Partial Discharge (PD) is the main sign of the insulation deterioration, therefore the online PD localization of the power transformers can provide the equipment degradation characteristics and has great value for the power grid. Because of some unique merits, acoustic PD detection is one of the best options for such application. However, when discharges happen inside the windings, precise PD localization by acoustic method becomes very challenging. As the acoustic PD signal can be reverberated and distorted along its propagation, the type and number of sensors, with their installation, should be carefully studied and selected. In this paper, the acoustic wave propagation inside the transformer is explored via numerical simulation. Based on the understanding of wave propagation, a novel-structure fiber-optic acoustic sensor array is properly designed and installed into one phase winding from a real 35 kV transformer. Experimental verification shows that, by using this proposed design, PD localization with less than 5 cm error can be achieved. Therefore, the novel sensor array developed in this paper, together with the installation and localization method, unveils a novel access to accurate online PD localization, especially for those PDs which happen inside transformer windings.

Laminated bus has been widely used in power converters for decades. Thicker insulation increases parasitic inductance, weight and size of the bus and decreases cooling performance and power density. Thus, it is desired to have thinner insulation while achieving the desired lifetime. The emerging medium voltage (MV) SiC-based converters require fast switching transients and high power density, which makes the optimal laminated bus design challenging. Since the internal partial discharges (PD) can significantly degrade insulation, they should be eliminated in the design stage to ensure the optimal performance. However, insulation overdesign does not necessarily help remove PD from internal defects and air pockets in the laminated bus. In order to obtain a PD-free laminated bus with thin insulation, this paper uses representative structures to show a path for removing internal PDs. This paper presents a systematic approach to remove air pockets and reduce the impact of the unavoidable defects in adhesive layers through theoretical and experimental studies. In addition, a proper material selection and processing method is derived to reduce the size and number of internal defects, while lead to a more favorable electric field distribution. The PD experimental results suggest the effectiveness of the proposed approach.

As a consequence of higher blocking voltage and power density by using SiC devices, detailed analysis and careful design of insulation are important for future medium-voltage power converters. Because of the insulation or mechanical coordination, air gaps inside the converters cannot be avoided. Moreover, some gaps are more critical than others, since much oversized design or electric shielding there cannot be acceptable. Admittedly, the final design should be partial discharge (PD) free under normal operation with a safety margin. However, due to possible change of air properties and insulation degradation, massive PD can be still initiated from high electric field regions along the gaps and may lead to the damage of devices, through the highly coupled power and driving circuitry. Therefore, with online PD monitoring and localization, insulation health condition of the critical gaps can be provided, which could have great values for converter operation, maintenance, and design. In this paper, after detailed acoustic signal propagation study, a fiber-optic acoustic sensor array has been implemented into a 6-kV H-bridge power electronics building block prototype, in order to realize online PD monitoring along the critical gaps. Finally, from experimental results, the proposed sensor array has good sensitivity, localization accuracy, and high immunity to the electromagnetic interference. It unveils a novel access to insulation online monitoring for future high voltage, high power density SiC-device-based converters.

Cross-linked polyethylene (XLPE) cables are widely used in power systems due to their excellent electrical and mechanical properties. Space charge is an important factor in cable aging and breakdown. At present, XLPE cables in the power system are mainly AC XLPE cables, so it is necessary to study the space charge characteristics under AC voltage. In this paper, an electro-thermal aging platform for XLPE cable was built and a 10 kV cable was aged. XLPE slices were obtained from the aging cable and AC space charge experiments were carried out on the XLPE slices at different stages of aging. The trap energy levels were calculated and the effects of different electric fields on the AC space charge characteristics were compared. The experimental results show that the trap energy level increases with the aging process and the non-averaged electric field is a necessary condition for charge migration.

A novel extrinsic Fabry–Perot interferometric (EFPI) ultrasonic sensor is developed that can detect ultrasonic signals of partial discharge (PD) in high-voltage electrical equipment under different temperature and pressure conditions. The unique structure of the novel sensor is able to balance the pressure difference between the two sides of the quartz diaphragm driven by the temperature and the density of the insulating medium, and the inner surface of the quartz diaphragm and the end face of the optical fiber in the sensor are coated, thus considerably improving the sensor sensitivity. Experiments are performed on the novel sensor at different temperatures and measurement distances, and the results verify the temperature, frequency, sensitivity and other types of detection performance, showing that the proposed sensor provides a novel means for detecting partial discharge of high-voltage electrical equipment.

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV); hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.