed after breakdown. High-resolution transmission electron microscope micrographs of the breakdown location show a highly localized (<1 nm) breakdown path extending between the two electrodes, with the h-BN layers fractured and disrupted, but not removed.

A Study on the Effectiveness of Partial Discharge Models for Various Electrical Machines’ Insulation Materials

A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the predominant insulation materials for the SGs’ windings because their characteristics and properties are suitable for extending the lifetime of the insulation. Partial discharges (PDs) are both a symptom of insulation degradation, as they cause serious problems for insulation, and a means to identify possible insulation faults with offline and/or online PD tests and measurements. A comparison of three different equivalent circuit models of PDs occurring in different insulation materials (epoxy resin, mica, and a combination of these two) is presented in this paper. Different applied voltages and/or various geometries of voids are the factors investigated through simulations. The number of PDs, PD activity, and flashover voltages are examined in order to evaluate which of the aforementioned materials has the best reaction against PD activity.

Thermal transport property correlated with microstructure transformation and structure evolution of Fe-based amorphous coating

The correlation between the thermal transport properties and microstructure features of amorphous coatings is crucial for reliable thermal insulation applications. In this work, an Fe48Cr15Mo14C15B6Y2 amorphous coating prepared by high-velocity oxygen-fuel spraying (HVOF) was employed to reveal this relationship under long-term heating. From the combined effects of the amorphous phase and unique coating structure, the proposed coating exhibits an extremely low thermal conductivity of 1.92 W/(m∙K), granting a significant potential for being as metal-based thermal barrier coatings. Isothermal annealing experiments below the glass transition temperature demonstrate that the thermal insulation property can maintain stability with prolonged annealing time, despite a slight degradation relative to the as-sprayed coating which is ascribed to structural relaxation. Although nearly full crystallization can be slowly induced by annealing at the critical temperature of 600 °C, the thermal conductivity displays a distinct two-stage variation with annealing time, which is primarily attributed to the sluggish sintering of the coating structure. Moreover, grain growth and rapid structural sintering result in a drastic degradation of thermal insulation in the early stage of annealing at 850 °C, whereas grain growth upon prolonged annealing governs further variation of thermal conductivity. The results of this study not only provide important insights into the degeneration mechanism of thermal insulation of Fe-based amorphous coating, but also present an inspiration for exploring the degeneration mechanism of the most metallic coatings serving at high temperatures.

A Method to Characterize the Shrinking of Safe Operation Area of Metallized Film Capacitor Considering Electrothermal Coupling and Aging in Power Electronics Applications

Metallized film capacitor (MFC) selection is a key step to ensure the safe and reliable operation of high-capacity power electronic equipment. However, as a crucial factor leading to overstress failure, the changing of stress tolerance boundary caused by parameter drift and insulation deterioration has not been fully considered in capacitor design. Therefore, the establishment method of MFC lifecycle safe operation area (SOA) is proposed innovatively in this article, which characterize the shrinking of safe operating boundaries caused by electrothermal coupling and capacitor aging during long-term operation. First, the failure mechanisms and electrothermal coupling of MFC are discussed and the modeling process of initial SOA is proposed based on the datasheet and dc withstand voltage experiments. In addition, considering the increase of the hot-spot temperature and the decrease of breakdown strength due to equivalent series resistance parameter drift and dielectric insulation degradation in the whole lifecycle, the shrinking of MFC operation boundary is characterized and the modeling process of lifecycle SOA is proposed based on aging model and lifetime tests. Finally, taking an MMC system as example, the MFC selection based on practical operating condition and lifecycle SOA is performed to verify the validity of the method.

Partial Discharge Characteristics of Biaxially Oriented Polypropylene Film Under Nanosecond Pulse Voltage With High Rise Rate

Film capacitors are used as the key energy storage device for high-power pulse generators, and partial discharge (PD) is an important factor leading to insulation degradation and the failure of film capacitors. To accurately evaluate the effect of the rise rate on PD under nanosecond pulse voltage, experiments on the PD characteristics of biaxially oriented polypropylene (BOPP) films under high rise rate (18.3–140 V/ns) nanosecond pulse voltages were carried out. The PDs were mostly concentrated in the rising and falling phases of the nanosecond pulse. Moreover, the repetitive PD inception voltage (RPDIV) first increased and then remained unchanged as the rise time increased, and the PD amplitude and the number of discharges in the rising phase of the nanosecond pulse increased as the rise rate increased, while the discharge time lag decreased gradually. The PD amplitude, the number of discharges, and the discharge time lag in the falling phase of the nanosecond pulse all increased as the rise rate increased. In addition, when the rise rate was less than 50 V/ns, the PD statistical characteristics changed rapidly as the rise rate increased, and when the rise rate was greater than 50 V/ns, the PD statistical characteristics gradually changed more slowly as the rise rate increased.

Hybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applications

The intermittent nature of renewable energy sources (RESs) hamper their integration to the grid. The stochastic and rapid-changing operation of RES technologies impact on power equipment reliability. Transformers are key integrative assets of the power grid and it is crucial to monitor their health for the reliable integration of RESs. Existing models to transformer lifetime estimation are based on point forecasts or steady-state models. In this context, this article presents a novel hybrid transformer prognostics framework for enhanced probabilistic predictions in RES applications. To this end, physics-based transient thermal models and probabilistic forecasting models are integrated using an error-correction configuration. The thermal prediction model is then embedded within a probabilistic prognostics framework to integrate forecasting estimates within the lifetime model, propagate associated uncertainties and predict the transformer remaining useful life with prediction intervals. Prediction intervals vary for each prediction according to the propagated uncertainty and they inform about the confidence of the model in the predictions. The proposed approach is tested and validated with a floating solar power plant case study. Results show that, from the insulation degradation perspective, there may be room to extend the transformer useful life beyond initial lifetime assumptions.

Evaluation of Long-Term Boiling Water Immersion as Artificial Aging Test of Silicone Rubber Insulators

Our country IRAN have long coastal area with length of more than 1830 km. Utility companies with 30 years of experience in using polymeric insulators in this very heavy polluted area. Operational history in these areas indicates many insulating problems. With the advancement of polymer insulation technology, the utilities want to select the longest life insulators for these areas. Laboratory studies accelerated testing of polymer insulators is the only available method to investigate the trend of insulator degradation during the aging process, because it takes a long time for noticeable changes and signs of aging to appear on insulators in the power grid. Different methods such as salt-fog, UV and boiling water are used to test artificial aging, each method has advantages and disadvantages. In our experience, boiling water is used as a low-cost method in compared to the other methods especially salt fog test. The aim of this research was to determine a shorter method for evaluation of SR insulators. In this paper, the accelerated aging of polymer insulators using salt-fog and boiling water immersion tests are investigated. During and after the aging test, the polymer insulator's electrical quality is analysed using leakage current measurement tools, partial discharge, and hydrophobicity. Surface changes are also analysed by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX), Thermo Gravimetrical Analysis (TGA), and Fourier Transform Infrared Spectroscopy (FTIR) both qualitatively and quantitatively.

Loop Optimization Noise-Reduced LSTM Based Classifier for PD Detection

Partial discharge (PD) is an important inducement of power failures such as insulation degradation. Effective, timely, and economical detection of the PD is the basis for maintaining power system stability. This paper proposes a loop optimization noise-reduced LSTM based classifier for PD detection of large-scale data disturbed with measurement noise. It establishes the loop optimization to greatly coordinate the data processing and data-driven based machine learning algorithm. The noise reduction, feature extraction, and the long short-term memory (LSTM) neural network are combined as a whole with internal linkages. The proposed method combines these three necessary steps to achieve a better solution for the PD detection, while other methods are in general single ones, and not dedicated designed for the solution. The combination of the different methods has combined advantages and can overcome each individual's drawback. This comprehensive solution is verified using large real trial data measured from medium voltage (MV) overhead power lines. It exhibits superior performance and great practicality for PD detection of MV overhead power lines with an accuracy of 95.4%. It outperforms other techniques in the literature based on the experimental results. This paper also summarizes the loop optimization rules for PD detection solutions based on extensive experiments and analysis. The loop optimization rules are competent for the optimization of wider engineering applications of PD detection.

Long lifespan thermal barrier coatings overview: Materials, manufacturing, failure mechanisms, and multiscale structural design

A long lifespan is a basic support for the thermal protection of thermal barrier coatings (TBCs). Plasma-sprayed TBCs provide high thermal insulation and cost-effective advantages; thus, they are used in cutting-edge areas, such as land-based gas turbines. However, following thermal exposure, sintering causes structural changes and significant degradation in thermal insulation and strain tolerance. Thus, plasma-sprayed TBCs often show poor performance in thermal cycling tests. Therefore, extending the lifespan of plasma-sprayed TBCs is crucial. This study reviews the structural design of TBCs for a long lifespan. The main contents reviewed include the following aspects: (1) the basic structural features of plasma-sprayed TBCs with high thermal insulation and strain tolerance in an as-deposited state; (2) the dynamic structural evolution during thermal exposure to reveal the degradation mechanism in thermal and mechanical properties; and (3) multiscale structural design of TBCs to achieve a long lifespan. This review aims to contribute to the development of advanced gas turbines.

Measurement of Moisture in Transformer Insulation Using the Intelligent High-Frequency Sensor System

High-frequency sensor-based system is proposed for evaluating the degradation of the transformer insulation through the measurement of moisture concentration. The proposed system employs an intelligent open-ended coaxial probe microwave technique. An optimized ANN model is designed by selecting appropriate features to obtain accurate dielectric characterisation. Redundant feature elimination reduces the computational burden and also bypasses the repeated calibration refresh required to complete RF measurement in the conventional technique. ANN model is trained in such a way that measured impedance mismatch due to the termination by the sample at the probe end is related to its permittivity. The introduction of an intelligent standardization procedure led to a low error in the measurement of moisture (< ±3%) with a sensitivity of 0.027/ppm. This method is found to be simple and faster compared to other existing techniques. Due to the advantage of high-speed measurement and low error, this sensing technique is very suitable for transformer condition monitoring.