AI-based Automation Control in Substations

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Scientific Journal of Information Engineering

Scientific Journal of Information Engineering (SJIE) is an international open access journal, concerning about the latest research results of information engineering. The goal of the journal is to provide a global forum for scholars and researchers in this field to exchange ideas and promote the development of theories, applications and technologies in all areas of information engineering.The journal receives manuscripts written in Ch... [More] Scientific Journal of Information Engineering (SJIE) is an international open access journal, concerning about the latest research results of information engineering. The goal of the journal is to provide a global forum for scholars and researchers in this field to exchange ideas and promote the development of theories, applications and technologies in all areas of information engineering.

The journal receives manuscripts written in Chinese or English. As for Chinese papers, the following items in English are indispensible parts of the paper: paper title, author(s), author(s)'affiliation(s),abstract and keywords. If this is the first time you contribute an article to the journal, please format your manuscript as per the sample paper and then submit it into the online submission system. Accepted papers will immediately appear online followed by printed hard copies by Ivy Publisher globally. Therefore, the contributions should not be related to secret. The author takes sole responsibility for his views.

ISSN Print:2167-0218

ISSN Online:2167-0226

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This journal is full-text indexed by Open J-Gate.

Paper Infomation

AI-based Automation Control in Substations

Full Text(PDF, 49KB)

Author: Jinrui Deng

Abstract: The rapid development of Artificial Intelligence (AI) technologies has led to transformative applications in substation automation, addressing the growing complexities and demands of modern power grids. Substations, as crucial nodes in power distribution systems, play a vital role in maintaining grid stability, reliability, and security. However, traditional substation control systems face significant challenges in coping with system instability, aging infrastructure, and the integration of renewable energy sources. AI offers advanced solutions such as machine learning, deep learning, fuzzy logic, expert systems, and natural language processing to enhance fault detection, predictive maintenance, energy management, and grid security. This paper explores the AI technologies applied in substation automation, the challenges encountered in their integration, and the potential for future improvements. With the ability to analyze vast datasets, identify patterns, and make real-time decisions, AI is set to revolutionize substation operations, improving operational efficiency, reducing downtime, and enhancing system resilience.

Keywords: Artificial Intelligence, Substation Automation, Machine Learning, Deep Learning, Predictive Maintenance, Grid Protection, Fault Detection

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