基于STM32的区域智能用电器检测设备  被引量:8

STM32-based Design of regional smart electrical appliances detector

在线阅读下载全文

作  者:栾天 刘一清[1] Luan Tian;Liu Yiqing(School of Communication and Electronic Engineering,East China Normal University,Shanghai 200241,China)

机构地区:[1]华东师范大学通信与电子工程学院,上海200241

出  处:《电子测量技术》2020年第14期182-188,共7页Electronic Measurement Technology

摘  要:近几年来,因用电安全问题导致的各类安全事故屡见不鲜。与此同时,随着科学技术的不断发展,用电器的种类越来越纷繁复杂,简易地检测负载网络上的电流电压等基本电参已经无法满足用电安全检测的需要。为此设计了一种基于STM32的区域智能用电器检测设备,实时采集并存储负载网络上的各类电参,将待识别用电器的频谱、功率等电参数据作为匹配依据,进行计算匹配,完成识别用电器种类的功能。介绍了该系统的硬件设计与软件设计。实验结果表明,在某一负载网络中用电器种类不大于7种,负载总电流小于9 A,负载工作状态正常的情况下,该系统的识别准确率能达到100%。相比于目前市面上的各类用电安全检测装置,该用电器检测设备体积小、质量轻、成本低,使用模式识别技术对于负载网络上的用电器种类进行精确识别,为家庭或学校的用电安全监控提供了一种新的解决方案。In recent years,safety accidents caused by electrical safety problems have often been occurred.Meanwhile,electrical appliances are more and more complicated with the continuous development of science and technology.According to this background,a regional smart electrical appliances detector based on STM32 is proposed in the paper,which can distinguish different types of electrical appliances through calculating and matching the electrical parameters sampled and saved in real time.The hardware design and software design are introduced in the paper.The result of system debugging shows that the device can distinguish no more than 7 different types of electrical appliances in a load network.The accuracy of distinguishing can achieve 100%if the total current is lower than 9 A and the working state of loads is stable.Compared with the various types of electrical safety detection devices on the market,the electrical appliance detection equipment is small in size,light in weight and low in cost.It uses pattern recognition technology to accurately identify the types of electrical appliances on the load network and provides a new solution to electricity safety monitoring.

关 键 词:用电安全 智能检测 STM32 电学参数 傅里叶变换 功率谱密度 

分 类 号:TN98[电子电信—信息与通信工程]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象