SUMMARYTransformer life is primarily influenced by thermal, electrical, and mechanical stresses, and each type of stress can be subdivided into time-varying common stress from the normal usage and variable stochastic stress caused by unusual events. Estimating the cumulative effect of variable stress is essential for life estimation. Current research on the cumulative effect of time-varying common stress or variable stochastic stress varies without a unified theoretical framework, resulting in the separate studies on common stress and stochastic stress. In this paper, the combined effect of time-varying common and variable stochastic stresses on transformer life is studied based on linear cumulative damage theory, where the concept of damage is defined as a universal measurement of the deterioration from common or stochastic stress, and it is linearly incorporated to obtain the linear cumulative damage model. The reasonability of this model is verified by being compared with various widely accepted and experimentally validated life estimation models, and this model is proved to be a unified theoretical framework for all three types of stresses. Subsequently, the probabilistic form of the model is obtained with the assumption that the failure criterion obeys a Weibull distribution. Two application cases on thermal stress are presented to validate the model proposed in this paper.
In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.
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