The detection and monitoring methods of power equipment are relatively complex. The analysis of a large amount of data obtained from detection and monitoring and the development of data recording methods make it possible to obtain higher-dimensional power data. If we adopt appropriate data analysis method to the data sets, we can better get the potential laws and value of power data. In this paper, we took various basic monitoring data and faults records of the 110kV main transformers into consideration, and the grid-equipment-environment data was fused. Based on the fused data, we used the decision tree and random forest algorithm to evaluate the main transformers’ defects and faults. The evaluation results of the two algorithms were obtained and compared, which proved the effectiveness of the fault evaluation algorithm and selected a more accurate fault evaluation algorithm. This paper provides new ideas for smart fault detection for power grid, and provides a reference for a more in-depth evaluation of power grid equipment.
The transient analysis model of grounding systems is an important tool to analyze the lightning characteristics of grounding devices. When lightning enters the soil through the grounding device, there is a centralized discharge channel. The spatial structure of the discharge channel in the soil has a great effect on the accuracy of the transient analysis model of the soil impulse discharge. In this paper, based on the gray information analysis method, the volume of successive impulse discharge channels in the ground under various currents and soil conditions is calculated, and the changing law with time interval is analyzed. According to the experimental results, an analysis method of the model considering the discharge channel structure is proposed, and an example is analyzed. The results show that the time interval has an effect on the volume of the discharge channel. In a certain range, the volume of the channel increases with the time interval of successive impulse discharges. Taking the critical breakdown electric field strength as the judgment condition and the residual resistivity as the variable, the development process of the discharge channel can be simulated. The calculated results of the model are close to the experimental results.
Here a new flash phase-shifting method has been proposed. In this method, the phase difference between three consecutive Moire images is achieved by changing the illumination angles. A theoretical derivation is presented, and certain formulas are obtained to calculate 3D information of the measured object. The special phase unwrapping algorithm connected with this phase shifting method is also solved out. At last, experiment results have been given to compare with theory derivation.
With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.
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