An inverse tangent segmented DC arc noise model and its parameter extraction method

Abstract

The present invention discloses an inverse tangent segmented DC arc noise model and a parameter extraction method thereof, the steps comprising: 1) using a current sensor to collect arc current signals; 2) calculating the spectrum distribution of the current signal; 3) using the inverse tangent segmented DC arc noise model to fit the spectrum distribution of the current signal; 4) selecting the arc noise model parameters to be extracted and constructing an objective function; 5) using a generalized hierarchical reflective chaos victory optimization algorithm to realize the extraction of the inverse tangent segmented arc noise model parameters. The present invention uses the inverse tangent segmented DC arc noise model to accurately fit the complex nonlinear characteristics of the DC arc noise spectrum under different working conditions, and at the same time uses a fitness distance balance algorithm, an average position information algorithm, and a reflective learning mechanism to improve the chaos victory optimization algorithm, which can improve the convergence accuracy of the algorithm and avoid local optimality, and can realize the accurate extraction of the inverse tangent segmented arc noise model parameters.

Description

Technical Field

[0001] The invention relates to an arc tangent segmented direct current arc noise model and a parameter extraction method thereof, and belongs to the field of arc fault detection. Technical Background

[0002] In order to cope with energy shortages and environmental pollution, replacing traditional fossil energy with renewable energy is an urgent problem that countries around the world need to solve. Among renewable energy sources, photovoltaic energy has the advantages of cleanliness, high abundance, and low geographical restrictions. In 2021, the global photovoltaic power generation installed capacity will increase by 180GW. Photovoltaic energy is considered to be one of the most promising alternative energy sources. However, during the long-term outdoor operation of the photovoltaic system, due to the effects of external stresses such as weather, animal gnawing, and high working voltage, the internal cables and terminals will deteriorate, which is prone to DC arc faults and fire accidents. Compared with AC arc faults, DC arc faults lack zero crossing points and are difficult to extinguish by themselves. Therefore, DC arc faults are more likely to produce continuous arcing. Standard UL1699B requires that photovoltaic systems with an operating voltage higher than 80V should have arc fault detection functions. In photovoltaic systems, DC arc faults are divided into series arc faults and parallel arc faults. Parallel arc faults trigger circuit overcurrent, and traditional protection devices can easily detect parallel arc faults. A series arc fault is equivalent to introducing a randomly fluctuating impedance into the line, which can cause a slight drop in current. It is difficult for traditional protection devices to detect series arc faults. Therefore, series arc faults are more harmful to photovoltaic systems than parallel arc faults. Series arc faults are highly random and hidden, and how to effectively detect series arc faults in photovoltaic systems is a challenging problem. Therefore, this application focuses on solving the problem of arc faults in DC systems.

[0003] Some scholars have studied arc fault detection methods based on perfect experimental data. However, in actual photovoltaic systems, collecting perfect DC arc fault experimental data at different locations and working conditions is costly, and the generation of DC arc faults is dangerous. Arc models can provide a more flexible and lower-cost means for studying arc fault detection methods, and model-based methods have become an important aspect of arc fault detection method research.

[0004] DC arc fault is a chaotic phenomenon with strong randomness. Arc modeling methods are usually divided into two categories: physical models and black box models. The physical model based on finite element simulation can accurately describe the microscopic physical mechanism of arc plasma discharge, but the physical model is too complex to be applied to the research of arc fault detection methods. The black box model regards the arc as an element in the circuit and is mainly used to describe the external characteristics of the arc without paying attention to its complex physical mechanism. Therefore, this application focuses on the black box model. The polynomial model can simulate the dynamic characteristics of arc noise, but the operation of the polynomial model requires additional experimental data. For example, if a polynomial model for calculating arc current is established, the arc current needs to be calculated based on the actual voltage data. Therefore, the polynomial model cannot completely get rid of the dependence on experimental data in practical applications. Some researchers fit arc noise based on the white noise model. Arc noise exists in the form of colored noise, and the white noise model cannot simulate the nonlinear characteristics of the spectrum. Some researchers use the pink noise model to simulate arc noise. The spectrum energy of the pink noise model is positively correlated with the negative first power of the frequency value. However, the fixed form of the pink noise model cannot accurately fit the various nonlinear characteristics of the arc spectrum. The segmented noise model is an improved form of the pink noise model. Compared with the pink noise model, the segmented noise model can provide more accurate fitting performance. However, the segmented noise model is difficult to accurately describe the strong nonlinearity of the arc spectrum shape through a single descent rate value, so it is necessary to further improve the segmented noise model.

[0005] The accuracy of parameter extraction has an important impact on the performance of the arc model. Grid search and least squares methods have been applied to arc model parameter extraction, but both methods have the problems of low efficiency and insufficient parameter extraction accuracy. Metaheuristic algorithms do not need to rely on the mathematical mechanism of the problem to be solved. They have the advantages of simple structure and insensitivity to initial values, and can effectively solve the problem of arc model parameter extraction. Some scholars use genetic algorithms, simulated annealing algorithms, particle swarm algorithms and cuckoo search to extract arc model parameters, but these algorithms have low search efficiency and are prone to premature in the iterative process. Therefore, in order to more accurately extract arc fault model parameters, it is necessary to further study new optimization algorithms. Summary of the invention

[0006] In order to solve the problems existing in the arc fault modeling and parameter extraction process, this invention proposes an inverse tangent segmented DC arc noise model and a generalized hierarchical reflection chaos victory optimization algorithm.

[0007] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0008] The present invention first provides an inverse tangent segmented DC arc noise model. The mathematical expression of the inverse tangent segmented DC arc noise model is shown in formula (1):

[0009]

[0010] Among them, f, f0 and L are the frequency value, frequency turning point and frequency energy amplitude control parameters respectively. w (f) represents the model output spectrum curve and the model input reference spectrum curve, respectively. Arctan(·) is the inverse tangent function. As the frequency value increases, the decreasing speed of the spectrum energy is controlled based on parameters k1, k2 and k3. The parameters of the inverse tangent segmented DC arc noise model include f0, L, k1, k2 and k3;

[0011] The present invention also provides a parameter extraction method for an inverse tangent segmented DC arc noise model, comprising the following steps:

[0012] Step 1): using a current sensor to collect DC arc current data, and obtaining the spectrum distribution of the arc current based on Fourier transform;

[0013] Step 2): Based on the analysis of the arc current spectrum distribution properties, an inverse tangent segmented DC arc noise model is constructed. The inverse tangent segmented DC arc noise model is shown in formula (1);

[0014]

[0015] Among them, f, f0 and L are the frequency value, frequency turning point and frequency energy amplitude control parameters respectively. w (f) represents the model output spectrum curve and the model input reference spectrum curve, respectively. Arctan(·) is the inverse tangent function. As the frequency value increases, the decreasing speed of the spectrum energy is controlled based on parameters k1, k2 and k3. The parameters of the inverse tangent segmented DC arc noise model include f0, L, k1, k2 and k3;

[0016] Step 3): Design the objective function and determine the parameters of the arc tangent segmented DC arc noise model to be extracted;

[0017] Step 4): Extracting parameters to be extracted based on the arc model.

[0018] The present invention provides a generalized hierarchical reflection chaos victory optimization algorithm, which analyzes the importance of different individuals based on the fitness distance balance algorithm, thereby obtaining the level values ​​of different individuals in the population of the optimization algorithm, and can fully integrate distance information and fitness information when analyzing the contribution of individuals to population evolution, and more effectively guide the population to evolve in the optimal direction. Secondly, the average position information of three dominant individuals (the three individuals with the lowest level values) and the average position information of three disadvantaged individuals (the three individuals with the highest level values) can be used to enhance the diversity of the population. At the same time, the reflective learning mechanism is used to enable the population to learn the difference between current evolutionary information and past evolutionary information, which can weaken the precocity of the population.

[0019] Preferably, the generalized hierarchical reflection chaos victory optimization algorithm specifically includes the following steps:

[0020] Step 4-1): Set the maximum number of iterations, the population size of the optimization algorithm, and randomly generate the positions of individuals in the population;

[0021] Step 4-2): Calculate the fitness value of each individual in the population of the optimization algorithm according to the objective function;

[0022] Step 4-3): Use the fitness distance balance algorithm to calculate the fitness distance balance value of different individuals in the population. The fitness distance balance algorithm is shown in formula (2):

[0023]

[0024] Among them, FD j is the fitness distance equilibrium value of individual j. The undetermined parameter μ∈(0,1), fit(x j ) is the fitness value of individual j, fit(x b ) is the fitness value corresponding to the individual with the smallest fitness value in the population. j is the current position of individual j, x b is the position corresponding to the individual with the smallest fitness value in the population.

[0025] Each individual j in the population of the optimization algorithm calculates the corresponding fitness distance balance value according to formula (2), and then ranks the importance of individuals in the population according to the size of the fitness distance balance value. The smaller the fitness distance balance value, the higher the importance of the individual (the lower the rank value). The rank value is the ranking value of different individuals. The ranking value corresponding to the individual with the smallest fitness distance balance value is 1, and the ranking value corresponding to the individual with the second smallest fitness distance balance value is 2. Similarly, the ranking value corresponding to the individual with the largest fitness distance balance value is NP, where NP is the number of individuals in the population.

[0026] Step 4-4): Use formula (3) and formula (4) to update the positions of non-optimal individuals and optimal individuals in the population of the optimization algorithm:

[0027]

[0028]

[0029] Among them, r1 and r2 are two random numbers that obey uniform distribution in the interval [0,1]. is the updated value of the i-th dimension of the j-th individual position. j,i is the value of the jth dimension of the current position of the i-th individual. The result of sorting according to the fitness distance equilibrium value is: is the average value of the i-th dimension of the three individuals with the smallest ranking value in the population, It is the average value of the i-th dimension of the three individuals with the largest ranking value in the population. b With Fit w are the fitness distance equilibrium values ​​of the best individual and the worst individual in the population, respectively. cr1 and cr2 are random numbers in the interval [0,1] generated based on the Tent chaotic mapping function. The expression of the Tent chaotic mapping is shown in formula (5):

[0030]

[0031] Where 0 <cr lt <1.

[0032] The position of individual j is calculated by formula (6).

[0033]

[0034] In the formula, For location The corresponding fitness value is For location The corresponding fitness value.

[0035] Step 4-5): Further adjust the position of individuals in the optimization algorithm population based on the reflective learning mechanism. The reflective learning mechanism is shown in formula (7):

[0036]

[0037]

[0038] In the formula, are individuals randomly selected from the previous generation population; is a randomly selected individual in the population of this iteration; rstore(·) is a random sorting operator; the position of individual j is calculated by formula (6); r3 is a random number in the interval [0,1] generated by uniform distribution:

[0039] Step 4-6): Calculate the current number of iterations. If the current number of iterations is greater than the maximum number of iterations, go to step 4-8), otherwise go to step 4-7);

[0040] Step 4-7): Increase the number of iterations by 1 and go to step 4-2);

[0041] Step 4-8): The result of the inverse tangent segmented DC arc noise model parameter extraction is the position vector of the optimal individual in the population of the optimization algorithm.

[0042] The beneficial effects of the present invention are:

[0043] 1. The present invention proposes an inverse tangent segmented DC arc noise model, which makes the frequency energy decrease at a nonlinear rate as the frequency value increases based on the inverse tangent function. Compared with the traditional segmented DC arc noise model, the output of the inverse tangent segmented DC arc noise model can more accurately fit the complex nonlinear variation law of energy value in the frequency energy turning interval of the spectrum.

[0044] 2. The present invention proposes a generalized hierarchical reflective chaos victory optimization algorithm by introducing a fitness distance balance algorithm, an average position information algorithm and a reflective learning mechanism into the chaos victory optimization algorithm. Based on the fitness distance balance algorithm, the importance of different individuals is analyzed, so that different individuals in the population of the optimization algorithm are sorted and the rank values ​​of different individuals are obtained, which overcomes the limitation that the information source of the traditional population individual sorting method that only focuses on the fitness value is too single. By integrating the information of the two dimensions of spatial distance and fitness value to analyze the importance of individuals, the convergence speed of the algorithm can be accelerated. Secondly, the average position information of the three dominant individuals (the three individuals with the lowest rank values) and the average position information of the three disadvantaged individuals (the three individuals with the highest rank values) are used to guide the population to evolve in the optimal direction, which can enhance the diversity of evolutionary directions. The reflective learning mechanism is used to enable the population to learn the difference between the current evolutionary information and the past evolutionary information, thereby weakening the premature phenomenon in the evolutionary process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of realizing arc model parameter optimization based on generalized hierarchical reflection chaos victory optimization algorithm according to one embodiment of the present invention;

[0046] Figure 2 The arc noise spectrum output by the arc tangent segmented DC arc noise model according to an embodiment of the present invention and the actual arc current noise spectrum;

[0047] Figure 3 is an arc noise spectrum output by a pink noise model of an embodiment of the present invention and an actual arc current noise spectrum;

[0048] Figure 4 It is an arc noise spectrum output by a segmented DC noise model and an actual arc current noise spectrum according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, this embodiment provides an inverse tangent segmented DC arc noise model and a parameter extraction method thereof, comprising the following steps:

[0051] Step 1): The arc current signal is collected at a frequency of 200 kHz based on a current sensor, and the frequency spectrum of the current signal is obtained using a fast Fourier transform method.

[0052] Step 2): Construct an inverse tangent segmented DC arc noise model based on the distribution curve characteristics of the arc current spectrum, as shown in formula (1):

[0053]

[0054] Among them, f, f0 and L are the frequency value, frequency turning point and frequency energy amplitude control parameters respectively. w (f) represents the model output spectrum curve and the model input reference spectrum curve, respectively, and arctan(·) is the inverse tangent function. As the frequency value increases, the decreasing speed of the spectrum energy is controlled based on parameters k1, k2 and k3. The parameters of the inverse tangent segmented DC arc noise model include f0, L, k1, k2 and k3.

[0055] Step 3): Construct the objective function based on the form of root mean square error:

[0056]

[0057] Here, NE represents the number of data points. In this embodiment, the NE value is set to 20, that is, the frequency domain of 0 to 100 kHz is evenly divided into 20 segments.

[0058] e(·) is the error equation, and its expression is shown in formula (12):

[0059]

[0060] In the formula, is the sum of the spectrum energy of the arc current in frequency band i. i Po is the sum of the spectrum energy of the analog data in frequency band i. i Refers to how many data points are contained in frequency band i.

[0061] Step 4): Optimize the parameters to be extracted in the inverse tangent segmented DC arc noise model based on the optimization algorithm. The optimization algorithm is a generalized hierarchical reflective chaos victory optimization algorithm. The importance of different individuals is analyzed based on the fitness distance balance algorithm, so as to obtain the level values ​​of different individuals in the population of the optimization algorithm. It can fully integrate the distance information and fitness information when analyzing the contribution of individuals to the evolution of the population, and more effectively guide the population to evolve in the optimal direction. Secondly, the average position information of the three dominant individuals (the three individuals with the lowest level values) and the average position information of the three disadvantaged individuals (the three individuals with the highest level values) can be used to enhance the diversity of the population of the optimization algorithm. At the same time, the reflective learning mechanism is used to enable the population to learn the difference between the current evolutionary information and the past evolutionary information, which can weaken the precocity of the population.

[0062] Step 4-1): Set the maximum number of iterations to 500, the population size to 20, and randomly generate the positions of individuals in the population;

[0063] Step 4-2): Calculate the fitness value of each individual in the optimization algorithm population according to the objective function;

[0064] Step 4-3): Use the fitness distance balance algorithm to calculate the fitness distance balance value of different individuals in the population of the optimization algorithm. The fitness distance balance algorithm is shown in formula (4):

[0065]

[0066] Among them, FD j is the fitness distance equilibrium value of individual j. The undetermined parameter μ=0.5, fit(x j ) is the fitness value of individual j, fit(x b ) is the fitness value corresponding to the individual with the smallest fitness value in the population. j is the current position of individual j, x b is the position corresponding to the individual with the smallest fitness value in the population.

[0067] Each individual j in the population of the optimization algorithm calculates the corresponding fitness distance balance value according to formula (4), and then ranks the importance of the individuals in the population according to the size of the fitness distance balance value. The smaller the fitness distance balance value, the higher the importance of the individual (the lower the rank value). The rank value is the ranking value of different individuals. The ranking value corresponding to the individual with the smallest fitness distance balance value is 1, and the ranking value corresponding to the individual with the second smallest fitness distance balance value is 2. By analogy, the ranking value corresponding to the individual with the largest fitness distance balance value is NP, where NP is the number of individuals in the population (NP=20 in this example).

[0068] Step 4-4): Use formula (5) and formula (6) to update the positions of non-optimal individuals and optimal individuals in the population of the optimization algorithm:

[0069]

[0070]

[0071] Among them, r1 and r2 are two random numbers that obey uniform distribution in the interval [0,1]. is the updated value of the i-th dimension of the j-th individual position. j,i is the value of the jth dimension of the current position of the i-th individual. The result of sorting according to the fitness distance equilibrium value is: is the average value of the i-th dimension of the three individuals with the smallest ranking value in the population, It is the average value of the i-th dimension of the three individuals with the largest ranking value in the population. b With Fit w are the fitness distance equilibrium values ​​of the best individual and the worst individual in the population, respectively. cr1 and cr2 are random numbers in the interval [0,1] generated based on the Tent chaotic mapping function. The expression of the Tent chaotic mapping is shown in formula (7):

[0072]

[0073] Where 0 <cr It <1.

[0074] Whether to retain the updated individual position is determined based on the individual's fitness value. The position of individual j is calculated by formula (8).

[0075]

[0076] In the formula, For location The corresponding fitness value is For location The corresponding fitness value.

[0077] Step 4-5): Further adjust the position of individuals in the optimization algorithm population based on the reflective learning mechanism. The reflective learning mechanism is shown in formula (9):

[0078]

[0079]

[0080] In the formula, are individuals randomly selected from the previous generation population; is an individual randomly selected from the population in this iteration; rstore(·) is a random sorting operator; the position of individual j is calculated by formula (8); r3 is a random number in the interval [0,1] generated by a uniform distribution.

[0081] Step 4-6): Calculate the current number of iterations. If the current number of iterations is greater than the maximum number of iterations, go to step 4-8), otherwise go to step 4-7).

[0082] Step 4-7): Increase the number of iterations by 1 and go to step 4-2).

[0083] Step 4-8): The result of the inverse tangent segmented DC arc noise model parameter extraction is the position vector of the optimal individual in the population.

[0084] The above embodiment can realize accurate identification of the parameters of the inverse tangent segmented DC noise model. The arc noise spectrum output by the inverse tangent DC arc noise model is similar to the actual arc current noise spectrum. Figure 2 As shown. The spectrum of the model output data can accurately fit the complex nonlinearity of the actual current noise signal spectrum, and the root mean square error calculated by formula (2) is 0.0417 (shown in Table 1). The root mean square errors obtained by optimizing the model parameter identification based on the chaos victory optimization algorithm, the teaching and learning optimization algorithm, the whale optimization algorithm and the particle swarm optimization algorithm are 0.0420, 0.0421, 0.0423 and 0.0495 respectively. The parameter identification accuracy of the generalized hierarchical reflection chaos victory optimization algorithm has better convergence accuracy than the other four comparison algorithms. In the generalized hierarchical reflection chaos victory optimization algorithm, the importance of different individuals is analyzed based on the fitness distance balance algorithm, so as to obtain the level values ​​of different individuals in the population of the optimization algorithm. Secondly, the average position information of the three dominant individuals (the three individuals with the lowest level values) and the average position information of the three disadvantaged individuals (the three individuals with the lowest level values) are used to guide the population to accelerate the convergence speed to the optimal direction. At the same time, the reflective learning mechanism is used to enable the population to learn the difference between the current evolutionary information and the past evolutionary information, which can weaken the premature phenomenon of the population. The experimental results prove the advanced nature of the generalized hierarchical reflective chaos victory optimization algorithm proposed by the present invention.

[0085] Table 1 Results of the inverse tangent segmented DC arc noise model parameters identified based on different optimization algorithms

[0086]

[0087] The arc noise spectrum output by the pink noise model and the actual arc current noise spectrum are shown in the figure below: Figure 3 The arc noise spectrum output by the segmented DC noise model and the actual arc current noise spectrum are shown in Figure 4 shown.

[0088] pass Figure 2 and Figure 3 It can be seen from the comparison that in the frequency bands of [7kHz, 20kHz] and [50kHz, 100kHz], the proposed arc tangent segmented DC noise model has a significantly better fitting accuracy for the actual arc current noise spectrum than the pink noise model. This is because the energy of the pink noise model is positively correlated with the negative first power of the frequency value in the entire frequency band, while the spectrum of the arc noise shows an obvious segmented characteristic in the entire frequency band.

[0089] pass Figure 2 and Figure 4 From the comparison, we can see that in the [1kHz, 3kHz] frequency band, the spectrum energy output by the segmented DC noise model is significantly lower than the spectrum energy corresponding to the actual data, while the spectrum energy output by the inverse tangent segmented DC noise model effectively overlaps with the spectrum energy corresponding to the actual data. In other frequency bands, the performance difference between the segmented DC noise model and the segmented DC noise model is difficult to display intuitively.

[0090] The embodiment calculates the root mean square error (modeling error) obtained when different models fit the actual arc current data spectrum. The smaller the root mean square error, the higher the model accuracy. The root mean square error of the traditional pink noise model is 0.1155, and the root mean square error of the segmented DC arc noise model is 0.0812. The root mean square error of the inverse tangent segmented DC noise model proposed in the present invention is only 0.0417. The root mean square error of the pink noise model and the root mean square error of the segmented DC arc noise model are 2.76 times and 1.94 times the root mean square error of the inverse tangent segmented DC noise model, respectively. It shows that the fitting accuracy of the inverse tangent segmented DC noise model is higher than that of the traditional pink noise model and the segmented DC noise model. Moreover, the inverse tangent segmented DC noise model can make the model output spectrum decrease in variable power by using the inverse tangent function, while the spectrum decrease rate of the segmented DC noise model is a constant value, so the inverse tangent segmented DC noise model proposed in the present invention has stronger flexibility than the segmented DC noise model.

[0091] The above experimental results show that compared with the pink noise model and the segmented DC arc noise model, the arc tangent segmented DC noise model proposed in the present invention can more accurately characterize the complex nonlinear characteristics of the arc noise spectrum.

[0092] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A parameter extraction method for an inverse tangent segmented DC arc noise model, characterized in that: The following steps are involved: Step 1): using a current sensor to collect DC arc current data, and obtaining the spectrum distribution of the arc current based on Fourier transform; Step 2): Based on the analysis of the arc current spectrum distribution properties, an inverse tangent segmented DC arc noise model is constructed. The inverse tangent segmented DC arc noise model is shown in formula (1): The parameters of the arc tangent segmented DC arc noise model include f0, L, k1, k2 and k3, where f, f0 and L are the frequency value, frequency turning point and frequency energy amplitude control parameters respectively; S(f) and S w (f) represents the model output spectrum curve and the model input reference spectrum curve, arctan(·) is the inverse tangent function; as the frequency value increases, the decreasing speed of the spectrum energy is controlled based on parameters k1, k2 and k3; Step 3): Design the objective function and determine the parameters of the arc tangent segmented DC arc noise model to be extracted; Step 4): Extract the parameters to be extracted from the arc noise model based on the generalized hierarchical reflection chaos victory optimization algorithm, the generalized hierarchical reflection chaos victory optimization algorithm introduces the fitness distance balance algorithm, the average position information algorithm and the reflection learning mechanism into the chaos victory optimization algorithm, calculates and sorts the fitness distance balance value of each individual based on the fitness distance balance algorithm, embeds the fused position information into the chaos victory mechanism based on the average position information algorithm to more accurately guide the update direction of the individual position, and further adjusts and optimizes the individual position based on the reflection learning mechanism. Specifically, The fitness distance balance algorithm ranks the importance of each individual based on the size of the fitness distance balance value. The smaller the fitness distance balance value, the higher the importance of the individual and the lower the grade value. The grade value is the ranking value of different individuals. The ranking value corresponding to the individual with the smallest fitness distance balance value is 1, and the ranking value corresponding to the individual with the second smallest fitness distance balance value is 2... and so on. The ranking value corresponding to the individual with the largest fitness distance balance value is NP, where NP is the number of individuals in the population. The expression of the fitness distance balance algorithm is shown in formula (2): Among them, FD j is the fitness distance equilibrium value of individual j, with undetermined parameter μ∈(0,1), fit j is the fitness value of individual j, fit b is the fitness value corresponding to the individual with the smallest fitness value in the population; x j is the current position of individual j, x b is the fitness value corresponding to the individual with the smallest fitness value in the population; The chaotic victory mechanism based on the average position information algorithm updates the positions of the non-optimal individuals and the optimal individuals in the population of the optimization algorithm through formula (3) and formula (4): Among them, r1 and r2 are two random numbers that obey uniform distribution in the interval [0, 1]. is the updated value of the i-th dimension of the j-th individual position, x j，i is the value of the jth dimension of the current position of the i-th individual; the result of sorting according to the fitness distance equilibrium value, is the average value of the i-th dimension of the three individuals with the smallest ranking value in the population, is the average value of the i-th dimension of the three individuals with the largest ranking value in the population; FD b With FD w are the fitness distance equilibrium values ​​of the best individual and the worst individual in the population respectively; cr1 and cr2 are random numbers in the interval [0, 1] generated based on the Tent chaotic mapping function.

2. The parameter extraction method of the arc tangent segmented DC arc noise model according to claim 1, characterized in that: In step 4), the position information of different individuals in the population of the optimization algorithm is updated using the generalized hierarchical reflective chaos victory mechanism to simulate the learning process of scholars in a certain field. The iteration is performed until the preset maximum number of iterations is reached. At this time, the result of the inverse tangent segmented DC arc noise model parameter extraction is the position vector of the optimal individual in the population of the optimization algorithm.

3. The parameter extraction method of the arc tangent segmented DC arc noise model according to claim 1, characterized in that: The expression of the Tent chaotic map is shown in formula (5): Where 0 <cr It <1.

4. The parameter extraction method of the arc tangent segmented DC arc noise model according to claim 1, characterized in that: Whether to retain the updated individual position is determined based on the individual's fitness value. The position of individual j is calculated by formula (6): In the formula, For location The corresponding fitness value is For location The corresponding fitness value.

5. The parameter extraction method of the arc tangent segmented DC arc noise model according to claim 4, characterized in that: In step 4), the position of individuals in the optimization algorithm population is further adjusted based on the reflective learning mechanism. The reflective learning mechanism is shown in formula (7): In the formula, are individuals randomly selected from the previous generation population; is a randomly selected individual in the population of this iteration; rstore(·) is a random sorting operator; the position of individual j adjusted by the reflective learning mechanism is Substitute into formula (6) to determine whether to keep the adjusted position; r3 is a random number generated by uniform distribution and located in the interval [0, 1].

6. The parameter extraction method of the arc tangent segmented DC arc noise model according to claim 1, characterized in that: In step 3), the objective function is constructed based on the form of root mean square error: Where NE represents the number of data points, the frequency band of the arc current is evenly divided into NE segments, and e(·) is the error equation, which is expressed as shown in formula (10): In the formula, is the energy sum of the i-th frequency band of the arc current; Sum i Po is the energy sum of the i-th frequency band of the simulated data in FB(i); i Refers to how many data points are contained in frequency band i.

7. An inverse tangent segmented DC arc noise model, based on the parameter extraction method of the inverse tangent segmented DC arc noise model according to claim 1, characterized in that: As shown below: The parameters of the arc tangent segmented DC arc noise model include f0, L, k1, k2 and k3, where f, f0 and L are the frequency value, frequency turning point and frequency energy amplitude control parameters respectively; S(f) and S w (f) represent the model output spectrum curve and the model input reference spectrum curve respectively, arctan(·) is the inverse tangent function; as the frequency value increases, the decreasing speed of the spectrum energy is controlled based on parameters k1, k2 and k3.

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