基于模糊单神经元PI的煤矿电气设备电源频率自动化控制系统  

Automatic Control System for Power Supply Frequency of Coal Mine Electrical Equipment Based on Fuzzy Single Neuron PI

作　　者：李杰 席义苗 曹楠楠 马越 LI Jie;XI Yimiao;CAO Nannan;MA Yue(School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China;Shaanxi Xiaobaodang Mining Co.,Ltd.,Yulin 719302,China)

机构地区：[1]西安交通大学电气工程学院,西安710049 [2]陕西小保当矿业有限公司,榆林719302

出　　处：《自动化与仪表》2024年第3期37-41,共5页Automation & Instrumentation

摘　　要：为确保煤矿电气设备电源频率不偏离标准频率,保障煤矿电气设备安全稳定运行,设计基于模糊单神经元PI的煤矿电气设备电源频率自动化控制系统。数据采集层通过电源频率信号输出检测仪,采集电源频率数据,并使用改进小波阈值法,去噪频率数据,然后由数据传输层发送其至控制中心,控制中心在接收该数据的同时,由理想电源频率求解模块,通过多元回归模型预测电源理想频率,而后模糊神经元PI控制模块以实际、理想电源频率为输入,经频率偏差求解、模糊化等操作,获得控制信号,控制电源频率无线接近理想值。实验结果表明,该系统可实现煤矿电气设备电源频率自动化控制,控制设备电源频率无线接近理想值,电源频率控制效果显著。To ensure that the power frequency of coal mine electrical equipment does not deviate from the standard frequency and ensure the safe and stable operation of coal mine electrical equipment,an automatic control system for power frequency of coal mine electrical equipment based on fuzzy single neuron PI is designed.The data acquisition layer outputs the power frequency signal to the detector,collects power frequency data,and uses the improved wavelet threshold method to denoise the frequency data.Then,the data transmission layer sends it to the control center.The control center receives the data while using the ideal power frequency solution module to predict the ideal power frequency through a multiple regression model.Then,the fuzzy neural PI control module takes the actual and ideal power frequency as inputs,The control signal is obtained by solving the frequency deviation and fuzzing,and the power frequency is controlled to approach the ideal value wirelessly.The experimental results show that the system can achieve automatic control of the power frequency of coal mine electrical equipment,and control the power frequency of the equipment to approach the ideal value wirelessly,the power frequency control effect is significant.

关 键 词：模糊PI控制 单神经元 煤矿电气设备 电源频率 自动化控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]