Electric appliance analysis and identification device based on Kalman filtering algorithm and KNN algorithm

YiXian Zou

doi:10.1117/12.2641582

10 November 2022 Electric appliance analysis and identification device based on Kalman filtering algorithm and KNN algorithm

YiXian Zou

Author Affiliations +

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481Y (2022) https://doi.org/10.1117/12.2641582
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

This paper designs a device for analyzing and identifying electrical appliances. In this system, STM32F103 single chip microcomputer is used as the main control chip, and HT7038 as the core electric quantity detection device. Connect HT7038 with current transformer and voltage transformer to analyze and calculate circuit parameters such as voltage, current and power, so as to determine the types of electrical appliances connected to the circuit. Kalman filter algorithm and KNN algorithm are used to eliminate the noise of voltage and current and improve the accuracy of identification. The test shows that the system can accurately measure the current and active power characteristic parameters of large and small current electrical appliances, identify the working state of electrical appliances, and display the data on the screen in real time. The system has a wide measuring range, high learning ability and recognition ability, fast response speed and high accuracy.

Citation Download Citation

YiXian Zou "Electric appliance analysis and identification device based on Kalman filtering algorithm and KNN algorithm", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481Y (10 November 2022); https://doi.org/10.1117/12.2641582

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available