The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

The coal mine power supply mainly rely on cable, its insulation level directly affects the coal mine safety production. Has long been lack of perfect insulation parameters online detesting equipment, the traditional detection methods need to stop the normal grid power supply, affect the normal production of coal mine, it is great significance that research a kind of on-line insulation parameters detection equipment. Using three-phase reactor in the neutral point insulation system to make artificial neutral point, from this point inject low-frequency voltage signal, the signal through the cable ontology, cable to ground insulation resistance and capacitance, and to a loop resistor. Measure the voltage and current signals and modulus conversion, using DSP to realize rapid changes of Fourier algorithm, can quickly and accurately calculate the cable to ground insulation parameters. The algorithm is effective to solve the traditional measuring circuit transformer reactance and unbalance current's influence to the accuracy of measurement, measurement process will not affect the normal operation of the electric power system, the insulation parameters can be accurately measure online.