基于物联网与BP神经网络的自适应MSC投切装置  

Adaptive MSC Switching Device Based on IoT and BP Neural Networks

作　　者：王誉棋 王玉峰[1] 郭玉宝 WANG Yuqi;WANG Yufeng;GUO Yubao(College of Electronic and Information Engineering,Liaoning University of Science and Technology,Anshan 114051,Liaoning,China)

机构地区：[1]辽宁科技大学电子与信息工程学院,辽宁鞍山114051

出　　处：《电气传动》2023年第7期23-30,共8页Electric Drive

摘　　要：传统的机械式静态无功补偿(MSC)装置的等电位投切算法在单机运行上存在很多局限性问题,例如单片机控制芯片运算时间长、受硬件影响计算结果收敛性差,等等。近些年,对单机可借由物联网(IoT)平台,通过消息队列遥测传输(MQTT)协议与后端通信的设备的研究愈发成熟,而物联网技术的发展,也让将神经网络这类机器学习算法应用于单机电气设备成为现实。在实验中,通过大量数据集训练,可预测不同电气环境下的投切时间,以达到自适应等电位的投切效果,研制出的装置已实现无火花、无涌流、无过电压等的自适应投切。研发重点包括装置的等电位投切原理及仿真、装置与后端软件的物联网通信方法、BP神经网络算法的实现过程与训练结果以及投切实验的实验结果和实验分析等。The equipotential switching algorithm of the traditional mechanical static compensator(MSC)device had many limitations in the operation of the single machine,such as the long operation time of the microcontroller control chip,the poor convergence of the calculation results had been affected by the hardware,and so on.In recent years,the research on devices that can communicate with the back end through the message queuing telemetry transpor(t MQTT)protocol through the internet of thing(s IoT)platform has becoming more and more mature,and the development of IoT technology has also made it possible to apply machine learning algorithms such as neural networks to stand-alone electrical equipment.In the experiment,through a large number of data sets training,the switching time in different electrical environments could be predicted to achieve the adaptive equipotential switching effect,the adaptive switching without spark,no inrush,no overvoltage,etc,was achieved in the developed device.The research and development focus includes the equipotential switching principle and simulation of the device,the IoT communication method between the device and the back-end software,the implementation process and training results of the BP neural network algorithm,and the experimental results and experimental analysis of the switching experiment.

关 键 词：物联网设备 机械式静态无功补偿装置 Matlab仿真 BP神经网络 等电位投切 自适应投切 

分 类 号：TM52[电气工程—电器]