The Study On Fuzzy Diagnosis Methods Of Insulation Faults In Power Transformer Based On Dissolved Gases Analysis

The operation reliability of the power transformer,which is the major equipment in power system,directly related to the safety and stability of whole power system. In accordance with the technological difficulties encountered in the process of insulation supervision based on the Dissolved Gases Analysis (DGA),several kinds of model and method are presented to improve the reliability and precision of fault diagnosis of the power transformer. Main research content includes:By deeply studying the common transformer faults diagnosing methods,such as three-ratio methods and improved electrical committee agreements,several shortcomings such as uncertainness judgment when the fault reasons,phenomenon and principles come out together while can not consistent to each other etc. For this reason,the old methods can not fully meet the need to engineering practical application.Considering fuzzy relationship matrix can fully represents the causality between fault symptoms and fault types,when diagnosing complex equipments with multiple symptoms and fault causes such as power transformer,a synthetic fuzzy diagnosing model is firstly proposed to diagnose transformer's insulation faults based on DGA in this paper. Meanwhile,by further study the character of fuzzy factors,aimed at correcting the defects in classifying fuzzy sets of common fuzzy judgment,a method are put forward by unifying fuzzy synthetic diagnosis and fuzzy principle reasoning. Firstly,fuzzy principle reasoning is applied to filter fault reasons with low possibility,then fuzzy synthetic diagnosis is utilized to test all the left fault reasons and pick up the reasonable ones.In connection with the difference and distribution characteristic of the samples in sample space RS based on DGA,a new self-adapted weight fuzzy omean clustering model of fault diagnosis of the power transformer based on the potential function is proposed. Meanwhile,from the aspect of geometry characteristic of FC-divided in s dimension sample space,a method is proposed for the purpose of getting an effective adjacent radius,adaptive cluster number c and original cluster center of X sample set. For the diagnosissample x,the property measure and diagnosis rule are proposed,which under the condition of potential density function that determine c number of optimal fuzzy cluster P1.Plenty of diagnosis examples demonstrate the validity of the gray relationship analysis method in diagnosing power system insulation faults. Not only the misjudgment rate can be reduced,but also the transformer fault incidences can be classified by using self-adapted weight fuzzy clustering method. Further more,the transformer insulation condition can be analyzed,as well as the fault position can be located by recognizing the fault types. The diagnosing precision is much higher comparing with former three-ratio method.