基于函数型数据分析算法的电阻性本质安全电路电弧放电模型OA北大核心CSTPCD

Different from the traditional inductive and capacitive intrinsically safe circuits,the resistive intrinsically safe circuit is difficult to describe accurately due to its three different discharge waveforms.The experimental waveform and theoretical analysis show that the arc of the resistive circuit has multi-dimensional characteristics,and there is a strong correlation between it and environmental factors.It is difficult to solve it directly by traditional modeling methods,which makes the intrinsic safety criterion of the resistive circuit lack a complete theoretical system,resulting in multiple accidents in the actual operating environment.In order to solve such problems,this paper proposes a discharge model based on functional data analysis regression algorithm on the basis of studying the arc discharge characteristic curve of resistive intrinsically safe circuit,and establishes the arc discharge energy criterion of resistive intrinsically safe circuit based on this model. Firstly,the functional data analysis regression algorithm framework is constructed,and the mathematical models of arc discharge voltage and current are established respectively based on the three essential different discharge waveform characteristics of low energy arc discharge when the electrode of IEC spark test device is disconnected.Secondly,a power model independent of voltage and current model is proposed theoretically.Based on the power model,the energy criterion of resistive intrinsically safe circuit is supplemented.The model is simulated by Matlab software and a classification method of arc discharge waveform is proposed.Finally,a spark discharge test platform is built to verify the feasibility of the arc model and the accuracy of the prediction effect.The model does not require any restrictions on the frequency of data acquisition,and converts the dynamic function curve into'original data',which has infinite dimensional spatial characteristics and reduces the error caused by the simplification of the formula in the derivation process.The comparison between the experimental data and the simulation results shows that under the experimental conditions carried out in this paper,at least 80% of the actual data of the three different discharge waveforms can be described by the model,which proves the feasibility and universality of the functional data analysis regression model.The evaluation of the prediction effect of the discharge model established by the functional data analysis regression algorithm shows that at least 84% of the actual data can be predicted by the arc voltage,current and power model under the test conditions in this paper.It solves the problem that the arc discharge waveform of resistive intrinsically safe circuit cannot be accurately described because of three types.In addition,the model is used to explore the circuit parameter conditions of three cases of arc discharge in resistive circuits.It is found that under the condition of the power supply voltage parameter of 24 V≤E≤48 V and the resistance range of 20 Ω≤R≤360 Ω,when the normal working current of the circuit is I≤0.15 A or I≥0.6 A,the arc discharge is characterized by an early increase in power;when the normal operating current value is 0.15 A＜I＜0.6 A,the arc discharge is characterized by the initial power basically unchanged and the later rapid decline. Through simulation and experimental results,the following conclusions are drawn:(1)The proposed functional data analysis arc model solves the problem that the arc discharge waveform of the resistive intrinsically safe circuit cannot be accurately described due to three types.(2)The proposed model uses the idea of functional data analysis to transform the dynamic function curves of different characteristics into'original data'for analysis,which reduces the error caused by formula simplification.(3)Compared with the traditional model,the proposed model can predict the discharge arc voltage and current values at different discharge times,which solves the problem that the traditional model cannot explore the numerical solution.