In this paper, a comprehensive evaluation method on the insulation performance and greenhouse effect of environmentally-friendly gas was studied. Firstly, considering that the gas greenhouse effect is determined by its atmospheric lifetime and infrared characteristics, and can be assessed by the global warming potential (GWP), this work establishes a regression prediction method for atmospheric lifetime based on the highest occupied orbital energy, combined with molecular infrared spectroscopy theory. The model realizes GWP calculation of any gas, and the maximum error less than 19%. Secondly, this work starts from the collision phenomenon and establishes a theoretical evaluation method. Under the classical collision theory, the collision is reduced to potential scattering, and the molecular electrostatic potential is calculated. It is found that there is a strong correlation between positive potential surface area of small molecules and insulation, which can be used for quantitative analysis. Besides, the collision model considering the quantum effect is studied, and finding that the low-energy resonance phenomenon can be used for qualitative analysis, for the results calculated by R matrix method of 15 gases show that only strong insulation gases such as SF6 can exhibit low-energy resonance.
Accurate and reliable fault analysis is essential to improve the performance of gas-insulated switchgear (GIS) and ensure the safe and stable operation of the power system. Advances in the perception and measurement technology of power primary equipment have increased the scale of GIS operation and fault data. Driven by massive samples, deep learning has brought new opportunities for GIS fault identification and intelligence diagnosis. As an unsupervised learning method, stacked autoencoder (SAE) can automatically extract representative expressions and in-depth features from massive unlabeled data. Consequently, it can effectively alleviate the problems of incomplete data category labels and limited data samples. Meanwhile, transfer learning can avoid the need to train the model from scratch under the new dataset. For this reason, this paper combines SAE with transfer learning to realize partial discharge (PD) pattern recognition of GIS in order to solve the problem of complex and variable and large randomness of the on partial discharge samples. Unsupervised greedy layer-wise pre-training and supervised fine-tuning are leveraged to train the SAE model. The experimental data was generated using four types of PD in the laboratory. The optimal SAE structure is trained under the source domain, and the GIS PD pattern recognition under the target domain is realized through transfer learning. Experimental results show that the proposed method has stronger robustness and generalization while improving the accuracy of partial discharge pattern recognition of GIS.
Excitation temperature of atoms attaches importance to understand the nature of vacuum arc plasmas. The traditional methods present the temperatures in one or two dimensions in space, but the results of the temperature distribution in three dimensions are crucial for understanding asymmetric plasmas. To address the issues, a 3D temperature reconstruction algorithm was proposed in this paper. First, a 3D tomographic optical platform based upon split fiber bundles was built to record the multi-angle arc images of CuI. Then, the 3D emissivity distribution at 510.6nm and 515.3nm was reconstructed, wherein A tomography algorithm named MLEM-SB was utilized. After that, the 3D excitation temperature distribution of CuI was calculated by the two-line method. The results show that the emissivity was higher at the center of the arc and lower at the edges, exhibiting characteristics of radial non-uniformity, and the emissivity at 510.6nm increases gradually from cathode to anode, while the emissivity at 515.3nm first increased and then decreased. With control of AMF, the maximum excitation temperature of CuI in the arc column was 9060 K at a peak current of 4 kA, and the temperature was higher at the center of the arc and lower at the edges.
In high current vacuum arcs, the contact materials fundamentally decide the arc behavior. The objective of this work is to experimentally study the influence of anode contact materials on the anode spot formation. A pair of Helmholtz coils is set to generate axial magnetic fields. A high speed video camera is used to record the arc evolution. The anode spot formation and the threshold currents for different anode materials are studied. To eliminate other factors that may affect the anode spot formation. The discharge geometry and the external axial magnetic field remain the same. In the experiments, four anode contact materials of Fe, Cr, Mo and W are used. Moreover, the cathode material remains Cu. Therefore, four cathode-anode materials sets are Cu-Fe, Cu-Cr, Cu-Mo and Cu-W. The anode spot threshold currents for these four materials sets are 9.5 kArms, 11.2 kArms, 14.1 kArms and 16.3 kArms, respectively. Compared to the previous work, the impact of cathode materials is eliminated. The experimental results could directly reflect the influence of the anode materials on the anode spot formation.
Conventional magnetic contactors are mostly composed of a solenoid magnetic actuator. However, the continuous energy consumption of the closing coil to maintain the closed state brings problems such as inefficient use of energy and heat generation. To cope with these problems, a permanent magnet type AC magnetic contactor is designed in this paper. An improved optimization algorithm is used to optimize the contact surface of the MC. Optimization algorithm and optimized results are shown.
Modular Green Substation means combination of Modular Substation and Green Substation. Modular substation is a substation in which all of its facilities will be manufactured in modular type, stored in a container, and transported to a trailer. Green substation is a substation equipped with compensation facilities such as ESS, and FACTS to link renewable energy to power system by guaranteeing power quality. This paper deals with plans for development and application of Modular Green Substation to cope with emergencies in the case of substation failure and to improve the acceptability of renewable energy.
To have high reliability for interrupting, it is necessary to design a circuit breaker by performing a transient analysis on the nonlinear arc characteristic that occurs during DC interrupt based on simulation. The black box arc model expresses the interaction between an arc and a circuit and can represent the nonlinear characteristics of an arc using a differential equation based on the electrical conductivity of the arc. In general, the existing black box arc models are widely used in AC system. However, few DC arc models can be used in DC system. This paper simulates the applicability evaluation for DC circuit breakers using KEMA and Habedank black box arc models based on the actual low voltage DC circuit breaker test data sheet. When designing a DC circuit breaker, parameters of the black box arc model were set by performing Parameter Sweep for the main factors, such as interrupting time, arc voltage, and arc time constant. Based on this, the DC arc behavior was predicted, and the voltage and current waveforms of the black box arc model were compared and analyzed. As a result, the applicability of the black box arc model to the DC circuit breaker was verified, and a more suitable black box arc model was proposed to analyze the DC arc behavior.
Since the converter transformer in the HVDC system is affected by both AC and DC voltages differently from the conventional converter transformer, a new isolation problem appears. As the DC voltage is applied to the valve winding of the converter transformer, the press board insulation on the valve side of the converter transformer must withstand the mixed AC voltage and DC voltage. In addition, high frequency harmonics are generated in the process of converting DC voltage to AC voltage through the converter transformer. High frequency AC voltage is mixed with DC voltage, eventually the mixed voltage can cause insulation breakdown in the press board. However, many studies have been conducted on the insulation characteristics of press boards under AC or DC voltage, but research on the insulation characteristics of press boards under AC-DC combined voltage are insufficient. Therefore, it is necessary to study the insulation characteristics of pressboard under AC-DC combined voltage. In this paper, frequency and ripple factor were selected to investigate the insulation characteristics of pressboard under high frequency and AC-DC combined voltage. Experiment results, confirmed that the dielectric strength decreased linearly as the ripple factor increased, and the breakdown voltage decreased as the frequency increased. Consequently, it has been analyzed that the valve-side insulation of the converter transformer is more affected by frequency and AC-DC combined voltage.
Convolutional neural network, as a research hotspot at present, is widely used in the field of fault diagnosis because of its effective automatic feature extraction ability, but it is still a difficult problem to integrate various information for comprehensive diagnosis. In order to realize multi-information fusion and comprehensive diagnosis of its mechanical faults, a fusion fault diagnosis method using multi-channel vibration signals to drive an integrated convolutional neural network is proposed. First, a multi-channel ensemble convolutional neural network based on fusion of multiple data is proposed. Aiming at the problem of small sample size in engineering practice, a transfer learning method is proposed. Finally, the effectiveness of the proposed method is verified by establishing a high-voltage circuit breaker fault acquisition system to obtain the mechanical fault signal of the high-voltage circuit breaker. Experimental results show that this method has many advantages compared with traditional deep methods.
High-voltage gas circuit breakers (GCBs) are widely used in power systems in order to interrupt fault current. As more equipment ages, it is becoming increasingly important to diagnose and maintain their condition to preserve the reliability of power systems. Inexpensive, non-contact acoustic diagnostics have been developed for automating the maintenance and inspection of power systems. In this study, we developed a system for acquiring major signals such as the stroke curve, contact (touching point), and motor current along with acoustic data in order to estimate the opening and closing operations of GCBs under normal conditions. The features of the frequency spectrum, which correspond to the start and end instants of the stroke, were stored in advance. These instants were estimated by comparing the correlation coefficient between the stored feature spectrum and the measured one. The resulting estimations were within an error of about 2%, making the acoustic diagnostics suitable for monitoring GCB operation.
The contribution presents the results of comparative study of typical RMF and AMF contact systems used in vacuum interrupters at typical operation conditions. An AC current pulse with a peak value up to 28 kA and frequency about 50 Hz was used. Electrodes were made of Cu-Cr. In addition to conventional arc current and voltage measurements, various optical diagnostics have been used. The arc dynamics was observed by a high-speed camera. Near infrared radiation (NIR) spectroscopy determined the anode surface temperature after current zero crossing. During the active phase, a highspeed camera equipped by a narrow band filter was applied for acquisition of qualitative distribution of the anode surface temperature. In addition, the density of neutral chromium vapour close to the current zero crossing was measured by means of broadband optical absorption spectroscopy. Three Cr I resonance lines at 425.43 nm, 427.78 nm, and 428.97 nm are used for the analysis. Special attention was put on the behavior after current interruption. The results for measured temperature evolution of anode surface temperature along with the corresponding Cr density are presented and discussed.
After replacing a certain amount of bisphenol A epoxide resin with epoxidized soybean oil (ESO), an eco-friendly bio-material, it was cured by adding an acid anhydride curing agent and a filler. The properties of cured product was measured using a Differential scanning calorimeter (DSC), universal test machine (UTM), AC and DC dielectric breakdown tester. The glass transition temperature (Tg), tensile strength, fracture toughness (KIC) and electrical characteristics that change with increasing ESO content were investigated. As the ESO content increases up to 30 wt.%, the glass transition temperature, tensile strength and electrical properties do not fall short of the target properties for medium voltage insulation applications. Therefore, it is possible to replace diglycidyl ether bisphenol A epoxy with ESO up to 30wt.% for eco-friendly indoor epoxy compounds.
In high-voltage gas circuit breakers (HVCBs), high-pressure gas is blown between the electrodes to cool the arc. The temperature distribution between the electrodes at the current zero point has a large gas temperature difference. This study aims to experimentally clarify the effect of the cold gas region on the breakdown voltage of gases with large temperature differences. In an experiment, a hot gas generator using arc heat was used to create a gas space with a large temperature difference between electrodes, and the breakdown voltage was evaluated. The breakdown voltage in the gas space with a cold gas region was twice that of the case, where the space between the electrodes was filled with hot gas. It was found that the discharge initiated in the hot gas region was suppressed in the cold gas region and that the breakdown voltage in a gas space with the hot and cold region was influenced by the existence of the cold gas.
In this paper, temperature distribution of GIS that filled with eco-friendly gas is predicted through numerical analysis when the rated current flows. Temperature rise is predicted using electromagnetic-thermal coupling analysis. The heat sources causing the temperature rise are joule losses in the main conductor during the flow of rated current, and eddy current losses of enclosure caused by the magnetic flux interlinkage created by the rated current and are calculated by electromagnetic analysis. The calculated power losses are used as the heat source of analysis to predict the temperature rise of GIS. The validity of prediction method is verified by comparing with the test results.
The breaking capability of a circuit breaker depends on the duration it takes for the switching arc to quench. In order to reduce this duration, outgassing polymers are used in circuit breakers. These polymers release gases upon interaction with the switching arc which further acts as an energy sink and quenches the arc quicker than non-gassing materials. The outgassing of polymer occurs as a result of ablation of these materials; hence these arcs are called ablation-dominated or ablation-controlled arcs. This work focuses on the numerical study of arc formed between two metal electrodes inside a polyamide enclosure performed using VizSpark®, a high-fidelity thermal plasma simulation software. The results are validated against the experimental research literature for current amplitude 1.1 kA and 3 kA respectively. We report temperature, pressure and electrical conductivity maps during arcing.
SF6 is one of the most serious greenhouse gas with global warming potential of 23900, and mainly used as the insulation gas inside GIS(Gas Insulated switchgear). So KEPCO is trying to adapt alternative eco-friendly gases instead of SF6. Among alternative gases, the gad mixture of C4F7N and CO2 is focused for GIS uses because of its good insulation property, adequate boiling point and moderate GWP and toxicity. Firs we constructed the measurement process of mixing ratio using GC and standard mixing gas with uncertainty of 0.006%. Next, for the purpose of the GIS diagnosis, the partial discharge pattern from various defects is obtained and compared that of SF6 or CO2. We also studied the decomposition propertied after the thermal and arc aging. CO is found out as major by-product. So we decided to check out the gas aging through the CO concentration. Finally the breakdown voltage according to the moisture concentration is measured to determine the limitation of moisture concentration inside GIS. We have plan to use properties of gas mixture for maintain eco-friendly GIS.
Since vacuum arc is usually considered as non-LTE plasma, it is reasonable to believe that the temperature of ions is different from the temperature of other particles whereas it still remains unknown. To achieve a better understanding of the vacuum arc, we measure the excitation temperature of Cu II (monovalent Cu ions or Cu+) by spectroscopic measurement and multiple-image optical measurements. As a result, the Cu II ions follow the Boltzmann distribution, and its excitation temperature has a negative linear relation with the gap length under diffuse mode at the peak of alternating current about -500 K/mm. In addition, the temporal evolution of excitation temperature does not follow the current's sinusoidal wave strictly and it can be divided into 5 phases: (1) rapidly rising phase, (2) slight drop phase, (3) steady phase, (4) slow drop phase, (5) rapid drop phase, which could be simulated by square wave roughly. Besides, the 2-D distribution of excitation temperature shows that the cathode and anode have a higher temperature than inter-electrode space, as for the interelectrode space, the excitation temperature increases from the vicinity of the cathode to the anode gradually.
SF6 gas has excellent electronegativity, so it is widely used in electrical insulation, but SF6 gas can cause serious safety hazards due to leakage, so timely detection of leaking gas is of great significance to the safe operation of power equipment. Nowadays, the efficiency of SF6 gas leakage detection is low, especially in complex environments where it is difficult to perform real-time detection. Therefore, this paper proposes an intelligent SF6 gas detection method based on image recognition, and compares it with other 2 mainstream detection models. The results show that the detection accuracy of this method is better than that of GMM and Faster R-CNN models, and the detection accuracy can reach more than 88%.
SF 6 gas has excellent insulation performance and stability, so it is widely used as an insulation material for high-voltage gas insulation systems of power equipment in the transmission and distribution industry. However, the GWP (Global Warming Potential) of SF 6 gas is very high at 23,900, limiting its use in the power industry. Therefore, research has been conducted for a long time to reduce the amount of use or to find an alternative gas. Alternative gas candidates include CO 2 , N 2 , dry air gases and C 5 F 10 O and C 6 F 12 O. Among them, CO 2 has a low GWP of 1 and a low boiling point, so it is easy to handle, and the arc extinguishing performance applied to GIS is relatively superior compared to other gases, and it can be mixed with O 2 to improve insulation performance. Therefore, it is being actively considered as a promising alternative gas.In general, replacement gas has less than half the dielectric strength of SF 6 gas, so the insulation distance between conductor and enclosure should be longer. In other words, the size of the enclosure that stores the gas is inevitably increased. However, due to cost and product size limitations, manufacturers are gaining insulation through activities such as increasing gas pressure or controlling other influencing factors. Since the critical electric field strength of the gas gap is expressed as the product of the gas pressure and the surface roughness, the surface roughness of the conductor under the ultra-high voltage condition is treated as an important factor affecting the insulation performance. Therefore, in order to secure sufficient insulation performance in eco-friendly products, it is necessary to check the insulation performance change according to the surface roughness and reflect this in the insulation design. In this paper, the effect of the surface roughness of the conductor on the breakdown voltage under the conditions of CO 2 , O 2 mixed gas among eco-friendly gases and SF 6 gas was analyzed and tested.
With the ever-increasing demand on electrical energy, safe and reliable power generation gains more and more importance. Specially designed and tested generator circuit breaker (GCB) according to IEC/IEEE 62271-37-013 is a must have when proper protection of power generation units (power plants, power supply for oil rigs etc.) is required. The reason for such specialty is the exposure of GCBs to h...
