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.

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.

Prediction of lightning occurrence has significant relevance for reducing potential damage to electric installations, buildings, and humans. However, the existing lightning warning system (LWS) operates using the threshold method and has low prediction accuracy. In this paper, an intelligent LWS based on an electromagnetic field and the artificial neural network was developed for improving lightning prediction accuracy. An electric field mill sensor and a pair of loop antennas were designed to detect the real-time electric field and the magnetic field induced by lightning, respectively. The change rate of electric field, temperature, and humidity acquired 2 min before lightning strikes, were used for developing the neural network using the back propagation algorithm. After observing and predicting lightning strikes over six months, it was verified that the proposed LWS had a prediction accuracy of 93.9%.

With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GISs), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase-resolved partial discharge (PRPD) signal generated in GIS spacers based on discrete wavelet transform (WT). Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded, whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified.