Study On Condition Asswaaessment For Power Transformer Based On Correlation Analysis And Cloud Model

As the core equipment of the transmission system, transformer operation status directly affects the safe operation of the entire power system, so the research of transformer condition assessment should be born. Through the accurate state assessment of the transformer, the transformer can be found in the risk factors and provide a scientific basis to the transformer outage maintenance, reducing the transformer failure caused huge economic losses. Transformer running state accurate and effective assessment has been the focus of power system studies.Therefore, the paper has been focused on the build of the transformer condition assessment index,the weight to strike, as well as state assessment method.For the build of transformer condition assessment index, the paper proposes a method of constructing indicators based association analysis. Collecting over the years all kinds of trouble-free operation of transformer data, using correlation analysis method to calculate each attribute transformer fault type of confidence, the confidence level of the Index as a standard,then the paper establishes the state evaluation index.For the index weight calculating, the paper presents a method based on interval data representation to determine the weights of transformer index. According to the fluctuation of the size of the index data, giving the index its respective weight. Its method is objective and can avoid the randomness of subjective due to human participation.In the study of power transformer condition assessment methods, owing to the fuzziness of subject grade boundaries for, the paper proposes the methods based on a normal cloud model to assess the transformer condition. Considering the limited data of the transformer condition assessment, the paper presents processing data by normality cloud and extends power transformer operating data into normal cloud. Due to the transformer condition classification is too absolute, so the paper expands grade level into normal cloud. Then calculate the positive correlation between data drops with grades cloud depend on the different probabilities of cloud droplets in clouds, and then get the evaluation matrix.Finally, the paper use the normal cloud theory and membership functions related theory to build a multi-level model of the transformer condition assessment. By analyzing the data of a transformer, verify the method is effectiveness used in transformers condition assessment.