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A New Technical and Economic Prediction Algorithm for Transformers in Distribution Networks Based on Data Mining Algorithms

Published: 31 July 2024 Publication History

Abstract

There is a new type of energy technology called source network load storage system. Its characteristics are source network linkage and load storage synergy, with the aim of improving energy utilization efficiency, improving energy structure, and achieving sustainable development. In this system, source refers to the energy supply side, including renewable energy such as solar energy, wind energy, hydropower, as well as traditional energy such as coal, oil, and natural gas. Grid refers to the energy transmission network, including transmission lines and power system equipment. He refers to the end users of energy, including residents, enterprises, public facilities, etc. Storage refers to the storage technology of energy, including energy storage equipment and energy storage technology. The source network load storage system has a wide range of application scenarios. It can be widely used in power systems. By combining energy storage equipment with renewable energy generation facilities such as solar photovoltaic and wind power, the proportion of renewable energy can be effectively increased, dependence on traditional energy can be reduced, and the development of the power system towards clean energy can be promoted. The source network load storage system can also be applied to urban energy systems. By connecting energy storage equipment to the urban power grid, achieving balanced scheduling of urban energy, improving electricity efficiency, reducing peak electricity consumption, and reducing grid load. The paper uses data mining techniques to analyze the construction plan of transformers, and applies the conclusions of data mining to guide the selection and analysis of transformers.

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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