Research On The Method Of Distribution Network Phase-to-ground Fault Diagnosis Based On The Reasoning Of Fuzzy

Distribution network supplies power to users directly, so its reliability and quality of power supply influence the national economy and People's daily life directly. But frequent power distribution network fault is greatly reduced the power supply reliability, so the fault diagnosis technique received widespread concern. However, because of the distribution system's complexity and its low level of automation, distribution network fault diagnosis suffered serious obstacles . How to improve the reliability and the rapidity of fault diagnosis based on the present equipments and diagnosis methods has important practical significance.Firstly this thesis briefly introduces the existing problems in the distribution system, and systematically introduces the earthing types. the phase-to-ground faul of neutral non-grounded system and neutral grounded system through an arc suppression coil were further research to the fault of the steady-state characteristics and transient process of a detailed analysis, and introduces the common method and principle of fault diagnosis detailedly.Secondly, introducing fuzzy theory knowledge and the advantages of fuzzy reasoning, according to the existing method of distribution network fault diagnosis to establish the fuzzy reasoning model; According to the more and complicated distribution branch but branch is not longer characteristics, the distribution network can be simplified and layered,and judging the distribution network with common fuzzy reasoning technology combined with fault diagnosis method layer by layer, and gradually reduce fault area, to determine the final diagnosis.Finally, two lines of 10kV power distribution line Qiaoshang line (neutral non-grounded system) and Nanqi line (neutral grounded system through an arc suppression coil) were exampled, using the software MATLAB simulation established simulation model respectively, and simulating the fault state and gather in the fault information. The fault information were diagnosed by fault diagnosis method which improved with fuzzy reasoning. The result of diagnosis confirmed the feasibility of this method.