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.
