Huijuan Hou

The tip size of metal protrusions is an important factor affecting corona discharge, since it influences the electric filed, the ionization range and strength around the protrusion defect. Most existing studies on the influence of protrusion tip size on corona discharge are implemented by experimental methods. However, these studies are difficult to obtain the microphysical process of charged particles, which is related to the generation of current pulses and electromagnetic (EM) waves. In this paper, a hydrodynamic simulation model is proposed to study the effect of protrusion tip size on the microphysical process and current pulses of negative corona discharge. A mapping relationship between microscopic behaviors of charged particles and current pulses is established by this simulation method. Based on the comparison of microphysical quantities, the effect of defect tip size on the detailed parameters of current pulses is clearly explained. It shows that with the increase of tip size under a constant voltage, all the five parameters of current pluses increase. The rising time is related to the effective ionization coefficient, while the falling time is dependent on the velocities of positive and negative ions. The experiment and simulation results coincide in current waveforms and all five parameters.

Precise predictions on state parameters of power transformers could provide a comprehensive assessment of current operation status and potential risks on equipment. In the proposed power transformer prediction model, a feature attention mechanism is introduced to basic long short-term memory network (FA-LSTM) to mining the association rules between the target attribute and its relevant features like grid operation and meteorological data. Compared to previous researches treating the attributes as independent ones, the relationship extracted through time iterations could correct prediction results by offering auxiliary information for target series. Additionally, the feature attention is not restricted by the preset threshold value in traditional association rule mining algorithms. In the case study of predicting the concentration of dissolved hydrogen in insulating oil, experiments show that our proposed FA-LSTM outperforms traditional methods with lower absolute percentage errors, giving a more accurate result for time series prediction of power transformer state parameters.

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.

The detection and diagnosis of partial discharge (PD) has been accepted as one of the most valuable means for assessing the quality of high voltage (HV) power apparatuses and their operating status. However, the present partial discharge monitoring methods are mainly based on monitoring single equipment, which needs multi-process test equipment, high cost, and huge maintenance engineering. In this paper, a method using a four element antenna array to receive the ultra-high frequency (UHF) signal, which is used to define a 3-D coordinate system and locate the PD source in the field of substations, is proposed. Moreover, this paper discusses the principles of the location algorithm based on time difference, and analyzes the origin of its location error. Then the relationship between the location error and the placement of antenna array, as well as the relationship between the location error and the time difference of arrival signals are discussed theoretically. Finally, this paper provides an optimal placement to the radio frequency antenna array.