KEYWORDS: Transformers, Deep learning, Data communications, Data acquisition, Evolutionary algorithms, Data modeling, Education and training, Telecommunications, Neural networks, Error analysis
The capacitor voltage transformer (CVT) is a measuring device that converts high voltage into a low voltage signal. It is better than the electromagnetic voltage transformer (PT) in terms of economy and protection against interference, so it is widely used in stations and substations of 35kV and above. Currently, CVT are still mainly inspected by the shutdown inspection method on a four-year cycle, which cannot meet the demand for intelligent monitoring of key equipment in smart substations. Considering the above problems, this paper investigates a device based on the deep learning prediction algorithm to realize intelligent in-line CVT prediction, which has good performance in terms of functionality and reliability.
KEYWORDS: Internet of things, Sampling rates, Matrices, Telecommunications, Sustainability, Power grids, Algorithms, Software development, Data transmission, Data storage
Intelligent applications with Internet of Things terminal as the core have penetrated into all aspects of social life. With the iterative development of Internet of Things technology, the needs of users and manufacturers may constantly change, and various software anomalies may be exposed after the deployment of Internet of Things devices in the field. How to effectively ensure the sustainable renewal of IoT devices after deployment is an urgent problem to be solved. In this paper, a lightweight firmware update method for distribution IoT terminals is proposed, which adopts differential upgrade method, only the difference between the new version and the old version needs to be upgraded, and the same upgrade effect can be achieved by transferring less data. The simulation results show that the proposed firmware update method has higher upgrade success rate and upgrade efficiency, and lower memory consumption rate.
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