This paper deals with a review of the state-of-the-art performance investigations of green gas for grid (g3) gas, which is an emerging eco-friendly alternative insulation gas for sulfur hexafluoride (SF₆) that will be used in gas-insulated power facilities for reducing environmental concerns. The required physical and chemical properties of insulation gas for high-voltage applications are discussed, including dielectric strength, arc-quenching capability, heat dissipation, boiling point, vapor pressure, compatibility, and environmental and safety requirements. Current studies and results on AC, DC, and lightning impulse breakdown voltage, as well as the partial discharge of g3 gas, are provided, which indicate an equivalent dielectric strength of g3 gas with SF₆ after a proper design change or an increase in gas pressure. The switching bus-transfer current test, temperature rise test, and liquefaction temperature calculation also verify the possibility of replacing SF₆ with g3 gas. In addition, the use of g3 gas significantly reduces theabovementioned environmental concerns in terms of global warming potential and atmosphere lifetime. In recent years, g3 gas-insulated power facilities, including switchgear, transmission line, circuit breaker, and transformer, have been commercially available in the electric power industry.

Although the gas insulated structures have a high degree of reliability, the unavoidable defects are primary reason of their failures.Partial discharge(PD) has been regarded as an effective indication for condition monitoring and diagnosis ofgas insulated switchgears(GISs) to ensure their reliable and stable operation. Among various PD detection methods, theultra-high frequency(UHF) technique has the advantages of on-line motoring and defect classification. In this paper, there are presented 7 types of artificial electrode systems fabricated for simulation of real insulation defects in gas insulated structures. A real-time measurement system was developed to acquire defect patterns in a form ofphase-resolve partial discharge(PRPD) intensity graph, using a UHF sensor. Further, the discharge distribution and statistical characteristics were extracted for defect identification using a neural network algorithm. In addition, a conversion experiment was proposed by detecting the PD pulse simultaneously using a non-induction resistor and a UHF sensor. A relationship between the magnitude of UHF signal and the amplitude of apparent charge was established, which was used for evaluation of PD using the UHF sensor.

This paper deals with the characteristics of partial discharge (PD) in green gas for a grid (g3), which is thought to be a promising eco-friendly alternative to substitute SF₆ used in electrical power facilities and to reduce the greenhouse effect. g3 gas with 4% NOVEC^TM 4710/96% CO₂ was used and electrode systems including a protrusion on conductor (POC) and a protrusion on enclosure (POE) were fabricated to simulate PD in a gas-insulated structure. PD characteristics in terms of partial discharge inception voltage (PDIV), pulse parameters in time and frequency, and the phase-resolved partial discharge (PRPD) pattern in SF₆ and g3 were compared. From the results, the PDIVs of g3 were 76%–81% of that of SF₆ in the POC and were 78%–84% of that of SF₆ in the POE, depending on the gas pressure. Rising time, pulse width, and relative amplitude in the frequency domain of PD pulses in g3 gas were greater than those in SF₆. In addition, the PRPD patterns indicated that both the average apparent charge and pulse count of PD in g3 were higher compared with those in SF₆. The results from this paper are expected to provide fundamental material for the green manufacturing of gas-insulated power apparatus.

Detection and analysis of series arcs is significantly meaningful for preventing arc-caused electrical fires in advance. However, the improvement of arc detection sensitivity and the discrimination of arc conditions are still challenges when developing an arc fault detector. In this paper, arc signals in various loads with three major incomplete connection states were detected and further analyzed using the discrete wavelet transform. It was verified that the db13 was the optimal mother wavelet to analyze the arc pulses and the decomposed signals in the detail components of D5, D6, D7, and D8 were related with arc phenomena. Therefore, a band pass filter with a frequency from 2.4 to 39 kHz was designed, which can extract arc signals while eliminating the AC mains current and noise generated in loads. By investigating the arc signal energy as well as the arc pulse counts that were important parameters of arc occurrence, an arc diagnosis algorithm was developed based on LabVIEW program for electrical fire prevention.