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Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment's running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

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In order to identify the shape of underground small magnetic anomaly objects, we use Support Vector Machines (SVM) to identify the underground magnetic anomaly targets. Firstly, as the SVM needs a lot of training data, and we also need to make full use of the magnetic field signal, nine component signals including total magnetic intensity (TMI) and five independent components of tensor are calculated from the original detected magnetic signal. Secondly, the nine component signals are subjected respectively to two-dimensional adaptively variational mode decomposition (2D-AVMD), which is advanced based on the two indicators, namely Mutual information (MI) and empirical entropy (EE), and we can get the nine primary signals from the decomposition results of nine component signals called the Intrinsic Mode Function (IMF). Then, the Histogram of Oriented Gradients (HOG) of the nine primary signals is extracted, and the feature data would be constructed into feature vectors. In the end, Support Vector Machines (SVM) are adopted to process these feature vectors. The output of the SVM can indicate the result of small objects’ shape recognition under the ground. Experiments prove that the shape recognition accuracy of underground small magnetic anomaly object recognition reaches 90%.

In order to make certain of the blocking impact of the ultra-wideband electromagnetic pulse (UWB EMP) on a radio communication station, an EMP injection test was conducted to study the blocking interference of a digital radio station under a step pulse train and double exponential pulse train. Combined with waveform parameters such as the pulse repetition rate (PRR), the concept of influence time when the UWB EMP was irradiated on the equipment under test (EUT) was introduced, and the model was established. According to the experimental results, the blocking impact of the EUT under the UWB EMP depended on factors such as waveform, PRR, and amplitude. The relationship between the PRR and the sensitive bit error rate could be determined by the effect test. In the case of a low PRR, the sensitive voltage threshold of the EUT was irrelevant to the PRR, and the effect was the monopulse effect. When the PRR reached above the reciprocal of the influence time, the sensitive voltage threshold of the EUT decreased with the increase in the PRR, which conformed to the cumulative multiple pulse effects.