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%.

Generally, the fault current supplied by inverter-based renewable energy sources (IBRES) and electrical storage systems (ESS) is about 1.2 to 2 times their rated current and much lower than synchronous generators because the former acts as a current source and the latter acts as a voltage source. A conventional power system in a small island is composed of only synchronous generators and protected from short circuit faults using an overcurrent relay (OCR). However, in the remote microgrid with IBRES, ESS, and synchronous generators, the fault current varies depending on the configuration of generation sources. Namely, the fixed OCR protection method cannot protect microgrids from short circuit faults. This paper proposes an adaptive protection method to protect the microgrid from faults by actively changing the OCR setting according to the state of the generator source combination. A microgrid with ESS and a synchronous generator is modeled and simulated through PSCAD/EMTDC software to validate the proposed adaptive protection method.

The expected increase in renewable energy sources (RESs) and electric vehicles (EVs) connected to distribution systems will result in many technical constraints. A meshed network is a promising solution; however, some remarkable challenges must be overcome. Among these, this paper mainly focuses on the line overload and short circuit current of a networked distribution system (NDS) in Korea, an advanced form of meshed network. An NDS refers to a system in which there exists permanent linkages between four feeders and N × N communication-based protection. We propose a method, which employs the tap changing control algorithm of the series reactor to control line overload and short circuit current. MATLAB/Simulink was used to evaluate the proposed method. Three different types of distribution system were employed. First, the utilization rate and feeder imbalance were analyzed in steady-state condition. Subsequently, the short circuit current was analyzed in short circuit condition. The results revealed that the proposed method can effectively prevent line overload in up to 82.7% of cases, enhance the utilization rate by up to 79.9%, and relieve the short circuit current; that is, it can contribute to system stability and the economic operation of an NDS.

Distributed generations, using solar photovoltaic (PV) generation systems, are generally connected to ungrounded distribution systems to ensure operational continuity and avoid electro-chemical corrosions. The ungrounded power distribution system possesses an advantage of continuous operation regardless of primary fault occurrence due to a small fault current. Conversely, a subsequent secondary fault can induce a large fault current representing that of other electrical grounding types, resulting in inevitable power shutdowns. As preventative methods, both insulation status monitoring and primary failure detection have become of high importance. This paper presents a method enabling the cooperative use of IMD (Insulation Monitoring Device) and GPT (Ground Potential Transformer) in an ungrounded distribution system connected with a transformerless inverter. Moreover, factors leading to errors during IMD insulation monitoring, CLR (Current Limit Resistor) burnout of a GPT, and malfunctions of related protection devices are presented. Furthermore, a method for selecting the inductor and capacitor in consideration of the operating characteristics of IMD and GPT is discussed. The proposed cooperative operation method enables the accurate measurement of insulation resistance using IMD, while concurrently reducing the constitutively induced zero-sequence voltage in the CLR of a GPT to prevent CLR burnouts and malfunctions of connected protection devices. Hence, the method is anticipated to contribute to the stable operation of alternating current (AC) and Direct Current (DC) combined systems connected with transformerless inverters.