| With the increasing scale of Power Grid and power cables of power system, it is crucial to ensure the security of power cables. Therefore, the research of online status monitoring and fault diagnosing of power cables becomes a popular research field. However, due to the complicated and changeable environment of practical cable faults, various factors which influence the change of environment and accuracy of faults location, it leads to the diversity of the cable fault information, the difficulty of obtaining fault information and the fault location accuracy not high enough. Therefore, it is with urgent practical significance to research how to analysis, process and improve the accuracy of fault location.Generation mechanism of power cable fault is analyzed in this paper. Based on the summary of domestic and foreign research of power cable fault diagnosis, the application of Wavelet Analysis, DNA Genetic Algorithm, Rough Set and Petri nets in the field of power cable fault diagnosis is studied in this paper. Furthermore, the application of Multi-Agent technology in the power cable faults is analyzed, and faults diagnosis system of power cables is designed based on this paper's research purposes. The main work and innovation of the paper are listed as below:1. Against the problem of fault feature signal extraction, the commonly used wavelet threshold de-noising methods and steps are put forward based on the basic principles of wavelet de-noising. Being compared with wavelet de-noising simulation, the 3σde-noising methods are verified good superiority in terms of signal de-noising.2. Based on previous Genetic Algorithm for attribute reduction, the idea of introducing molecular DNA algorithm into the coding process, taking the approach of applying the unique two bit binary encoding and obtaining an improved algorithm for attribute reduction is proposed. This new method is verified feasible in fault diagnosis and more valuable based on examples.3. Against the problem that classic optimization algorithm cannot solve the optimization problem of the high-dimensional space and search space increases exponentially with the increasing degree of problem complexity, a hybrid algorithm, the combination of the Gravity Search Algorithm and BP neural network, and feasible steps are proposed in this paper. Simulation results show that this method has relatively fast convergence speed in the early stage of network training, and can basically meet the diagnosing requirements..4 Against the problem that when locating cable fault, the high-voltage pulse signal produced by high-voltage pulse injection method propagating in the cable will inevitably generate noise, causing the received signal not be direct to identify the faults point position corresponding to the first wave, the problem can be solved by de-noising the signal wave by Hilbert-Huang transform algorithm. ATP simulation verified that the waveform is getting close to the ideal state obtained from this method and, in addition, faults point position corresponding to the wavehead can be identified visually.5. According to processing results of faults signal de-noising, fault feature reduction and fault classification, a Multi-Agent model of power cable fault diagnosis, which can build fuzzy reasoning on the basis of the relationship between fault symptoms and faults, is designed in this paper. Multiple functional Agents are also designed. Multi-Agent groups take the approach of combining contract net model with blackboard model to coordinate the work of fault detection among the groups. Based on the relationship between fault symptoms and faults, fault diagnosing model is established by applying fuzzy Petri Nets. Then fault diagnosing model of fuzzy Petri Nets be built to diagnosis reasoning structure through the category of faults, which can diagnose faults of more reasons leading to more results effectively. Calculation analysis proved the effectiveness and reliability of this diagnosing model.
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