Development Of Automatic Calibration System For The Pipeline Potential Collector

With the rapid development of the oil and gas industry,pipeline potential collector has been widely used in buried long-distance pipelines.From the perspectives of pipeline transportation safety,environmental protection,and extension of pipeline service life,the accuracy requirements of pipeline potential collector are greatly required.Traditionally,the pipeline potential collector is manually calibrated.Due to the large number of parameters that need to be calibrated and the complex calculation formulas,it also needs to be repeatedly fine-tuned.After consulting relevant domestic and foreign materials,no functional products related to the automatic calibration system of the pipeline potential collector has been found.Therefore,the development of the automatic calibration system of the pipeline potential collector has important engineering practical value.By consulting a large amount of relevant data at home and abroad,this topic first elaborates the research status and development trends of pipeline potential collector at home and abroad,in-depth studies of virtual instrument technology,automatic calibration technology of instruments,and theoretical knowledge of data fusion.The system design requirements were introduced,and the overall design scheme of the system was introduced in detail.It is proposed to use the Lab VIEW virtual instrument software platform as a development tool,a computer as the system control core,an Agilent E3647 A programmable DC voltage source,a DG1022 U dual-channel function / arbitrary waveform generator,and an Agilent 34450 A digital voltmeter as standard instruments.RS232 interface bus realizes communication between the computer and various instruments.Based on VISA function library and SCPI instrument programming language,it uses an improved BP neural network to process the calibration data.A complete set of functions,high reliability and stability are designed and developed.Automatic calibration system for pipe potential collector developed with strong performance and friendly operation interface.Secondly,the design flow of the hardware,software,and calibration data processing algorithms are discussed separately.The overall hardware structure is designed by analyzing the logic flow of the system calibration,the hardware selection is determined,and the hardware circuit is designed in detail.Design analysis of the softwares structural framework,program design process,etc.,achieved all functional requirements.In order to solve the problems of large number of calibration parameters,complicated process and low accuracy,this paper proposes to use data fusion algorithm to improve the calibration accuracy,design a BP neural network model that handles high-precision data fitting,and use genetic algorithms to optimize the network.The experiments show that: the number of iterations of the BP neural network optimized by the genetic algorithm is significantly reduced,and the maximum error can be guaranteed to not exceed 0.23% when the voltage input is small.It has fast convergence,high operating efficiency,and small prediction error.By constructing a BP neural network optimized based on genetic algorithm,the mapping relationship between the input data and the parameters to be calibrated is completed,and the fusion calibration of multi-sensor data is realized.Finally,under the same test conditions,the system calibration,system calibration without data fusion program,and manual calibration are used to perform the calibration test on the same pipeline potential collector,and the calibration accuracy and time required for the three methods are performed.Comparative analysis.The experimental results show that the system calibration takes the least time to complete all calibration test operations,only 10 minutes and 47 seconds,which is far less than the other two calibration methods,and the maximum accuracy error does not exceed 0.54%,verifying the efficiency and reliability of the system.