A method to evaluate the accuracy of space-charge measurements by estimating the electric field distribution in the insulating walls of cables is proposed in this work. Time-domain deconvolution is used to derive the stable and fully automatic processing techniques necessary to manipulate the large data required to perform the statistical analysis. The evaluation method optimizes the deconvolution parameters and analyzes the space charge waveform, which is usually only used as a reference waveform. The proposed method was validated by analyzing pulse electroacoustic waveforms obtained from a 66 kV-class extruded cable under dc voltage application. The impulse response of the observation system was optimized to provide a 95.4% confidence interval of 0.8% ± 24.1% for the waveforms, which were observed under application of a dc voltage of 30 kV. Similar estimation accuracy was confirmed for waveforms measured under application of 45 kV. Therefore, the accuracy estimated using the reference waveform can be applied to waveforms observed at high voltages that can induce both space-charge accumulation and field distortion.
The likelihood of electrostatic discharge due to the accumulation of charge from the space environment depends strongly on the conductivity of spacecraft dielectrics. Spacecraft polymers are often filled with inorganic substances including glass fibers, glass beads, and thickening powders in order to modify their mechanical properties. This study examines the impact of these fillers on the conductivity of a few common spacecraft polymers. We show that the impact of inorganic fillers varies greatly from one usage case to another.
The potential application of eco-friendly thermoplastic material - Polypropylene (PP) for high voltage direct current (HVDC) cable has attracted significant attention. However, a better understanding of the ageing effects of on the long-term performance of PP is needed for a reliable operation of a HVDC cable. Based on previous work on thermal ageing of neat PP, an antioxidant (AO) has been introduced to investigate its influence in PP thermal-oxidative ageing for a long potential service life (>40 years) under high temperature in this work. According to chemical changes characterized using Fourier-Transform Infrared (FTIR) spectroscopy, the AO is initially ready to react with oxygen on sample surface and subsequently progresses towards the bulk of the sample over time. The space charge dynamics were measured with the pulsed electroacoustic (PEA) technique. It has been found that hetero charge formed near the cathode would further increase after adding AO, but negative charge becomes dominant with continuous poling time. After thermal ageing experiments are completed, there is an obvious charge injection near the cathode. Combined with the positive charge movement caused by the AO, a resultant charge peak can be observed. We correlate this behavior with DC breakdown strength experiments, which show an increase at an early stage of the ageing process, before decreasing as a function of ageing time.
Crosslinked polyethylene (XLPE) is a key material for power cable insulation due to its superior electrical properties. It is co-extruded with a semiconducting “semicon” layer, which is significantly more conductive than XLPE due to the incorporation of carbon black. Understanding the electrical properties of the XLPE/semicon bilayer and the interface between them is critical due to their common use. Two techniques were used to study electrical properties of XLPE and XLPE/semicon bilayers. One was current-voltage measurements, which analyzed resistivity as a function of time. The other was high-voltage polarization, which analyzed polarizability and dielectric loss as a function of electrical field. Thin films of PE and semicon were made via melt pressing. Dicumyl peroxide was infused to the beads prior to melt pressing such that XLPE was formed. All samples were degassed to remove DCP byproducts. Semicon/polyethylene bilayers were made by pressing the two layers together. LDPE and LDPE/semicon were also studied. Bulk brass electrodes were used. Current-voltage measurements showed that adding the semicon layer increased conductivity by an order of magnitude. Polarization measurements revealed that significant enhancement in the dielectric loss occurred at electric fields of 15 kV/mm and above. Polyethylene by itself and semicon bilayer samples had distinct electrical properties, and thus a proper understanding of the performance of XLPE in power cables required analyzing both. Further work is suggested that focuses on correlating changes in measured conductivity to the nature of the material interface (e.g., via enhanced charge injection and/or introduction of charge carriers).
Transformer bushing is one of the transformer current-carrying components. Transformer bushing failure will cause the oil tube lead role to be damaged, which is not conducive to the normal operation of the transformer. In order to accurately predict the bushing temperature and detect potential transformer faults in a timely manner, a temperature prediction model based on historical temperature monitoring data was proposed using Particle Swarm optimization (PSO) to optimize Long and Short Term Memory Network (LSTM). The real data was collected continuously for 13 days under the same sensor on a high voltage bushing. After cleaning, the data were used as the input data set for training and test purpose of PSO-LSTM. PSOLSTM method was used to automatically optimize the LSTM parameters, which avoids the problem of low prediction accuracy due to empirical selection of parameters, thus improving the convergence speed and prediction accuracy. The simulation results show that the PSO-LSTM algorithm can complete the bushing temperature prediction within 7s. In terms of temperature prediction accuracy, root mean square error and average absolute error, it outperforms Back Propagation (BP) neural networks, Wavelet Neural Networks (WNN) and single LSTM algorithms, which shows its good application prospect in online monitoring and fault diagnosis for electric transformers.
