Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
AbstractThe electrical fire monitoring system will automatically alarm to tell people where the residual current is abnormal before the fire occurs, which greatly reduces the occurrence of fire. Use data mining technology to find useful data from a large amount of data recorded by the electrical fire monitoring system, so as to reduce the occurrence of electrical fires. The purpose of this paper is to study the electrical fire monitoring system with different intelligent algorithms, and obtain the residual current data of different materials and materials with different cross-sectional areas in each time period. And then the electrical fire monitoring system feeds back whether the identification is successful or not through data mining technology. Experiments showed that the recognition rates of electrical fire monitoring systems with different cross-sections of the same material are roughly the same, and the recognition rates of electrical fire monitoring systems with the same cross-section of different materials are also roughly the same, and their recognition rates are roughly above 90%. The electrical fire monitoring system deserves further research to find a system with a higher recognition rate.
International Journal of Emerging Electric Power Systems – de Gruyter
Published: Dec 1, 2022
Keywords: data mining technology; electrical fire monitoring system; intelligent algorithm; neural networks; residual current
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.