CN111579940A - Electric arc furnace modeling and harmonic wave analysis method and system - Google Patents

Abstract

The present invention provides an electric arc furnace modeling and harmonic analysis method and system. Among them, the electric arc furnace modeling method includes: setting a reasonable sampling time for the measured voltage and current data of each stage of the electric arc furnace smelting process, sampling respectively, obtaining the waveforms of the voltage and current and the vector values of various harmonics, obtaining the arc voltage, The actual distribution of the arc; establish the nonlinear time-varying resistance model of the arc resistance, that is, the static model; based on the particle swarm algorithm, identify the parameters in the nonlinear time-varying resistance model of the arc furnace, and calculate the parameters of the arc equivalent resistance model ;Because the rapid and irregular change of arc length is the main factor of electric arc furnace voltage and current distortion, according to the modulation principle, three small signals, such as random signal, Gaussian noise signal and chaotic signal, are superimposed on the static model of arc length to adjust each signal. A simulation model that can reflect the external characteristics of electric arc furnace smelting is derived.

Description

A kind of electric arc furnace modeling and harmonic analysis method and system

technical field

The invention relates to the field of electrical control, and more particularly, to a method and system for modeling and harmonic analysis of electric arc furnaces.

Background technique

The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

The AC electric arc furnace is one of the important equipments of modern iron and steel enterprises, with outstanding economic benefits, and widely exists in the metal smelting industry. In the smelting process of the electric arc furnace, due to its own operating characteristics, the arc current changes very irregularly during the operation, the three-phase current in the power grid is seriously distorted and there is an obvious three-phase imbalance phenomenon, and the arc short circuit and short circuit occur frequently, causing The current impact generates a large amount of harmonic current, and the active power and reactive power change drastically, which has a negative impact on the safe and stable operation of the power system. With the continuous increase in the capacity and number of electric arc furnaces, electric arc furnaces have become a common source of harmonics, which leads to the deterioration of power quality of the power grid, affects the working efficiency of equipment, and increases equipment losses. Therefore, the accurate modeling of electric arc furnaces is very important for harmonic analysis. Evaluation and harmonic current suppression are important.

At present, there have been studies on the harmonic modeling of electric arc furnaces. Common methods can be summarized into two categories: controllable power supply models and load models. The former is to replace the electric arc furnace with a voltage source with the same waveform as the arc voltage, and the latter is dedicated to In order to study the external impedance characteristics of the electric arc furnace load, on the basis of Ohm's law and the energy balance equation, the analytical expression of the arc resistance is derived. The inventor found that there are many types of electric arc furnaces used in the industry and the tonnage is different, so it is difficult to accurately determine the parameters of the simulation model; and the existing model is difficult to accurately reflect the time-varying, chaotic and chaotic characteristics of the electric arc furnace smelting. Randomness and other external characteristics. AC electric arc furnace is one of the important equipments of modern iron and steel enterprises and widely exists in the metal smelting industry. As an important nonlinear harmonic source, the electric arc furnace will inject a large number of non-stationary random harmonics into the power grid during the smelting process, resulting in The aggravation of grid voltage distortion leads to power quality problems such as voltage flicker, which not only threatens the safe and stable operation of the power system, but also brings immeasurable economic losses to users.

SUMMARY OF THE INVENTION

In order to solve the above problems, the first aspect of the present invention provides an electric arc furnace modeling method, which establishes a method that can consider the influence of rapid irregular changes in arc length on arc voltage and current distortion, and conducts electric arc furnace model based on measured data. The parameter identification model improves the accuracy of the simulation model, and proposes a model determination method with strong versatility.

In order to achieve the above object, the present invention adopts the following technical solutions:

An electric arc furnace modeling and harmonic analysis method, comprising the following steps:

S1: Set the sampling time for the voltage and current data of each stage of the electric arc furnace smelting process, conduct sampling respectively, obtain the waveforms of voltage and current and the vector values of various harmonics, and obtain the actual distribution of arc voltage and current;

S2: establish the nonlinear time-varying resistance model of the arc resistance;

S3: Based on the particle swarm algorithm, identify the parameters in the nonlinear time-varying resistance model of the electric arc furnace, calculate the model parameters in the nonlinear time-varying resistance model of the electric arc furnace, obtain the expression of the arc time-varying resistance, and fit the arc The change curve of the arc equivalent resistance in the furnace smelting process;

S4: Superimpose three small signals such as random signal, Gaussian noise signal and chaotic signal with the static model of arc length, adjust the modulation parameters of each signal, and obtain the modulated random fluctuation model of arc length;

S5: According to the above steps, establish an electric arc furnace model based on the PSO algorithm and arc length modulation;

S6: Calculate the harmonic voltage and current value of each order in the electric arc furnace smelting process, and carry out harmonic analysis.

In order to solve the above problems, a second aspect of the present invention provides an electric arc furnace harmonic modeling system, including:

The data acquisition module is used to sample the measured voltage and current data at each stage of the electric arc furnace smelting process, and then perform Fourier analysis respectively to obtain the waveform distribution of the voltage and current and the vector value of each harmonic;

Arc furnace nonlinear time-varying resistance module, which is used to obtain a static model of the arc equivalent resistance;

The model parameter identification module is used to identify the parameters in the nonlinear time-varying resistance model of the electric arc furnace through the particle swarm algorithm, calculate the model parameters in the nonlinear time-varying resistance model of the electric arc furnace, and obtain the expression of the arc time-varying resistance. , and fit the variation curve of the arc equivalent resistance during the smelting process of the electric arc furnace;

Arc furnace arc length modulation module, which superimposes three small signals such as random signal, Gaussian noise signal and chaotic signal with the static model of arc length, adjusts the modulation parameters of each signal, and obtains the modulated random fluctuation model of arc length;

The harmonic analysis module is used to calculate the distortion degree of each harmonic voltage and current in the electric arc furnace smelting process according to the change of the arc equivalent resistance in the electric arc furnace smelting process.

In order to solve the above problems, a third aspect of the present invention provides a computer-readable storage medium, which establishes an electric arc furnace model that can take into account the influence of rapid irregular changes in arc length on arc voltage and current distortion, and uses measured data. The parameter identification model improves the accuracy of the simulation model, and proposes a model determination method with strong versatility.

In order to achieve the above object, the present invention adopts the following technical solutions:

A computer-readable storage medium on which a computer program is stored, when the program is executed by a processor, implements the steps in the above-mentioned electric arc furnace modeling and harmonic analysis method.

In order to solve the above problem, the fourth aspect of the present invention provides a computer device, which establishes a method that can consider the influence of the rapid irregular change of the arc length on the arc voltage and current distortion, and identify the parameters of the electric arc furnace model through the measured data. The accuracy of the simulation model is improved, and a model determination method with strong versatility is proposed.

In order to achieve the above object, the present invention adopts the following technical solutions:

A computer device, comprising a memory, a processor and a computer program stored in the memory and running on the processor, when the processor executes the program, the above-mentioned electric arc furnace modeling and model parameter identification method are realized. A step of.

The beneficial effects of the present invention are:

(1) The present invention establishes a model in which the relationship between arc length fluctuation and arc voltage and current distortion degree can be considered, and the parameters in the model can be determined according to the actual operating conditions of the electric arc furnace, which improves the accuracy of the simulation model and proposes a strong universality. the model determination method;

(2) According to the modulation principle, the present invention uses random signals, Gaussian noise signals and chaotic signals to perform arc length modulation, so that it can reflect the typical external characteristics of the electric arc furnace;

(3) The present invention estimates model parameters through particle swarm algorithm, and can adjust dynamic model parameters according to the application type, nameplate parameters and actual operating conditions of the electric arc furnace; the present invention accurately evaluates the harmonics of the electric arc furnace, which can improve the power grid. Stability, reduce harmonic current, improve power quality, and improve the economy and stability of power system operation.

Description of drawings

FIG. 1 is a flowchart of an electric arc furnace modeling process according to an embodiment of the present invention.

FIG. 2 is a comparison diagram of arc resistance before and after parameter identification provided by an embodiment of the present invention.

FIG. 3 is an arc length modulation process provided by an embodiment of the present invention.

FIG. 4 is a schematic diagram of a Chua's circuit of an asymmetric nonlinear resistor provided by an embodiment of the present invention.

FIG. 5( a ) is a waveform diagram of a chaotic signal generated by Chua’s circuit according to an embodiment of the present invention.

Fig. 5(b) is a waveform diagram of a chaotic signal generated by Chua's circuit after the initial value is increased by 5% according to an embodiment of the present invention.

FIG. 6( a ) is a waveform diagram of an electric arc furnace arc voltage simulation provided by an embodiment of the present invention.

FIG. 6( b ) is a waveform diagram of an electric arc furnace arc current simulation provided by an embodiment of the present invention.

FIG. 7( a ) is a waveform diagram of a three-phase voltage on the primary side of a transformer according to an embodiment of the present invention.

FIG. 7(b) is a waveform diagram of a three-phase current on the primary side of a transformer according to an embodiment of the present invention.

FIG. 8 is a volt-ampere characteristic curve diagram of phase A of an electric arc furnace provided by an embodiment of the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

As shown in Figure 1, an electric arc furnace modeling and harmonic analysis method of the present embodiment includes:

S101: Establish an experimental measurement platform, perform experimental measurement on the load of the electric arc furnace, perform Fourier analysis on the measured voltage and current data at each stage of the electric arc furnace smelting process, and obtain the harmonic vector values of each order of voltage and current. Leaf function fitting to obtain the distribution of arc voltage and current.

Perform experimental measurements on different types of electric arc furnaces to obtain voltage and current data in normal operation, then synchronize the measured data, and obtain the voltage and current harmonic phasor values through Fourier analysis.

S102: According to Ohm's law and arc related theory, establish a nonlinear time-varying resistance model of arc resistance.

S103: According to the measured electric arc furnace voltage, current waveform and each harmonic vector value, calculate each parameter required in the electric arc furnace nonlinear time-varying resistance model.

Starting from the internal physical characteristics of the AC arc, according to the arc theory and Ohm's law, the equations satisfied by the arc are simplified and approximated, and factors such as the power factor of the arc furnace, the smelting temperature and the arc current phase angle are taken into account, and a more accurate calculation is obtained. Time-varying resistance model reflecting arc impedance characteristics, specific expression

where F(L(t)) reflects the effect of arc length on arc resistance,

L(t) is the expression for the random variation of arc length; the parameters to be identified are A, B, C' and (D+θ). R represents time-varying resistance.

The main task of parameter identification of the electric arc furnace model is to find a set of optimal parameter vectors λ*, so that the objective function value of the predetermined error can be minimized. The error objective function F _fitness is usually a non-negative function. Error sum of squares for arc voltage:

为电弧电压的模型计算值，n为S101中测量到的样本数目。where U _i is the measured value of arc voltage,

is the model calculated value of arc voltage, and n is the number of samples measured in S101.

Then, using the arc current as the input quantity and the arc voltage as the identification quantity, the four model parameters of A, B, C and (D+θ) in the nonlinear time-varying resistance model of the electric arc furnace are calculated based on the particle swarm algorithm. The specific steps are as follows:

1) Input: arc current I, arc voltage U, arc resistance R and the initial value of the parameter to be identified.

2) Loop: Set the position and speed of each sample point and update it step by step.

3) Calculate F _fitness and compare it with the historical optimal value. If the current value is better, update the parameter value with the current value, and store the calculated 4 model parameters such as A, B, C' and (D+θ).

4) Update the position and velocity of each particle.

5) End the loop and output the parameters to be identified.

In order to verify the model parameter method proposed by the present invention, the model parameter value method based on the results obtained by formula derivation and empirical analysis and calculation is adopted as a comparison method, and the parameter value comparison of a nonlinear time-varying resistance model of an electric arc furnace is carried out. For the specific results, see Table 1:

Table 1

parameter initial value Calculated A 0.1806 0.1812 B 0.0978 0.0986 C' 0.000577 0.000569 (D+θ) -2.7 -2.61

The comparison results of the arc resistance waveforms obtained by the above two model parameter value methods are shown in Figure 2.

S104: According to the external characteristics characterized in the electric arc furnace smelting process, use random signals, Gaussian noise signals and chaotic signals to perform arc length modulation, and set corresponding modulation coefficients.

According to the arc theory and relevant experience in the smelting process of the electric arc furnace, the effect of the arc length on the arc resistance in the smelting process of the electric arc furnace can be characterized by the following expression:

In the formula, rc is the arc radius, L( _t ) is the random fluctuation of the arc length, and its expression is shown in Equation 4:

L(t)=L ₀ +0.5L ₁ ·(1+sin wt) (4)

L ₀ is the minimum arc length during operation, and L ₁ is the maximum change in arc length, that is, the difference between the maximum and minimum values. The selection range of w is usually between 1 Hz and 30 Hz, which is the most sensitive range for human eyes to voltage flicker. In the present invention, w=15 Hz. The rapid and irregular change of arc length is the main cause of arc voltage and current distortion. In order to enable the electric arc furnace simulation model to characterize the typical external characteristics of the electric arc furnace operation, the present invention uses three small signals to modulate the arc length.

Referring to Figure 3, specifically, it includes:

(1) Establish a static fluctuation model of arc length;

(2) Establish a random signal generating circuit, and use the random signal for arc length modulation;

(3) Establish a Gaussian noise signal generating circuit, and use the Gaussian noise signal for arc length modulation;

(4) Establish a chaotic signal generating circuit, and use the chaotic signal for arc length modulation;

(5) Obtain the modulated arc-length random fluctuation model;

The main steps (1) include

A static fluctuation model of arc length is established, and the specific expression is:

L(t)=L ₀ +0.5L ₁ ·(1+sin wt) (4)

Among them, L ₀ is the minimum value of the arc length during operation, and L ₁ is the maximum change value of the arc length, that is, the difference between the maximum value and the minimum value. The selection range of w is usually between 1 Hz and 30 Hz, which is the most sensitive range for human eyes to voltage flicker. In the present invention, w=15 Hz.

The main steps (2) include

Establish a random signal generation circuit, the specific expression is:

singal-1=a·sin wt (5)

Among them, a is the modulation coefficient of the random signal.

The modulation of the arc length is realized by superimposing small signals. The small signal used is actually a·sin wt, and 1 represents the initial expression of the modulated arc length, that is, formula (5).

The random signal can reflect the quasi-periodic and existing low-frequency harmonic components in the operation of the electric arc furnace, and in the present invention, f=10Hz is taken. According to the superposition theorem, the above random signal is added to the arc length for modulation, and the modulated arc length expression is:

L ₁ (t)=L(t)·(1+asin wt) (6)

The main steps (3) include

Establish a Gaussian noise signal generation circuit, the specific expression is:

为随机数模块。where b is the modulation coefficient of the random signal,

is a random number module.

The Gaussian noise signal can reflect the randomness in the operation of the electric arc furnace, and the sampling time t=10 ^-4 s is taken in the present invention. According to the superposition theorem, the above Gaussian noise signal is added to the arc length for modulation, and the modulated arc length expression is:

The main steps (4) include

A chaotic noise signal generation circuit is established, and the specific expression is:

singal-3=c·δ(9)

Among them, c is the modulation coefficient of the random signal, and δ is the chaotic signal.

The chaotic signal can reflect the chaos in the operation of the electric arc furnace. The chaotic signal in the present invention is generated by an asymmetric nonlinear resistance Chua's circuit, and the schematic diagram of the circuit is shown in FIG. 4 . In the present invention, the chaotic signal is generated by the voltage-controlled asymmetric nonlinear resistor Nr in FIG. 4, and the required chaos can be obtained by changing the current value of the inductor L at the initial moment and the voltage value of the capacitors _C1 and _C2 at the initial moment. Signal.

In order to prove the sensitivity of the chaotic signal generated by the chaotic system in Figure 4 to the initial conditions, the parameter value of the energy storage element of the Chua's circuit can be changed, and the initial parameter setting is increased by 0.5%. The simulation waveform obtained before and after adjustment is shown in Figure 5. and Figure 6. It can be seen from the results that the two simulated waveforms deviate within 0.3s, which indicates that applying this signal to the electric arc furnace modeling can make the model show chaos, which is consistent with the actual operating state of the electric arc furnace.

According to the superposition theorem, the above chaotic signal is added to the arc length for modulation, and the modulated arc length expression is:

S105: According to the description of S101-S104, the electric arc furnace model of the present invention is established, and according to the actual power supply system of the electric arc furnace, the parameter values of the electrical components of the electric arc furnace simulation electrical system are calculated and the system construction is completed.

S106: Perform harmonic analysis of the electric arc furnace, calculate the voltage and current value of each harmonic in the smelting process of the electric arc furnace through Fourier analysis, and analyze its influence on the power quality of the power grid.

The calculation accuracy of the arc resistance value and the harmonic voltage value of the time-varying resistance model after model parameter identification and arc length modulation used in this embodiment is compared with that of the traditional nonlinear time-varying resistance model. Figure 2 shows the comparison of the real-time change waveforms of the arc resistance of the two models, and Table 2 shows the comparison of the harmonic voltage content values of the two models with the measured values:

Table 2

It can be seen that, compared with the traditional model, the simulation accuracy of the 2nd to 7th harmonics of the model after parameter optimization and arc length modulation of the present invention is significantly improved, and the operation of the electric arc furnace can be better simulated.

There are many types and tons of electric arc furnaces used in the modern smelting industry. Table 3 shows the accuracy of the calculated and measured values of the harmonic voltage values of three common AC electric arc furnaces, such as steelmaking furnaces, calcium carbide furnaces and ferroalloy furnaces. By comparison, it is proved that the method of the present invention has strong generality and can be applied to the harmonic analysis research of various electric arc furnaces.

table 3

Using the model proposed in this embodiment, a 40t electric arc furnace model is established, and the parameters of each component of its electrical system are as follows:

1) Power supply: phase voltage is 10kV, frequency is 50Hz

2) High voltage transmission line: R ₁ =1.258Ω, X ₁ =3.156Ω

3) Transformer: S=400kVA, R _F =0.620mΩ, X _F =7.12mΩ

4) Short net: R ₂ =2,1mΩ, X ₂ =6mΩ

The change curves of arc voltage and current in the smelting process are shown in Figure 6(a)-Figure 6(b), and the change curves of the three-phase voltage and current on the primary side of the transformer are shown in Figure 7(a)-Figure 7(b). The volt-ampere characteristic curve of the A-phase load of the furnace electrical system is shown in Figure 8. It can be seen from the attached figure that the electric arc furnace has a great contribution to the distortion rate of the grid voltage and current, the three-phase current and voltage are seriously unbalanced, and the THD can reach more than 5.5%; The load characteristic curve of the electric arc furnace is consistent, showing obvious randomness and time-varying; the asymmetry of the positive and negative half cycles is in line with the characteristics of the electric arc furnace electrode when the cathode and anode alternate, which is consistent with the dynamic characteristics of the electric arc furnace steelmaking. The simulation results of the model are basically consistent with the measured results, and are more accurate than the traditional model results.

The embodiment of the present invention also provides an electric arc furnace modeling and harmonic analysis system. include:

The data acquisition module is used to sample the measured voltage and current data at each stage of the electric arc furnace smelting process, and then perform Fourier analysis respectively to obtain the waveform distribution of the voltage and current and the vector value of each harmonic;

The nonlinear time-varying resistance module of the electric arc furnace is used to obtain the static model of the equivalent resistance of the arc according to Ohm's law and the related theory of the arc;

The model parameter identification module is used to identify the parameters of the nonlinear time-varying resistance model of the electric arc furnace through the particle swarm algorithm and based on the measured voltage and current data, and obtain a simulation model with a high degree of fitting with the electric arc furnace;

The electric arc furnace arc length modulation module is used to modulate the rapid and irregular change of the arc length with small signals, so that the simulation model can characterize the external characteristics of the electric arc furnace smelting process, and at the same time adjust the modulation coefficient in this module to conduct the electric arc furnace three. Phase unbalance simulation analysis;

The harmonic analysis module is used to calculate the distortion degree of each harmonic voltage and current in the electric arc furnace smelting process according to the change of the arc equivalent resistance in the electric arc furnace smelting process;

In another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, implements the steps in the electric arc furnace modeling and harmonic analysis shown in FIG. 1 . .

In another embodiment, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implementing the program as shown in FIG. 1 when the processor executes the program. Steps in EAF Modeling and Harmonic Analysis shown.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including but not limited to disk storage, optical storage, and the like.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. During execution, the processes of the embodiments of the above-mentioned methods may be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (19)

1. an electric arc furnace modeling and harmonic analysis method, is characterized in that, comprises the steps: S1: Set the sampling time for the voltage and current data of each stage of the electric arc furnace smelting process, conduct sampling respectively, obtain the waveforms of voltage and current and the vector values of various harmonics, and obtain the actual distribution of arc voltage and current; S2: establish the nonlinear time-varying resistance model of the arc resistance; S3: Based on the particle swarm algorithm, identify the parameters in the nonlinear time-varying resistance model of the electric arc furnace, calculate the model parameters in the nonlinear time-varying resistance model of the electric arc furnace, obtain the expression of the arc time-varying resistance, and fit the arc The change curve of the arc equivalent resistance in the furnace smelting process; S4: Superimpose three small signals such as random signal, Gaussian noise signal and chaotic signal with the static model of arc length, adjust the modulation parameters of each signal, and obtain the modulated random fluctuation model of arc length; S5: According to the above steps, establish an electric arc furnace model based on the PSO algorithm and arc length modulation; S6: Calculate the harmonic voltage and current value of each order in the electric arc furnace smelting process, and carry out harmonic analysis. 2. The method of claim 1, wherein the step S1 comprises: The load of the electric arc furnace is experimentally measured, and the measured voltage and current data at each stage of the electric arc furnace smelting process are subjected to Fourier analysis respectively, and the harmonic vector values of the voltage and current are obtained. Through Fourier function fitting, the arc is obtained. The actual distribution of voltage and current. 3. The method of claim 1, wherein In the step S2, the nonlinear time-varying resistance model of the electric arc furnace, the specific expression is:

Among them, F[L(t)] reflects the influence of arc length on arc resistance, L(t) is the expression of random variation of arc length; the parameters to be identified are A, B, C' and (D+θ); varistor, ω represents the angular velocity. 4. The method of claim 3, wherein In the step S3, identifying the parameters in the nonlinear time-varying resistance model of the electric arc furnace includes: S31: Set the error objective function F _fitness as:

where U _i is the measured value of arc voltage,

is the model calculated value of the arc voltage, and n is the number of samples measured in step S1; Step S32: Using the arc current as the input quantity and the arc voltage as the identification quantity, calculate the four model parameters of A, B, C' and (D+θ) in the nonlinear time-varying resistance model of the electric arc furnace based on the particle swarm algorithm. 5. method as claimed in claim 4, wherein the concrete step of S32 comprises: 1) Input: arc current I, arc voltage U, arc resistance R and the initial value of the parameter to be identified; 2) Loop: Set the position and speed of each sample point and update it gradually; 3) Calculate F _fitness and compare it with the historical optimal value. If the current value is better, update the parameter value with the current value, and store the calculated A, B, C' and (D+θ) 4 model parameters; 4) Update the position and velocity of each sample particle; 5) End the loop and output the parameters to be identified. 6. The method of claim 5, wherein S4 specifically comprises: 1) Establish a static fluctuation model of arc length; 2) Establish a random signal generating circuit, and use the random signal for arc length modulation; 3) Establish a Gaussian noise signal generating circuit, and use the Gaussian noise signal for arc length modulation; 4) Establish a chaotic signal generating circuit, and use the chaotic signal for arc length modulation; 5) The modulated arc-length random fluctuation model is obtained. 7. method as claimed in claim 6, is characterized in that, wherein in step 1), set up electric arc arc length static fluctuation model, concrete expression is: L(t)=L ₀ +0.5L ₁ ·(1+sin wt) Among them, L ₀ is the minimum value of the arc length during operation, L ₁ is the maximum change value of the arc length, that is, the difference between the maximum value and the minimum value, and the selection range of w is between 1Hz-30Hz. 8. The method of claim 6, wherein step 2) comprises: Establish a random signal generation circuit, the specific expression is: singal-1=a·sin wt Among them, a is the modulation coefficient of the random signal; The random signal reflects the quasi-periodic and existing low-frequency harmonic components in the operation of the electric arc furnace. Take f=10Hz, and add the random signal to the arc length for modulation. The modulated arc length expression is: L ₁ (t)=L(t)·(1+a·sinwt). 9. The method of claim 6, wherein step 3) comprises: Establish a Gaussian noise signal generation circuit, the specific expression is:

where b is the modulation coefficient of the random signal,

is a random number module. The Gaussian noise signal reflects the randomness in the operation of the electric arc furnace. The sampling time t=10 ^-4 s is taken, and the Gaussian noise signal is added to the arc length for modulation. The modulated arc length expression is:

. 10. The method of claim 6, wherein step 4) comprises: The chaotic signal generation circuit is established, and the specific expression is: singal-3=c·δ Among them, c is the modulation coefficient of the random signal, and δ is the chaotic signal; The chaotic signal is generated by an asymmetric nonlinear resistance Chua's circuit, and the chaotic signal is added to the arc length for modulation. The modulated arc length expression is:

. 11. A computer-readable storage medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the electric arc furnace modeling and harmonic analysis according to any one of claims 1-10 are realized steps in the method. 12. A computer device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any of claims 1-10 when the processor executes the program. Steps in a described method for electric arc furnace modeling and harmonic analysis. 13. An electric arc furnace modeling and harmonic analysis system, characterized in that, comprising: The data acquisition module is used to sample the measured voltage and current data at each stage of the electric arc furnace smelting process, and then perform Fourier analysis respectively to obtain the waveform distribution of the voltage and current and the vector value of each harmonic; Arc furnace nonlinear time-varying resistance module, which is used to obtain a static model of the arc equivalent resistance; The model parameter identification module is used to identify the parameters in the nonlinear time-varying resistance model of the electric arc furnace through the particle swarm algorithm, calculate the model parameters in the nonlinear time-varying resistance model of the electric arc furnace, and obtain the expression of the arc time-varying resistance. , and fit the variation curve of the arc equivalent resistance during the smelting process of the electric arc furnace; Arc furnace arc length modulation module, which superimposes three small signals such as random signal, Gaussian noise signal and chaotic signal with the static arc length model, adjusts the modulation parameters of each signal, and obtains the modulated arc length random fluctuation model; The harmonic analysis module is used to calculate the distortion degree of each harmonic voltage and current in the electric arc furnace smelting process according to the change of the arc equivalent resistance in the electric arc furnace smelting process. 14. The system of claim 13, wherein The specific expression of the nonlinear time-varying resistance model of the electric arc furnace is:

Among them, F[L(t)] reflects the influence of arc length on arc resistance, L(t) is the expression of random variation of arc length; the parameters to be identified are A, B, C' and (D+θ); varistor, ω represents the angular velocity. 15. The system of claim 13, wherein The model parameter identification module identifies parameters in the nonlinear time-varying resistance model of the electric arc furnace, including: Set the error objective function F _fitness as:

where U _i is the measured value of arc voltage,

is the model calculated value of the arc voltage, and n is the number of samples measured in step S1; Taking the arc current as the input quantity and the arc voltage as the identification quantity, the four model parameters of A, B, C' and (D+θ) in the nonlinear time-varying resistance model of the electric arc furnace were calculated based on the particle swarm algorithm. 16. The system of claim 13, wherein in the electric arc furnace arc length modulation module, A static fluctuation model of arc length is established, and the specific expression is: L(t)=L ₀ +0.5L ₁ ·(1+sinwt) Among them, L ₀ is the minimum value of the arc length during operation, L ₁ is the maximum change value of the arc length, that is, the difference between the maximum value and the minimum value, and the selection range of w is between 1Hz-30Hz. 17. The system of claim 16, wherein in the electric arc furnace arc length modulation module, Establish a random signal generation circuit, the specific expression is: singal-1=a·sinwt Among them, a is the modulation coefficient of the random signal; The random signal reflects the quasi-periodic and existing low-frequency harmonic components in the operation of the electric arc furnace. Take f=10Hz, and add the random signal to the arc length for modulation. The modulated arc length expression is: L ₁ (t)=L(t)·(1+a·sinwt). 18. The system of claim 16, wherein in the electric arc furnace arc length modulation module, Establish a Gaussian noise signal generation circuit, the specific expression is:

where b is the modulation coefficient of the random signal,

is a random number module. The Gaussian noise signal reflects the randomness in the operation of the electric arc furnace. The sampling time t=10 ^-4 s is taken, and the Gaussian noise signal is added to the arc length for modulation. The modulated arc length expression is:

. 19. The system of claim 16, wherein in the electric arc furnace arc length modulation module, The chaotic signal generation circuit is established, and the specific expression is: singal-3=c·δ Among them, c is the modulation coefficient of the random signal, and δ is the chaotic signal; The chaotic signal is generated by an asymmetric nonlinear resistance Chua's circuit, and the chaotic signal is added to the arc length for modulation. The modulated arc length expression is:

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