Corona-generated audible noise of HVDC transmission lines is a paramount parameter for design consideration. An acoustic source model and a computational technique are proposed to simulate the sound pressure pulse of positive dc corona discharge. A reduced-scale experimental platform with artificial defeat conductor is built up to verify this numerical method and used to study the time-domain characteristics of the audible noise of the single, three and five corona sources, respectively. The measured and computational results show that calculations of sound pressure are reasonable agreement with the measurements. The repetition frequency of the audible noise pulses increases more significantly than their amplitude, as the number of the corona discharge points increase. Furthermore, with the increase of the applied voltage, the repetition frequency and amplitude of the audible noise pulses increases.

The wide application of nanocomposites in the insulation system has greatly contributed to the performance improvement of power equipment. However, nano fillers are not omnipotent for improving the properties of composite dielectrics. In some situations, nano-modified materials are in fact a compromise of improving some performance features while sacrificing others. In this work, the breakdown characteristics and time-to-failure of polypropylene film with nano-clay fillers have been evaluated under combined thermal stress and AC electric fields. Experiments on plain polypropylene (PP) samples have also been carried out under the same test conditions as control. Test results indicated that the time-to-failure of the samples with nano-clay filler was shorter than those without nano filler, which is different from the previous experience. SEM and EDS analyses were conducted to study how the failure mechanism had taken place in both plain polypropylene and the nano-clay filled polypropylene. The failure phenomenon in these materials can be explained by molecular thermodynamics. The main reason for the premature thermal breakdown of PP nanocomposite is essentially due to the weak coupling between nano-clay filler and polymer matrix. Finally, suggestions are proposed for nano modification methods and lifespan prediction models of composite dielectrics.

A pollution flashover along an insulation surface—a catastrophic accident in electrical power system—threatens the safe and reliable operation of a power grid. Silicone rubber coatings are applied to the surfaces of other insulation materials in order to improve the pollution flashover voltage of the insulation structure. It is generally believed that the hydrophobicity of the silicone rubber coating is key to blocking the physical process of pollution flashover, which prevents the formation of continuously wet pollution areas. However, it is unclear whether silicone rubber coating can suppress the generation of pre-discharges such as corona discharge and streamer discharge. In this research, the influence of silicone rubber coating on the characteristics of surface streamer discharge was researched in-depth. The streamer ‘stability’ propagation fields of the polymer are lower than that of the polymer with silicone rubber coating. The velocities of the streamer propagation along the polymer are higher than those along the polymer with silicone rubber coating. This indicates that the surface properties of the polymer with the silicone rubber coating are less favorable for streamer propagation than those of the polymer.

Partial discharge (PD) is widely recognized as the early sign of fatal insulation failure of high-voltage (HV) power equipment and is considered a major factor in accelerating insulation degradation. However, the detection of weak PD signals in noisy environments and the identification of PD modes based on poor-quality data are rather challenging. In this work, using compact and implantable optical detection tools, optical emission spectroscopy (OES) and phase-resolved (PR) ultraviolet (UV) are combined to monitor PD. Based on the obtained dataset, a Transformer-based deep learning model is built and its recognition performance is optimized to realize the fast recognition and classification of spark, corona, and surface flashover, with a precision of 100% and an accuracy of 99%. This capability will help to alarm potential HV equipment failure and assess the risk in advance.

Composite insulators have been widely used in power transmission lines due to their excellent insulation performance. However, it is impossible to completely avoid flashovers in power transmission lines, so it is crucial to ensure the safety and reliability of these lines. To address this issue, the data sets are collected based on the composite insulator surface images, and three convolutional neural networks (CNN) models VGG, GoogleNet and ResNet are established. However, the small number of images in the data set has made it difficult to achieve accurate classification, so the authors also establish three generative adversarial network (GAN) models - original GAN, DCGAN, and WGAN - to fit the existing image data and generate virtual images of the insulator surface to expand the data set. Using transfer learning, the authors obtained pre-trained models and trained them on the expanded data set. By comparing the test results of different models, the authors found the best classification model with an accuracy of 97.5%. This shows that the CNN models can effectively classify the surface images of composite insulators, especially after using the virtual images generated by GAN to expand the data set. The results of this study suggest that these models have potential for engineering applications.

To be able to predict the DC flashover characteristics of composite insulators, a four-layer BP neural network model is established with composite insulator shed structure parameters as the input. Three algorithms (gradient descent with momentum, RMSProp gradient descent, and Adam gradient descent) are applied, and the DC pollution flashover experimental data of composite insulators are used as training data. The results show that all three algorithms have good prediction capabilities. Among them, the Adam gradient descent model has the best prediction result, which can make the average prediction with an error of less than 4% and a maximum error of less than 8%, so these results can provide a reference for the design of composite insulators in DC voltage and product performance verifications.