[go: up one dir, main page]

CN116400157A - Method and device for measuring equivalent electrical parameters of ion tube - Google Patents

Method and device for measuring equivalent electrical parameters of ion tube Download PDF

Info

Publication number
CN116400157A
CN116400157A CN202310394986.4A CN202310394986A CN116400157A CN 116400157 A CN116400157 A CN 116400157A CN 202310394986 A CN202310394986 A CN 202310394986A CN 116400157 A CN116400157 A CN 116400157A
Authority
CN
China
Prior art keywords
voltage
ion tube
voltage side
neural network
low
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310394986.4A
Other languages
Chinese (zh)
Other versions
CN116400157B (en
Inventor
沈坤
张萌妹
陈昊翔
武梦谣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hunan Normal University
Original Assignee
Hunan Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hunan Normal University filed Critical Hunan Normal University
Priority to CN202310394986.4A priority Critical patent/CN116400157B/en
Publication of CN116400157A publication Critical patent/CN116400157A/en
Application granted granted Critical
Publication of CN116400157B publication Critical patent/CN116400157B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/30Circuit design
    • G06F30/32Circuit design at the digital level
    • G06F30/33Design verification, e.g. functional simulation or model checking
    • G06F30/3323Design verification, e.g. functional simulation or model checking using formal methods, e.g. equivalence checking or property checking
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/003Environmental or reliability tests
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • G06F30/27Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Computer Hardware Design (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Geometry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Environmental & Geological Engineering (AREA)
  • Measurement Of Current Or Voltage (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

The invention discloses a method and a device for measuring equivalent electrical parameters of an ion tube, comprising the following steps: s1, training a neural network calculation model by adopting low-voltage side voltage, current signals and high-voltage side voltage signals of an ion tube transformer; s2, obtaining a low-voltage side voltage and current signal of the ion tube transformer, and calculating a high-voltage side voltage signal based on a neural network model; s3, calculating equivalent electrical parameters of the ion tube according to the calculated high-voltage side voltage signals. The method has the advantages of being capable of measuring equivalent electrical parameters of the ion tube without a high-voltage probe and an external measurement capacitor, being beneficial to realizing the detection engineering of the parameters of the ion tube, being safe and efficient, and the like.

Description

一种离子管等效电参数测量方法及装置A method and device for measuring equivalent electrical parameters of an ion tube

技术领域technical field

本发明涉及一种离子管等效电参数测量方法及装置。The invention relates to a method and a device for measuring equivalent electric parameters of an ion tube.

背景技术Background technique

离子管是一种广泛应用于空气质量治理领域的介质阻挡放电器件,离子管的运行状态及参数是影响空气质量治理效率的关键因素。在离子管使用过程中,由于被处理的对象是含有灰尘以及微小颗粒物的待处理空气,因此在离子管工作的高电压环境下,灰尘和微小颗粒的聚齐会阻塞离子管的放电通道,这将导致持续工作的离子管存在放电能力逐步下降的趋势,并最终影响空气质量治理的效率,因此如何实时准确地监测离子管的工作状态是离子管使用及维护的重点和难点。The ion tube is a dielectric barrier discharge device widely used in the field of air quality control. The operating status and parameters of the ion tube are the key factors affecting the efficiency of air quality control. During the use of the ion tube, since the processed object is the air to be treated that contains dust and tiny particles, in the high voltage environment where the ion tube works, the accumulation of dust and tiny particles will block the discharge channel of the ion tube, which will As a result, the discharge capacity of ion tubes that work continuously has a tendency to gradually decline, which will eventually affect the efficiency of air quality control. Therefore, how to monitor the working status of ion tubes in real time and accurately is the focus and difficulty of ion tube use and maintenance.

作为一种典型的介质阻挡放电器件,离子管的工作状态可分为非放电状态与放电状态,非放电状态的等效电路为介质电容与气隙电容的串联,放电状态的等效电路为介质电容与处于击穿状态的齐纳二极管的串联。因此,离子管工作状态可以由离子管等效电路的参数表征,即通过获取离子管等效电路的参数来分析离子管的工作状态,离子管的等效电参数包括介质电容与气隙电容。As a typical dielectric barrier discharge device, the working state of the ion tube can be divided into non-discharging state and discharging state. The equivalent circuit of the non-discharging state is the series connection of the dielectric capacitance and the air gap capacitance, and the equivalent circuit of the discharging state is the dielectric Capacitor in series with Zener diode in breakdown state. Therefore, the working state of the ion tube can be characterized by the parameters of the equivalent circuit of the ion tube, that is, the working state of the ion tube can be analyzed by obtaining the parameters of the equivalent circuit of the ion tube. The equivalent electrical parameters of the ion tube include the dielectric capacitance and the air gap capacitance.

现有的介质阻挡放电器件等效电参数的测量方法需要在放电器件的工作回路中设置测量电容和高压探头,并采用示波器获取器件的李萨如图,这种方法仅适用于实验室环境,不能对实际工作中的离子管进行等效电参数的在线测量,且存在成本高、需接触高压以及无法实现参数测量的自动化等问题。The existing method for measuring the equivalent electrical parameters of a dielectric barrier discharge device requires setting a measurement capacitor and a high-voltage probe in the working circuit of the discharge device, and using an oscilloscope to obtain the Lissajous figure of the device. This method is only applicable to the laboratory environment. On-line measurement of equivalent electrical parameters of ion tubes in actual work cannot be carried out, and there are problems such as high cost, need to be exposed to high voltage, and inability to realize the automation of parameter measurement.

发明内容Contents of the invention

为解决上述技术问题,本发明提供了一种低成本、安全可靠、且易于实现参数测量工程化应用的离子管等效电参数测量方法与装置。In order to solve the above-mentioned technical problems, the present invention provides a low-cost, safe and reliable ion tube equivalent electrical parameter measurement method and device that is easy to realize the engineering application of parameter measurement.

本发明解决上述问题的技术方案是:一种离子管等效电参数测量方法及装置,包括以下步骤:The technical solution of the present invention to solve the above problems is: a method and device for measuring the equivalent electrical parameters of an ion tube, comprising the following steps:

S1、采用离子管变压器低压侧电压、电流信号与高压侧电压信号训练神经网络计算模型;S1. Use the ion tube transformer low-voltage side voltage, current signal and high-voltage side voltage signal to train the neural network calculation model;

S2、获取离子管变压器低压侧电压、电流信号,并基于神经网络模型计算出高压侧电压信号;S2. Obtain the voltage and current signals of the low-voltage side of the ion tube transformer, and calculate the voltage signal of the high-voltage side based on the neural network model;

S3、由计算出的高压侧电压信号计算离子管等效电参数。S3. Calculate the equivalent electrical parameters of the ion tube based on the calculated high voltage side voltage signal.

本发明的效果在于:针对离子管的等效电参数测量问题,与传统方法相比,本发明提供了一种低成本、安全可靠、且易于实现参数测量工程化应用的测量方法与装置。该参数测量方法的优势在于:在实际测量过程无需改变离子管的工作电路,即不需要在离子管工作电路中接入测量电容;无需测量高压信号,即不需要高压探头或者在离子管工作电路中并接取样电容;无需多通道示波器,即不需要示波器绘制李萨如图,再通过在图上描点的方式计算平行四边形两边的斜率;便于参数测量的工程化,在所构建的基于嵌入式系统的测量装置中,可直接基于计算出的数据集计算等效电参数。The effect of the present invention is that: for the measurement of the equivalent electrical parameters of the ion tube, compared with the traditional method, the present invention provides a low-cost, safe and reliable measurement method and device that is easy to realize the engineering application of parameter measurement. The advantage of this parameter measurement method is that there is no need to change the working circuit of the ion tube during the actual measurement process, that is, there is no need to connect a measuring capacitor in the working circuit of the ion tube; The sampling capacitor is connected in parallel; no multi-channel oscilloscope is needed, that is, no oscilloscope is required to draw a Lissajous diagram, and then calculate the slope of the two sides of the parallelogram by drawing points on the diagram; it is convenient for the engineering of parameter measurement. In the measurement device of the system, equivalent electrical parameters can be calculated directly based on the calculated data set.

附图说明Description of drawings

图1为本发明的流程图Fig. 1 is a flowchart of the present invention

图2为本发明中离子管特性测量电路图Fig. 2 is the ion tube characteristic measurement circuit diagram in the present invention

图3为本发明中训练神经网络的数据集波形图Fig. 3 is the data set oscillogram of training neural network in the present invention

图4为本发明的测量方案图Fig. 4 is a measurement scheme figure of the present invention

图5为本发明中离子管等效电参数测量电路图Fig. 5 is the measurement circuit diagram of ion tube equivalent electric parameter in the present invention

图6为本发明中高压侧电压信号神经网络计算值与实际测量值对比图Fig. 6 is a comparison chart between the calculation value of the voltage signal neural network of the middle and high voltage side of the present invention and the actual measurement value

图7为本发明中由计算值绘制的离子管李萨如图Fig. 7 is the ion tube Lissajous diagram drawn by the calculated value in the present invention

图8为本发明中定点搜索策略计算原理图Fig. 8 is a schematic diagram of fixed-point search strategy calculation in the present invention

实施方式Implementation

下面结合附图和具体实施例对本发明作进一步的详细说明。The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

本发明的一种离子管等效电参数测量方法及装置,其流程图如图1所示,包括以下几个步骤:A kind of ion tube equivalent electrical parameter measuring method and device of the present invention, its flow chart as shown in Figure 1, comprises the following steps:

S1.采用离子管变压器低压侧电压、电流信号与高压侧电压信号训练神经网络计算模型;S1. Use the ion tube transformer low-voltage side voltage, current signal and high-voltage side voltage signal to train the neural network calculation model;

S2.获取离子管变压器低压侧电压、电流信号,并基于神经网络模型计算出高压侧电压信号;S2. Obtain the voltage and current signals of the low-voltage side of the ion tube transformer, and calculate the voltage signal of the high-voltage side based on the neural network model;

S3.由计算出的高压侧电压信号计算离子管等效电参数。S3. Calculate the equivalent electrical parameters of the ion tube based on the calculated high voltage side voltage signal.

1、步骤S1的具体过程包括:1. The specific process of step S1 includes:

S1.1.构建如图2所示的离子管特性测量电路,该电路包括单相220V交流电源、离子管变压器、离子管、测量电容(CM、C1、C2)、电压传感器PT、电流传感器CT、4通道示波器。单相220V交流电源与离子管变压器低压侧连接,电压传感器与电流传感器分别用于测量离子管变压器低压侧电压与电流信号;离子管与测量电容CM串联后与离子管变压器高压侧连接,取样电容C1、C2串联后与离子管变压器高压侧连接,测量电容CM用于测量离子管工作过程的电荷,取样电容C1、C2用于获取离子管的工作电压,并基于电容分压原理,得到可用于示波器测量的低压信号,本发明中测量电容CM取值0.47uF,电容C1为47pF,电容C2为47000pF;4通道示波器用于记录低压侧电压U与电流I,以及高压侧总电压VW和测量电压VMS1.1. Build the ion tube characteristic measurement circuit shown in Figure 2, the circuit includes a single-phase 220V AC power supply, ion tube transformer, ion tube, measuring capacitance (C M , C 1 , C 2 ), voltage sensor PT, Current sensor CT, 4-channel oscilloscope. The single-phase 220V AC power supply is connected to the low-voltage side of the ion tube transformer, and the voltage sensor and current sensor are used to measure the voltage and current signals of the low-voltage side of the ion tube transformer; Capacitors C 1 and C 2 are connected in series with the high voltage side of the ion tube transformer. The measuring capacitor C M is used to measure the charge in the working process of the ion tube. The sampling capacitors C 1 and C 2 are used to obtain the working voltage of the ion tube, and based on the capacitance analysis According to the voltage principle, the low-voltage signal that can be used for oscilloscope measurement is obtained. In the present invention, the value of the measurement capacitor C M is 0.47uF, the capacitor C1 is 47pF, and the capacitor C2 is 47000pF; the 4-channel oscilloscope is used to record the low-voltage side voltage U and current I, And the total voltage V W of the high voltage side and the measurement voltage V M ;

S1.2.构建神经网络计算模型,所构建的神经网络采用三层结构,其中输入层由2个神经元构成,隐含层包括5个神经元,输出层为2个神经元;输入层神经元与离子管变压器低压侧电压与电流信号对应,输出层神经元与高压侧总电压与测量电压信号对应;S1.2. Construct a neural network calculation model. The constructed neural network adopts a three-layer structure, in which the input layer consists of 2 neurons, the hidden layer includes 5 neurons, and the output layer consists of 2 neurons; the input layer neurons The neurons in the output layer correspond to the voltage and current signals on the low-voltage side of the ion tube transformer, and the neurons in the output layer correspond to the total voltage on the high-voltage side and the measured voltage signal;

S1.3.训练神经网络计算模型,将记录的低压侧电压与电流信号和高压侧总电压与测量电压信号作为训练神经网络的样本数据,样本数据包含4组1000个点,如图3所示,其中图3(1)为低压侧电压信号U、图3(2)为低压侧电流信号I、图3(3)为高压侧总电压信号VW、图3(4)为高压侧测量电压信号VM;为提高神经网络训练的效率,对低压侧电流信号先进行积分处理,再输入神经网络;最后采用误差反向传播算法训练神经网络模型,得到训练好的网络模型。S1.3. Train the neural network calculation model, use the recorded voltage and current signals on the low-voltage side and the total voltage and measured voltage signals on the high-voltage side as sample data for training the neural network. The sample data contains 4 groups of 1000 points, as shown in Figure 3 , where Figure 3(1) is the voltage signal U on the low-voltage side, Figure 3(2) is the current signal I on the low-voltage side, Figure 3(3) is the total voltage signal V W on the high-voltage side, and Figure 3(4) is the measured voltage on the high-voltage side Signal V M ; in order to improve the efficiency of neural network training, the current signal on the low-voltage side is integrated and processed first, and then input into the neural network; finally, the neural network model is trained using the error back propagation algorithm, and the trained network model is obtained.

2、步骤S2的具体过程包括:2. The specific process of step S2 includes:

本发明的测量方案如图4所示,采用电压传感器和电流传感器分别测量离子管变压器低压侧的电压、电流信号,并对电流信号进行积分得到电荷量;将低压侧电压信号和电荷量输入训练好的神经网络模型,由网络模型计算出高压侧的总电压和测量电压,最后绘制李萨如图,并计算等效电参数。The measurement scheme of the present invention is as shown in Figure 4, adopts voltage sensor and current sensor to measure the voltage of ion tube transformer low voltage side, current signal respectively, and current signal is carried out integration to obtain electric charge; Low voltage side voltage signal and electric charge input training A good neural network model calculates the total voltage and measured voltage on the high-voltage side from the network model, and finally draws the Lissajous diagram and calculates the equivalent electrical parameters.

S2.1.构建如图5所示离子管等效电参数测量电路,该电路包括单相220V交流电源、离子管变压器、离子管、电压传感器、电流传感器、参数测量装置;单相220V交流电源与离子管变压器低压侧连接,电压传感器与电流传感器分别用于测量离子管变压器低压侧电压与电流信号;离子管与离子管变压器高压侧连接;参数测量装置获取电压、电流传感器采样的离子管变压器低压侧电压U与电流I,采样点数为1000点,采样时长为40ms;S2.1. Build the ion tube equivalent electrical parameter measurement circuit as shown in Figure 5, the circuit includes a single-phase 220V AC power supply, ion tube transformer, ion tube, voltage sensor, current sensor, parameter measuring device; single-phase 220V AC power supply It is connected to the low-voltage side of the ion tube transformer, and the voltage sensor and current sensor are used to measure the voltage and current signals of the low-voltage side of the ion tube transformer respectively; the ion tube is connected to the high-voltage side of the ion tube transformer; the parameter measuring device obtains the ion tube transformer sampled by the voltage and current sensors Low-voltage side voltage U and current I, the number of sampling points is 1000 points, and the sampling time is 40ms;

S2.2.基于神经网络模型计算出高压侧电压信号,将训练好的神经网络模型存放在参数测量装置中,并将获取到的低压侧电压与电流信号送入该神经网络,由神经网络计算出对应的高压侧电压信号;计算结果如图6所示,图6(1)为高压侧总电压计算值与测量值对比图,图6(2)为高压侧测量电压计算值与测量值对比图,由图可知由所构建神经网络计算出的高压侧电压信号具有较小的计算误差。S2.2. Calculate the high-voltage side voltage signal based on the neural network model, store the trained neural network model in the parameter measurement device, and send the obtained low-voltage side voltage and current signals into the neural network for calculation by the neural network The corresponding high-voltage side voltage signal is obtained; the calculation results are shown in Figure 6, Figure 6(1) is a comparison chart of the calculated value and measured value of the total voltage at the high-voltage side, and Figure 6(2) is a comparison between the calculated value and the measured value of the measured voltage at the high-voltage side It can be seen from the figure that the high-voltage side voltage signal calculated by the constructed neural network has a small calculation error.

3、步骤S3的具体过程为:3. The specific process of step S3 is:

S3.1.根据计算出的高压侧电压信号数据集绘制李萨如图,如图7所示,为典型的DBD放电特性图;结合图7采用定点搜索策略计算李萨如图平行四边形的斜率,其计算原理如图8所示,过程为:①先搜索数据集中测量电压的绝对值小于0.5的数据点,并记录该数据点在数据集中的序号,如图8所示的A点和B点;②将搜索到的数据点序号分为两类,第一类数据点序号对应的总电压为正值,如图8中的B点,第二类数据点序号对应的总电压为负值,如图8中的A点;③以每一个第一类数据点序号为中心,取与其前后间隔为15的序号,并基于该序号所对应的总电压和测量电压计算对应线段的斜率,最后计算全部第一类数据点序号所对应线段斜率的平均值,计为k1,在图8中与B点序号相对应的线段为CD,计算其斜率为k11;④以每一个第二类数据点为基准,在数据集中找到与第二类数据点对应总电压的偏差的绝对值小于0.3,并且对应测量电压小于-10的数据点序号,如图8中的E点,取与其前后间隔为10的序号,并基于该序号所对应的总电压和测量电压计算对应线段的斜率,最后计算全部第二类数据点序号所对应线段斜率的平均值,计为k2,在图8中,先找到与A点序号相对应的点E,再得到与E点相对应的线段为FG,计算其斜率为k21。S3.1. Draw a Lissajous diagram according to the calculated high-voltage side voltage signal data set, as shown in Figure 7, which is a typical DBD discharge characteristic diagram; combined with Figure 7, use a fixed-point search strategy to calculate the slope of the parallelogram in the Lissajous diagram , its calculation principle is shown in Figure 8, and the process is as follows: ①First search the data point in the data set whose absolute value of the measured voltage is less than 0.5, and record the serial number of the data point in the data set, as shown in Figure 8. Points A and B ②Divide the searched data point numbers into two categories, the total voltage corresponding to the first type of data point number is positive, as shown in Figure 8 at point B, the second type of data point number corresponding to the total voltage is negative , as point A in Figure 8; ③ centering on the serial number of each first-type data point, take the serial number with an interval of 15 before and after it, and calculate the slope of the corresponding line segment based on the total voltage and the measured voltage corresponding to the serial number, and finally Calculate the average value of the slope of the line segment corresponding to the serial number of all first-type data points, which is counted as k1. In Figure 8, the line segment corresponding to the serial number of point B is CD, and its slope is calculated as k11; ④ Take each second-type data point As a benchmark, find in the data set that the absolute value of the deviation from the total voltage corresponding to the second type of data point is less than 0.3, and the serial number of the data point corresponding to the measured voltage is less than -10, such as point E in Figure 8, and the interval before and after it is 10 serial number, and calculate the slope of the corresponding line segment based on the total voltage and the measured voltage corresponding to the serial number, and finally calculate the average value of the slope of the line segment corresponding to the serial number of all second-type data points, which is counted as k2. In Figure 8, first find the slope corresponding to Point E corresponding to the serial number of point A, then get the line segment corresponding to point E as FG, and calculate its slope as k21.

S3.2.根据计算出的斜率,计算离子管等效电参数,所述参数为:介质电容C d 和气隙电容C g ;计算公式为:S3.2. Calculate the equivalent electrical parameters of the ion tube according to the calculated slope, the parameters are: dielectric capacitance C d and air gap capacitance C g ; the calculation formula is:

C d = (k2×CM)/1000 C d = (k2×C M )/1000

Cg= (k1×k2×CM)/[( k1-k2)×1000] Cg = (k1×k2×C M )/[( k1-k2)×1000]

4、离子管等效电参数测量装置包括传感器模块、AD模块、嵌入式处理器、参数显示屏、串口模块;其中串口用于加载神经网络模型,电压、电流传感器用于获取离子管变压器低压侧电压、电流信号,AD模块用于将采样的低压侧电压、电流信号转化为数字信号,嵌入式处理器用于电流信号积分处理、神经网络计算、李萨如图斜率计算以及离子管等效电参数计算,显示屏用于显示李萨如图以及离子管等效电参数。4. The ion tube equivalent electrical parameter measurement device includes a sensor module, AD module, embedded processor, parameter display screen, and serial port module; the serial port is used to load the neural network model, and the voltage and current sensors are used to obtain the low voltage side of the ion tube transformer. Voltage and current signals, the AD module is used to convert the sampled low-voltage side voltage and current signals into digital signals, and the embedded processor is used for current signal integral processing, neural network calculation, Lissajous figure slope calculation and ion tube equivalent electrical parameters For calculation, the display screen is used to display the Lissajous figure and the equivalent electrical parameters of the ion tube.

Claims (5)

1.一种离子管等效电参数测量方法及装置,包括以下步骤:1. A method and device for measuring an ion tube equivalent electrical parameter, comprising the following steps: S1.采用离子管变压器低压侧电压、电流信号与高压侧电压信号训练神经网络计算模型;S1. Use the ion tube transformer low-voltage side voltage, current signal and high-voltage side voltage signal to train the neural network calculation model; S2.获取离子管变压器低压侧电压、电流信号,并基于神经网络模型计算出高压侧电压信号;S2. Obtain the voltage and current signals of the low-voltage side of the ion tube transformer, and calculate the voltage signal of the high-voltage side based on the neural network model; S3.由计算出的高压侧电压信号计算离子管等效电参数。S3. Calculate the equivalent electrical parameters of the ion tube based on the calculated high voltage side voltage signal. 2.根据权利要求1所述的离子管等效电参数测量方法及装置,其特征在于所述步骤S1的具体步骤包括:2. ion tube equivalent electric parameter measuring method and device according to claim 1, is characterized in that the concrete steps of described step S1 comprise: S1.1.构建离子管特性测量电路,该电路包括单相220V交流电源、离子管变压器、离子管、测量电容、电压传感器、电流传感器、4通道示波器;单相220V交流电源与离子管变压器低压侧连接,电压传感器与电流传感器分别用于测量离子管变压器低压侧电压与电流信号;离子管及测量电容与离子管变压器高压侧连接,测量电容用于测量离子管工作电压及电荷; 4通道示波器用于记录低压侧电压与电流信号和高压侧电压信号;S1.1. Construct the ion tube characteristic measurement circuit, which includes single-phase 220V AC power supply, ion tube transformer, ion tube, measuring capacitor, voltage sensor, current sensor, 4-channel oscilloscope; single-phase 220V AC power supply and ion tube transformer low voltage The voltage sensor and current sensor are used to measure the voltage and current signals of the low-voltage side of the ion tube transformer respectively; the ion tube and the measuring capacitor are connected to the high-voltage side of the ion tube transformer, and the measuring capacitor is used to measure the working voltage and charge of the ion tube; 4-channel oscilloscope Used to record voltage and current signals on the low-voltage side and voltage signals on the high-voltage side; S1.2.构建神经网络计算模型,所构建的神经网络采用三层结构,其中输入层由2个神经元构成,隐含层包括5个神经元,输出层为2个神经元;输入层神经元与离子管变压器低压侧电压与电流信号对应,输出层神经元与高压侧电压信号对应;S1.2. Construct a neural network calculation model. The constructed neural network adopts a three-layer structure, in which the input layer consists of 2 neurons, the hidden layer includes 5 neurons, and the output layer consists of 2 neurons; the input layer neurons The neurons of the ion tube transformer correspond to the voltage and current signals on the low-voltage side of the ion tube transformer, and the output layer neurons correspond to the voltage signals on the high-voltage side; S1.3.训练神经网络计算模型,将记录的低压侧电压与电流信号和高压侧电压信号作为训练神经网络的样本数据;对低压侧电流信号先进行积分处理,再输入神经网络;采用误差反向传播算法训练神经网络模型,得到训练好的网络模型。S1.3. Train the neural network calculation model, use the recorded low-voltage side voltage and current signals and the high-voltage side voltage signal as sample data for training the neural network; first perform integral processing on the low-voltage side current signal, and then input it into the neural network; use error feedback Train the neural network model to the propagation algorithm to obtain the trained network model. 3.根据权利要求1所述的离子管等效电参数测量方法及装置,其特征在于所述步骤S2的具体步骤包括:3. ion tube equivalent electrical parameter measuring method and device according to claim 1, is characterized in that the concrete steps of described step S2 comprise: S2.1.构建离子管等效电参数测量电路,该电路包括单相220V交流电源、离子管变压器、离子管、电压传感器、电流传感器、参数测量装置;单相220V交流电源与离子管变压器低压侧连接,电压传感器与电流传感器分别用于测量离子管变压器低压侧电压与电流信号;离子管与离子管变压器高压侧连接;参数测量装置获取电压、电流传感器采样的离子管变压器低压侧电压与电流信号;S2.1. Build an ion tube equivalent electrical parameter measurement circuit, which includes a single-phase 220V AC power supply, ion tube transformer, ion tube, voltage sensor, current sensor, parameter measurement device; single-phase 220V AC power supply and ion tube transformer low voltage side connection, the voltage sensor and current sensor are used to measure the voltage and current signals of the low-voltage side of the ion tube transformer respectively; the ion tube is connected to the high-voltage side of the ion tube transformer; the parameter measuring device obtains the voltage and current of the low-voltage side of the ion tube transformer sampled by the current sensor Signal; S2.2.基于神经网络模型计算出高压侧电压信号,将训练好的神经网络模型存放在参数测量装置中,并将获取到的低压侧电压与电流信号送入该神经网络,由神经网络计算出对应的高压侧电压信号。S2.2. Calculate the high-voltage side voltage signal based on the neural network model, store the trained neural network model in the parameter measurement device, and send the obtained low-voltage side voltage and current signals into the neural network for calculation by the neural network Output the corresponding high voltage side voltage signal. 4.根据权利要求1所述的离子管等效电参数测量方法及装置,其特征在于所述步骤S3的具体过程为:4. ion tube equivalent electrical parameter measuring method and device according to claim 1, is characterized in that the concrete process of described step S3 is: S3.1.根据计算出的高压侧电压信号数据集绘制李萨如图,并采用定点搜索策略计算李萨如图中平行四边形的斜率;S3.1. Draw a Lissajous diagram according to the calculated high-voltage side voltage signal data set, and use a fixed-point search strategy to calculate the slope of the parallelogram in the Lissajous diagram; S3.2.根据计算出的斜率,计算离子管等效电参数,所述参数为:介质电容C d 和气隙电容C g S3.2. Calculate the equivalent electrical parameters of the ion tube according to the calculated slope, and the parameters are: dielectric capacitance C d and air gap capacitance C g . 5.根据权利要求1所述的离子管等效电参数测量方法及装置,其特征在于所述装置包括传感器模块、AD模块、嵌入式处理器、参数显示屏、串口模块;其中串口用于加载神经网络模型,电压、电流传感器用于获取离子管变压器低压侧电压、电流信号,AD模块用于将采样的低压侧电压、电流信号转化为数字信号,嵌入式处理器用于电流信号积分处理、神经网络计算、李萨如图斜率计算以及离子管等效电参数计算,显示屏用于显示李萨如图以及离子管等效电参数。5. ion tube equivalent electric parameter measuring method and device according to claim 1, is characterized in that said device comprises sensor module, AD module, embedded processor, parameter display screen, serial port module; Wherein serial port is used for loading The neural network model, the voltage and current sensors are used to obtain the voltage and current signals of the low-voltage side of the ion tube transformer, the AD module is used to convert the sampled low-voltage side voltage and current signals into digital signals, the embedded processor is used for current signal integral processing, neural Network calculation, Lissajous diagram slope calculation and ion tube equivalent electrical parameter calculation, the display screen is used to display Lissajous diagram and ion tube equivalent electrical parameters.
CN202310394986.4A 2023-04-14 2023-04-14 Method and device for measuring equivalent electrical parameters of ion tube Active CN116400157B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310394986.4A CN116400157B (en) 2023-04-14 2023-04-14 Method and device for measuring equivalent electrical parameters of ion tube

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310394986.4A CN116400157B (en) 2023-04-14 2023-04-14 Method and device for measuring equivalent electrical parameters of ion tube

Publications (2)

Publication Number Publication Date
CN116400157A true CN116400157A (en) 2023-07-07
CN116400157B CN116400157B (en) 2025-06-27

Family

ID=87008828

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310394986.4A Active CN116400157B (en) 2023-04-14 2023-04-14 Method and device for measuring equivalent electrical parameters of ion tube

Country Status (1)

Country Link
CN (1) CN116400157B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017041572A1 (en) * 2015-09-08 2017-03-16 中国电力科学研究院 Improved distribution transformer energy efficiency measurement testing method, device and storage medium
CN208332504U (en) * 2018-05-30 2019-01-04 四川艾巴适环境科技有限公司 A kind of ion deodorization fresh air purifying all-in-one machine
US20220318635A1 (en) * 2019-10-12 2022-10-06 United Microelectronics Center Co., Ltd Energy identification method for micro-energy device based on bp neural network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017041572A1 (en) * 2015-09-08 2017-03-16 中国电力科学研究院 Improved distribution transformer energy efficiency measurement testing method, device and storage medium
CN208332504U (en) * 2018-05-30 2019-01-04 四川艾巴适环境科技有限公司 A kind of ion deodorization fresh air purifying all-in-one machine
US20220318635A1 (en) * 2019-10-12 2022-10-06 United Microelectronics Center Co., Ltd Energy identification method for micro-energy device based on bp neural network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
潘文玲: "李萨如图形的研究", 《物理通报》, no. 1, 15 January 1997 (1997-01-15), pages 5 - 6 *
赵华军 等: "DBD电路参数的在线测量的实现和研究", 《电子技术应用》, no. 11, 6 November 2014 (2014-11-06), pages 57 - 59 *

Also Published As

Publication number Publication date
CN116400157B (en) 2025-06-27

Similar Documents

Publication Publication Date Title
CN106568805B (en) A kind of high integration Langmuir probe diagnostic system and method
EP2639592A1 (en) Method and apparatus for collecting voltage differential parameters of individual battery cells in battery pack
CN103712679A (en) Converter transformer operating state on-line audio analyzing and monitoring system
CN108508399B (en) Voltage transient test method based on simulation of electronic voltage transformer transfer process
CN102135593A (en) On-line diagnosis and evaluation method of insulation state of large electric machine
CN112748315B (en) Dynamic measurement and analysis device and method for insulation performance of electrical safety tester
CN114252749B (en) Transformer partial discharge detection method and device based on multiple sensors
CN106324538A (en) Partial discharge automatic calibration system
CN102193035A (en) Automatic sorting, detecting and evaluating system for impedance elements
CN115424077A (en) Cable defect identification method based on residual error neural network
CN103616636A (en) Multi-contact-finger contact state detection method of conductive circuit of electrical equipment
CN109284933A (en) A system and method for evaluating the state of an electronic transformer based on mathematical statistics
CN118225851A (en) An interventional measurement system for dissolved gas in transformer oil based on surface sensitive materials
CN113466624A (en) Method and system for detecting fault area of multi-terminal hybrid direct-current transmission line
CN116400157A (en) Method and device for measuring equivalent electrical parameters of ion tube
CN111929533B (en) A multifunctional cable sheath DC testing device
CN105974332A (en) Intrinsic safety power performance dynamic test system
CN107991593A (en) A kind of noise and shelf depreciation positioning GIS state of insulation on-line monitoring systems
CN2629047Y (en) Computer testing electric machine system
CN2814405Y (en) On-line measuring device for break-out rate of electric cleaning dust and residual heat recovery system
CN1328586C (en) Electric energy quality monitoring, recording and analyzing system
CN2864702Y (en) Experimental device for electric field performance of electrostatic precipitator
CN101063703A (en) Digital type alternating current-direct current partial discharge detecting method and device
CN2731460Y (en) Theoretical line loss monitor
CN105974353A (en) Mutual inductor amplitude and phase detection method based on virtual instrument

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant