This study explores the influence of hydrostatic pressure on the discharge along the oil-paper interface under AC voltage, especially for the normal operating condition and breakdown. In this paper, an experimental platform was set up to record the partial discharge (PD) parameters of the test sample under different hydrostatic pressures, while the applied AC voltage was increased to final flashover voltage step by step. Experimental results showed that higher hydrostatic pressure had different effects on PD under different voltages. Higher pressure decreased the PD energy and increased the flashover voltage. Furthermore, under higher hydrostatic pressure, discharge traces (white mark) were found on the surface of the samples after intense discharging on the oil-paper interfaces, indicating that the hydrostatic pressure can affect the gas generation and dissipation process underneath the surface of the pressboards. Finally, the mechanism of how hydrostatic pressure influences the PD, flashover voltage, and white mark was interpreted based on the bubble theory. The results derived in this paper can be helpful for an optimal design and reasonable operation of oil-paper insulation systems, especially for power transformers.
The pressboard surface is the electric weak link of the oil-paper insulation in transformers, and long-term partial discharge (PD) erosion is the dominant cause of degradation in pressboard. To explore the development processes of surface tracking under the effect of tip curvature, the typical needle-plate model was selected to initiate an electric field with a high tangential component on pressboard surface under needle tip curvature of 4~42 μm. With the help of a high-speed camera and a PD detecting system, the development processes of surface tracking and PD were recorded under a sustained AC voltage. A profound difference between surface tracking under different curvatures was discussed. Pressboard surfaces after tests were observed under a scanning electron microscope (SEM), and the damage degree of cellulose fibers was dependent on the tip curvature.
The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.
The vibration signal of gear box shows the information of its running state. The thesis explains the basic model and its algorithm of blind source separation, simulates the common fault of gear box in the condition of laboratory, disposing the fault signals of gear box by blind source separation and intelligently identifying the faulty condition of gear box by the method of support vector machine (SVM) after extracting eigenvector, which achieves success.
This paper puts forward a classification method of imitating the human eyes to recognize image as a whole which combined chaotic neural network and the Empirical Mode Decomposition (EMD). The method takes the individual of weeds plant as the research object and utilizes the chaotic neural network, the EMD, Teager energy operator and cluster analysis technology comprehensively. Firstly take the two-dimensional gray image matrix as the chaotic neural network weight matrix directly. Use the chaotic neural network with n neurons expression to iterate and get the one-dimensional output curve. Secondly, make the EMD decomposition for the curve and get the corresponding intrinsic mode function (IMF) curves. Then make the Teager energy transformation for each IMF component and get the average Teager energy. Finally use the fuzzy clustering algorithm to cluster analyze and get the clustering results. It can realize the classification of different categories of weeds through analysis and contrast the results with the original images. The experiment proves the effectiveness of the proposed algorithm for classifying weeds, and it is a universal new method of weeds classification.
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