یک روش تجزیه و تحلیل ارتعاش جدید برای پیش بینی گسل های ترانسفورماتور در محیط ابر

Internet of Things (IoT) and its applications are becoming more prevalent among researchers and companies across the world. IoT technologies offer solutions to many industrial challenges and, as such, they replace classical diagnostic methods with prognostic techniques that can potentially lead to smart monitoring systems. One of the vital applications of IoT is in smart monitoring of major electric power equipment such as transformers whilst in service. Mechanical integrity and operation condition of energized transformers might be evaluated by employing vibration method, which is a non-destructive and economic approach. However, researchers have not yet reached a consensus on how to interpret the results of this method. A new approach has been introduced in this study in order to evaluate transformer real-time vibration signal. A detailed discussion has been provided on transformer vibration modelling and interpretation challenges of the results. Furthermore, a novel method is introduced to evaluate transformer vibration signal during short circuit contingency. As we show, it is straightforward to implement the introduced methods over the cloud environment. Practical studies are conducted on two distribution transformers to examine the introduced methods. The results demonstrate that the methods are remarkably effective, fast and feasible to be programmed over cloud for transformer short circuit fault prognosis.