首页|压差式流量计误差自动化修正算法研究

压差式流量计误差自动化修正算法研究

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针对在多干扰源扰动下压差式流量计测量结果面临输出不稳、误差较大的问题,提出多源扰动下的压差式流量计误差自动化修正算法.考虑全补偿气体可膨胀性系数、压缩系数、密度系数和流出系数等因素,研究压差式流量计误差自动化修正算法.利用均值滤波滤除信号中的高斯噪声,结合一阶滞后滤波优化卡尔曼滤波算法,修正多源扰动误差.引入自组织算法和Volterra神经网络进一步改进卡尔曼滤波算法,并优化卡尔曼滤波算法的先验模型参数,以实现多源扰动误差的自动化修正.试验结果表明,经该算法控制后:当参考流量为900 m3/h时,示值误差绝对值为0.203%;当参考流量为 700 m3/h时,流量计重复性为 0.06%.该研究可以有效识别并修正由于多源扰动造成的流量异常值,且流量测量精度较高.
Study of Automated Error Correction Algorithm for Differential Pressure Flow Meter
Aiming at the problem of unstable output and large error of differential pressure flow meter measurement results under the multi-source disturbance,the automated error correction algorithm for differential pressure flow meter under the multi-source disturbance is proposed.Considering the fully compensated gas expandability coefficient,compression coefficient,density coefficient and outflow coefficient and other factors,the automatic error correction algorithm for differential pressure flow meter is studied.The Gaussian noise in the signal is filtered by mean filtering,and the Kalman filtering algorithm is optimized by combining with the first-order hysteresis filtering to correct the multi-source disturbance error.Self-organizing algorithm and Volterra neural network are introduced to further improve the Kalman filtering algorithm and optimize the priori model parameters of the Kalman filtering algorithm to realize the automated error correction of multi-source disturbance.The experimental results show that after controlled by the algorithm,when the reference flow is 900 m3/h,the absolute value of the indication error is 0.203%;when the reference flow is 700 m3/h,the repeatability of the flow meter is 0.06%.The study can effectively identify and correct the flow outliers due to multi-source disturbance,and the flow measurement accuracy is high.

Multi-source disturbanceDifferential pressure flow meterError dataAutomated error correctionKalman filteringError compensationSelf-organizing algorithmVolterra neural network

黄秀娟

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江苏联合职业技术学院常州刘国钧分院,江苏 常州 213000

多源扰动 压差式流量计 误差数据 误差自动化修正 卡尔曼滤波 误差补偿 自组织算法 Volterra神经网络

江苏省职业教育教学改革研究基金

ZYB141

2024

自动化仪表
中国仪器仪表学会 上海工业自动化仪表研究院

自动化仪表

CSTPCD
影响因子:0.655
ISSN:1000-0380
年,卷(期):2024.45(5)
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