In recent years, many accidents occurred worldwide owing to Buchholz relay maloperation and failure to act in time, which significantly compromised the security of power systems. To address this problem, an experiment based on a 25 MVA/110 kV oil-immersed power transformer setup was performed to characterize the response of a Buchholz relay; gas-generation faults of different severity inside the transformer's oil tank were simulated by controlling the pressure and volume of injected gas. The experimental results reveal that the gas accumulation relay has high sensitivity but insufficient speediness. Further, the oil surge relay is relatively fast but low in sensitivity, whose action threshold is affected by static pressure difference. Based on the different characteristics of non-electric parameters under internal and external faults, a digital non-electric parameter protection method, which exhibits good performance, is devised. The findings of this study can lay the foundation for optimizing the design and evaluation of the Buchholz relay, and for developing non-electric parameter protection devices.

The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

Zinc oxide arresters, a major type of overvoltage protection equipment in power systems, can suppress transient lightning overvoltage, operation overvoltage, and other types of power surges. However, the response characteristics and protection effects under nanosecond pulses, such as those in a high-altitude electromagnetic pulse conduction region, remain unclear. Therefore, the surge protection performance of arresters should be properly understood, and high-performance arresters should be developed. In this study, critical parameters, including overshoot peak voltage, residual voltage, response time, and voltammetry characteristics of a representative 10 kV ZnO arrester under nanosecond pulses, were obtained using a double exponential pulse current injection source. The overshoot peak voltage of this arrester under a nanosecond pulse is 2.19 times of lightning impulse residual voltage, and the residual voltage ratio under a nanosecond pulse is 4.31. The average response time is approximately 45 ns, and the response time is independent of the nanosecond pulse amplitude. Furthermore, an electromagnetic transient model of this type of lightning arrester is established based on the CIGRE guidelines, as well as a method to determine the key parameters. The model can simultaneously account for the response characteristics of the small current region and the conduction region of the arrester, which properly agrees with the experimental results.