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With the economy developing, the environment problems, especially about air quality, have been a hit problem. In the PM2.5 source analysis, coal combustion is one of the important factors in the formation of PM2.5 local source. In this paper, we build network model with PM2.5 data of 366 cities in China from January 2015 to June 2017. Specifically, we construct a symmetric correlation network by partial correlation coefficient and an asymmetric information transportation network by transfer entropy. Then the spatial and temporal characteristics of PM2.5 are studied through time-variant complex networks. Structural control theory of complex networks helps to find the control node sets, which provide suggestions to prevent PM2.5 pollution. Further, we analyze the national power data and the correlation analysis shows the clean energy ratio and PM2.5 concentration are strongly correlated. Finally, control strategy of clean energy for PM2.5 is provided.

C 5 f 10 O has the potential to replace SF 6 gas in medium and low voltage gas insulated switchgear because of its excellent insulation performance and low global warming potential. In the case of partial overheating or discharge in the equipment, whether C 5 f 10 O can recover itself after decomposition is one of the important factors that determine its application prospect. However, there are few researches on the recovery of decomposed C 5 f 10 O by overheating at home and abroad. In this paper, the recovery experiments of C 5 f 10 O were carried out on the existing experimental system of partial decomposition of gas insulating medium. The experimental results show that C 5 f 10 O has a certain self-recover ability after overheating decomposition in the temperature range of 400 °C - 475 °C; the molecular structure of C 5 f 10 O and its main decomposition products is optimized by the hybrid: B3LYP method in the density functional theory, and the reaction paths of CF 3 CFCOCF 3 -, CF 3 - radicals and C3F6 to produce C 5 f 10 O are studied, and compared with the main decomposition reactions of C 5 f 10 O under the same conditions, the self-recover characteristics of C 5 f 10 O were revealed preliminarily in molecular level, which laid a foundation for the industrial application of C 5 f 10 O gas.

Research on the determination of the product gas decomposed by sulfur hexafluoride (SF 6 ) in diverse discharge conditions is of considerable significance for the status evaluation of related electrical equipment. In this paper, the discharge experiment of SF 6 medium was conducted with a needle-plate electrode simulating the operating environment of gas insulated substation (GIS). The products were measured by gas chromatography, ion chromatography and other instruments, by which the appearance moment and content variation of HF, SO 2 F 2 , CF 4 , hydrolysable fluoride and other decompositions generated at 50 kV power frequency are illustrated. The formation of the product at lower energy conditions was investigated as a comparison by the discharge test at 14kV, and further experimental investigation was carried out with the electrode spacing of 0.2cm. This article also analyzed the impact of solid insulating medium (such as polyester film) on characteristic breakdown products by the discharge experiment involving solid insulating medium. The results indicated that HF was the first to be generated despite of the discharge energy, and the detected moment of SO 2 F 2 will be delayed with the decrease of discharge energy, as its concentration promoted when the discharge energy augmented. CF 4 was found in different discharge experiment, and a higher concentration of CF 4 was supposed to be related with the participating of solid insulating media. The experiment launched in this paper considered all forms of discharge fault in SF 6 electrical equipment in general and measured the consistence of breakdown gas of SF 6 correspondingly. The conclusion in this paper could contribute to the beforehand diagnosis and monitoring of sudden failures caused by discharges in SF 6 electrical equipment.

Because of its excellent insulation and arc extinguishing performance, Sulfur hexafluoride (SF 6 ) is widely used in electrical equipment such as gas insulated switchgear (GIS), circuit breakers, transformers. The detection of the components and contents of the decomposition products of SF 6 can provide an effective basis for the running state, abnormality or latent faults of SF 6 gas insulated electrical equipment in time. In order to ensure the accuracy and reliability of the results, this paper analyzed many cases in which SF 6 gas insulated electrical equipment were abnormal or broken-down in recent years, and made an in-depth study on multiple influence factors, such as sampling vessel material, sampling time, detection time, SF 6 decomposition products auto hydrolysis or be adsorbed by adsorbent and equipment structure. The results show that sampling by stainless steel bottles with fluorine coating, comprehensive investigation of the SF 6 gas decomposition products (such as SO 2 F 2 , SOF 2 ) that could characterize the fault, going to the scene as soon as possible or carrying out the inspection work through online device, fully considering the different detailed structure of electrical equipment are of important significance to improve the supervision level of SF 6 electrical equipment and achieve diagnosis and prediction when SF 6 electrical equipment gets fault.

Contact temperature of GIS is the most direct index reflecting contact thermal fault information, so it is necessary to monitor its temperature in real time. At present, it is impossible to install sensors on the contacts to monitor their temperature. So, in this paper, an inversion calculation method is presented for contact temperature of GIS using the monitoring values of multi-point shell temperature and environmental temperature and an optimal layout method of the multi-point sensors installed on the shell is proposed. First, the temperature rise test was carried out on the 500kV real size GIS to obtain the distribution characteristics of internal temperature field. Then, support vector regression machine was used to gain the corresponding relationship between multi-point shell temperature, environmental temperature and contact temperature, and the actual measurement of multi-point shell temperature and environmental temperature was used to obtain the contact temperature by inversion calculation. By comparing the calculated and measured results, the average absolute monitoring error was 0.47%. Finally, the genetic algorithm was used to optimize the layout of the shell temperature sensors, and the number of sensors was reduced from 25 to 5. After optimization, the average absolute monitoring error was 2.07%, which saved the cost and improved the speed of inversion calculation.

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies., to appear in the proceedings of IJCNN'20

Typical composite insulators are widely used in the electric power system, including the composite insulators for the transmission lines, the composite pillar insulators for substations and the hollow composite insulators of transformer bushing. The composite insulators have different operating environment and surface E-field distribution characteristics, so it is urgent to make reasonable evaluation of the operation status. Firstly, the typical composite insulators are observed by the ultraviolet and infrared imaging technology to form the database of electrical-thermal image. The original images are denoised by the wavelet imaging technology, and the image region of the composite insulators is segmented based on the clustering algorithm. The adaptive recognition of composite insulators and the matching of external contours are realized by visual depth learning. At the same time, three-dimensional modeling technology is applied to simulate the electric field distribution law of typical composite insulators, to visualize the E-field in the high field strength area of composite insulators. Finally, the post-processing results of image and the characteristics of electric field distribution are combined to form evaluation strategy of composite insulators operation status . This study has guiding significance for operation and maintenance of composite insulators used in power system.

Monitoring the humidity and pressure(density) of SF6 in electrical equipment has been used as the monitoring and management method for many years, however there is no obvious corresponding relationship between the data obtained by these methods and equipment faults. Now the detection of decomposition product of SF6 gas has been widely applied as a gradually popularized analysis method in the diagnosis of abnormal situation and fault of SF6 electrical equipment. However, factors such as sampling container material, sampling time, detection time, self-hydrolysis or adsorption by adsorbent of gas decomposition products and the structure of the equipment itself will affect the accuracy of the test data and the judgment on operating conditions of equipment. Through many cases dealing with faults of SF6 electrical equipment and the deep research on factors mentioned above, we have effectively improved the supervision level of SF6 electrical equipment. And it is proved that the detection of SF6 gas decomposition product is an effective method for monitoring and diagnosing SF6 electrical equipment, which is of great significance realizing the diagnosis and predicting SF6 electrical equipment fault.

Preparing standard gas materials of trace amount of characteristic decomposition components of SF 6 is the top priority in monitoring the SF 6 insulated electrical equipment online. Making using of quantifying method, we prepared four kinds of standard gas reference material, which were named as CO 2 /SF 6 , CF 4 /SF 6 , SOF 2 /SF 6 , SO 2 F 2 /SF 6 , and they were applied for quantitative calibration in the four major kinds of decomposition components of SF6, that is CO 2 , CF 4 , SOF2 and SO 2 F 2 . The storage problems of the four trace amount of gas reference materials had been considered and solved while the short-term and long-term stability, pressure stability of the gas reference materials were taken into account as the test index. It had been showed that the standard uncertainties of the four kinds of gas reference material were all less than 0.2 %, and the consistency of material, long-term stability in 6 months and the uniformity of them all behaved well, which had met the state second-grade reference. As a consequence, the standard gas reference materials we made could be used in quantitative calibration of the characteristic decomposition components of SF6.

The traditional consistent capacitance method is foundation of the improved design for the Oil-SF6 immersed bushings condenser. Firstly, brief introduction about traditional one has been proposed. The capacitance between adjacent foils can be consistent by adjusting radius and length of every foil, and the consistent capacitance of each layer can be introduced. Moreover, two computing technologies based on Finite Element Method (FEM) have been addressed. One is that how to couple separate electric and thermal calculation process, the other is that how to take material non-linearity into consideration in coupling process. In the paper, for the former difficulties, electro-thermal coupling method has been applied in strong interaction between electric and thermal distribution to calculated the hotspot temperature of the bushing condenser.

With the more and more extensive application of gas insulated equipment, it is imperative to establish an accurate and effective fault diagnosis method for SF 6 gas insulating electrical equipment, which can effectively reduce the failure rate of insulation equipment and ensure the safe and stable operation of power system. In this paper, firstly, the decomposition mechanism of SF 6 , the causes of failure, and several common diagnostic methods based on decomposition component analysis (DCA) were introduced. Secondly, triangle diagnosis method, a new method for fault diagnosis of SF 6 gas insulating electrical equipment was introduced. The three eigenvalues as the axes of the triangle coordinate system was selected, and the experimental data show that within the allowable error range, the data of different fault types can be well clustered into different regions of the triangular coordinate plane. And the diagnostic method was also tested by a large number of experimental data and field failure data. The research results provide a research idea and theoretical basis for the formation of a complete and accurate DCA diagnosis system.