基于模糊神经网络的火力发电厂热控设备故障检测研究  

Research on Fault Detection of Thermal Control Equipment in Thermal Power Plants Based on Fuzzy Neural Networks

作　　者：张印 ZHANG Yin(Guoneng(Quanzhou)Thermal Power Co.,Ltd.,Quanzhou 362000,China)

机构地区：[1]国能(泉州)热电有限公司,福建泉州362000

出　　处：《电工技术》2024年第24期50-52,共3页Electric Engineering

摘　　要：热控设备在运行过程中易受多种因素影响而故障问题频发,影响整个火力发电厂的稳定性。为此,研究基于模糊神经网络的火力发电厂热控设备故障检测。采集火力发电厂热控设备振动信号,从频域和时域提取信号特征参数,结合模糊神经网络模型与特征参数对火力发电厂热控设备故障进行分类检测。实验结果表明,设计方法下火力发电厂热控设备故障检测准确率高达95.31%,检测精度较高。The susceptibility of thermal control equipment to various factors during operation,frequent malfunctions occur,affecting the stability of the entire thermal power plant.This study investigates the fault detection of thermal control equipment in thermal power plants based on fuzzy neural networks.After collecting vibration signals of thermal control equipment in thermal power plants,signal feature parameters are extracted from the frequency domain and time domain,and combined with fuzzy neural network models and feature parameters to classify and detect faults in thermal control equipment in thermal power plants.The experimental results show that the accuracy of fault detection for thermal control equipment in thermal power plants under the design method is as high as 95.31%,and the detection accuracy is relatively high.

关 键 词：模糊神经网络 火力发电厂 热控设备 设备故障 故障检测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]