Operation state assessment and situation prediction of distribution transformer for cloud edge collaboration
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Abstract:
The construction of the Power Internet of Things has been under rapid progress. With the guidance of the “cloud-pipe-edge-terminal” construction system, this paper presents a general technical framework for operation state evaluation and trend prediction of distribution transformer. The framework is deployed in cloud center and edge nodes, and use the cloud edge collaboration mechanism to analyze and process massive power data so as to complete the operation management of large-scale distribution transformer cluster. The specific process includes extracting multi-dimensional characteristics of distribution transformer, such as basic state, real-time state and cumulative state, constructing evaluation index system, and realizing real-time portrait description of distribution transformer operation state through dynamic evaluation model. According to the time order and change trend of the characteristic data stream, Long Short-Term Memory network (LSTM) is used for extracting the regulations of characteristic data, and Support Vector Regression model (SVR) for its prediction. Then, the future characteristic data flow is obtained and input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the advanced nature and applicability of the technology framework.
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Supported by the State Grid Corporation of China Science and Technology (5206001900F7).
