Study On Transformer Condition Assessment And Fault Diagnosis Based On Cloud Model

In the power system, the transformer is seen to be the core equipment, so the safety and reliability of the transformer is directly related to the security and reliability of the power system. Due to various internal factors and external factors it is difficult to avoid failure or accident occurred in the transformer's long run. As the basis of the state of transformer's maintenance, power transformer condition assessment is particularly important, and as a special case of condition assessment, fault diagnosis technology also has a great significance. Therefore, the study of power transformer condition assessment and fault diagnosis technology for finding incipient faults as early as possible not only become common concern of power industry issues but also received widespread attention in academic.In the study of transformer condition assessment, in view of the traditional fuzzy comprehensive evaluation model only considers fuzziness and ignored randomness of uncertainty, while the cloud model considers fuzziness and randomness of uncertainty at the same time, we establish a new condition assessment model based on cloud model. Besides, we will use sub-indicators of the evaluation object to calculate the evaluation results of objective indicators and get weight information by AHP, using variable weight formula to balance adjustment individual key indicators'weights, and get the actual evaluation vector using cloud formula, then determine the status of the transformer. Through case analysis and compared with the traditional condition assessment, we find that the result based on the new assessment model is closer to the actual state.In the study of transformer fault diagnosis, in view of matter-element theory solving fault diagnosis problems in a simple and effective method, ignoring the uncertainty of boundary values, the result is deviated from the actual situation. Using cloud model theory to consider the uncertainty of the boundary, we combine cloud model theory with matter-element to propose a new model which is based on matter-element theory and cloud model theory. Meanwhile, we combine oil dissolved gas analysis, by analyzing the concentration of dissolved gases, gas production rate, the total hydrocarbon content and the ratio between gas in oil, and establish transformer fault diagnosis model, then determine the transformer fault accurately and objectively. By compared with other techniques, combined with case analysis, we reach the conclusion that the new technique which is based on matter-element theory and cloud model has a higher accuracy.