基于KNN算法的用电安全智能监控系统设计

Design of Intelligent Monitoring System for Electricity Safety Based on KNN Algorithm

为解决传统的人工排查违规电器方式在时效与规模上存在的局限性,同时也为了预防因使用违规电器而引发的安全事故,研究并设计了一个用电安全智能监控系统。本系统由用电安全执行终端和用电安全监控平台组成,执行终端主要由树莓派3B+微型计算机和四路交流电流电压采集模块组成,实现了对宿舍用电状况的自动监测、违规电器的自主识别和断复电操作;监控平台实现了对电器使用情况的实时监控、短信预警和违规记录存储等功能。选取有功功率增量和视在功率增量作为特征量,通过KNN(K最近邻)算法进行电器识别,实验结果表明,该系统的用电器平均识别正确率高达95%以上,并且可以及时预警且限制学生公寓违规电器的使用,能有效降低因使用违规电器所带来的生命和财产风险。

In order to solve the limitations of timeliness and scale of the traditional manual investigation of illegal electrical appliances,and also to prevent safety accidents caused by the use of illegal electrical appliances,a smart safety monitoring system for power consumption was studied and designed.This system is composed of a power safety execution terminal and a power safety monitoring platform.The execution terminal is mainly composed of a Raspberry Pi 3 B+microcomputer and a four-way AC current and voltage acquisition module.Autonomous identification and power-off operation;monitoring platform realizes real-time monitoring of electrical appliance usage,SMS alert and storage of violation records.The active power increment and apparent power increment are selected as feature quantities,and electrical identification is performed by the KNN(K nearest neighbor)algorithm.The experimental results show that the average electrical identification accuracy of the system is as high as 95%or more,and timely warning and Restricting the use of illegal appliances in student apartments can effectively reduce the risk of life and property caused by the use of illegal appliances.