WO2021043027A1 - Machine learning-based direct current fault arc detection method for photovoltaic system - Google Patents

Abstract

A machine learning-based direct current fault arc detection method for a photovoltaic system, comprising the following steps: building a random forest model, a support vector machine model and a decision tree model respectively, and performing training (S1); collecting a real-time current signal of a photovoltaic array (1) (S2); analyzing the real-time current signal to obtain a time-domain feature and a frequency-domain feature (S3); inputting the obtained time-domain feature and frequency-domain feature into the trained random forest model, support vector machine model and decision tree model respectively to obtain respective status label values (S4); setting the sum of all status label values as the total status label value, and determining whether the total status label value is greater than or equal to two (S5); when the determination result is yes, increasing the determination frequency value of a determination frequency counter by one, and further determining whether the increased determination frequency value is equal to a predetermined determination frequency value (S7); and if the increased determination frequency value is equal to the predetermined determination frequency value, then a circuit breaker operates so as to disconnect a circuit, and an alarm message is issued (S8).

Description

A DC fault arc detection method for photovoltaic system based on machine learning Technical field

The invention belongs to the technical field of photovoltaic electrical fault detection, and in particular relates to a method for detecting a DC fault arc of a photovoltaic system based on machine learning.

Background technique

Arc is a kind of gas discharge phenomenon, which refers to the instant spark produced by some insulating medium (such as air) when current passes through. It is a kind of gas discharge phenomenon. Arc discharge is a self-sustaining discharge, which is distinguished from other types of discharge in that the sustaining voltage of arc discharge is very low. At present, it is difficult to give a strict definition to arc discharge. Simply speaking from the electrical characteristics of the discharge, arc discharge is a discharge with a reduced cathode position and a large current density. Generally speaking, it has negative volt-ampere characteristics.

In the photovoltaic system, once a fault arc is generated, if timely and effective protective measures are not taken, the continuous DC arc will generate a high temperature of more than 3000 ℃, which will cause a fire. In recent years, many fire accidents caused by fault arcs have occurred in Europe and America, causing equipment damages of varying degrees. In 2011, the National Electrical Code (NEC) stipulates that photovoltaic systems should be equipped with detection devices and circuit breakers to detect arc faults. The Underwriters Laboratories (UL) also introduced corresponding development test methods and mechanisms.

At present, most of the detection methods proposed by researchers conduct passive detection for the characteristics of the arc. The disadvantage is that in some large current situations, the performance of the arc characteristics is not very obvious, and it is easy to cause false detection. Once the false detection occurs, it will cause the shutdown of the entire photovoltaic system and cause unnecessary losses.

Summary of the invention

The present invention is made to solve the above technical problems, and aims to provide a machine learning-based photovoltaic system DC fault arc detection method that has obvious arc characteristics and is not easy to cause false detection.

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

The present invention provides a photovoltaic system DC fault arc detection method based on machine learning. It has the feature that the detection method is realized by a photovoltaic system, and the photovoltaic system includes a photovoltaic array, a combiner box, an inverter, and an AC power grid that are sequentially connected to each other. There is a current collecting device between the combiner box and the circuit connected to the inverter, and a switch is provided between the photovoltaic array and the combiner box. The detection method includes the following steps:

Step S1: Build a random forest model, a support vector machine model, and a decision tree model respectively, set the state label values of the normal working state and the fault arc state to 0 and 1, respectively, and compare the random forest model, the support vector machine model, and the decision tree model. The tree model is trained, and the trained random forest model, support vector machine model and decision tree model are obtained;

Step S2, collecting the real-time current signal obtained by the photovoltaic array on the DC side of the photovoltaic system by using the current collecting device and according to the predetermined sampling time and the predetermined sampling interval;

Step S3: Analyze and process the real-time current signal collected in step S2 to obtain the time-domain and frequency-domain characteristics of the real-time current signal.

The time domain features include current average value a1 and current variance a2,

N is the number of samples, A _i is the i-th current sampled value,

Frequency domain features include wavelet coefficient variance a3, wavelet coefficient energy a4, and wavelet coefficient fluctuation degree a5 in a specific frequency band,

a4=d ²

d _i is the coefficient after wavelet decomposition of the i-th sampled current signal,

Is the average value of the coefficients after wavelet decomposition of N current signals, d is the coefficient after wavelet decomposition of the current signal in a specific frequency band, and d _max is the maximum value of the wavelet decomposition coefficient of the current signal in a specific frequency band;

Step S4, input the time domain features and frequency domain features obtained in step S3 into the random forest model, support vector machine model, and decision tree model trained in step S1, respectively, to obtain respective state label values;

Step S5: Set the sum of all status tag values obtained in step S4 as the total status tag value, and determine whether the total status tag value is greater than or equal to 2. When the determination result is no, go to step S6, when the determination result If yes, go to step S7;

Step S6, further determine whether the total status tag value is equal to 1, if the total status tag value is equal to 1, then repeat step S2 after reducing the predetermined sampling interval; otherwise, repeat step S2;

Step S7: Increment the determination times value of the determination times counter by 1, and further determine whether the incremented determination times value is equal to the predetermined determination times value. If the incremented determination times value is equal to the predetermined determination times value, go to step S8; otherwise, decrease Repeat step S2 after a small predetermined sampling interval;

In step S8, the circuit breaker operates to open the switch, and an alarm message is issued.

In the method for detecting the DC fault arc of a photovoltaic system based on machine learning provided by the present invention, it may also have the following characteristics: wherein, step S1 includes the following steps:

Step S1-1: Use the current acquisition device to collect multiple sets of current signals obtained by the photovoltaic array on the DC side of the photovoltaic system according to the predetermined sampling time and predetermined sampling interval. Each set of current signals includes a normal current signal in a normal working state and a Fault current signal in the state of fault arc;

Step S1-2, the normal current signal and the fault current signal in each group of current signals are analyzed and processed separately to obtain their respective time domain characteristics and frequency domain characteristics,

The time domain characteristics of the normal current signal include the current average value a11 and the current variance a21,

A _1i is the i-th normal current sampling value,

The time domain characteristics of the fault current signal include the current average value a12 and the current variance a22,

A _2i is the i-th fault current sampling value,

The frequency domain characteristics of the normal current signal include the wavelet coefficient variance a31, the wavelet coefficient energy a41, and the wavelet coefficient fluctuation degree a51 in a specific frequency band.

d _1i is the coefficient after wavelet decomposition of the i-th normal current signal,

Is the average value of the coefficients after wavelet decomposition of N normal current signals, d ₁ is the coefficient after wavelet decomposition of normal current signals _{in a specific frequency band, d 1max} is the maximum value of the wavelet decomposition coefficients of normal current signals in a specific frequency band,

The frequency domain characteristics of the fault current signal include the wavelet coefficient variance a32, the wavelet coefficient energy a42, and the wavelet coefficient fluctuation degree a52 in a specific frequency band.

d _2i is the coefficient after wavelet decomposition of the i-th fault current signal,

Is the average value of the coefficients after wavelet decomposition of N fault current signals, d ₂ is the coefficient after wavelet decomposition of the fault current signal _{in a specific frequency band, and d 2max} is the maximum value of the wavelet decomposition coefficient of the fault current signal in a specific frequency band;

Step S1-3, construct a random forest model, a support vector machine model, and a decision tree model respectively, and set the state label values of the normal working state and the faulty arc state to 0 and 1, respectively;

Step S1-4, using the time-domain and frequency-domain features of the normal current signal obtained in step S1-2, and the time-domain and frequency-domain features of the fault current information as the training set, respectively, to the random forest model, support vector machine model, and The decision tree model is trained to obtain the trained random forest model, support vector machine model, and decision tree model.

In the method for detecting DC arc faults of photovoltaic systems based on machine learning provided by the present invention, it may also have the following characteristics: wherein the maximum depth adopted by the random forest model is 2, and the number of sub-models is 10.

In the method for detecting DC arc faults of photovoltaic systems based on machine learning provided by the present invention, it may also have the following characteristics: wherein the kernel function adopted by the support vector machine model is a radial basis function, and the regularization weight is 10.

In the method for detecting DC arc faults of photovoltaic systems based on machine learning provided by the present invention, it may also have the following characteristics: wherein the regularization parameter adopted by the decision tree model is 11, and the regularization weight is 1.

In the DC fault arc detection method of photovoltaic system based on machine learning provided by the present invention, it may also have the following characteristics: wherein, the predetermined sampling time is 0.1s, the initial value of the predetermined sampling interval is 0.5s, and the predetermined sampling interval is reduced. The value is 0.1-0.3s.

In the method for detecting DC fault arc of photovoltaic system based on machine learning provided by the present invention, it may also have the characteristic that the specific frequency band is 40kHz-80kHz.

In the method for detecting DC fault arc of photovoltaic system based on machine learning provided by the present invention, it may also have the feature: wherein the value of the predetermined number of times of identification is 2 or 3.

In the method for detecting DC arc faults of photovoltaic systems based on machine learning provided by the present invention, it may also have the following characteristics: wherein, in step S3, the optimal wavelet base adopted by the wavelet decomposition is a db10 wavelet.

In the method for detecting DC fault arc of photovoltaic system based on machine learning provided by the present invention, it may also have the following characteristics: wherein the current collecting device is a series-in-coil induction type real-time current collecting device.

The role and effect of the invention

According to the machine learning-based DC fault arc detection method for photovoltaic systems according to the present invention, the random forest model, support vector machine model, and decision tree model are constructed separately, and the state label values of the normal working state and the fault arc state are set to 0 respectively. And 1, and train the random forest model, support vector machine model, and decision tree model respectively to obtain the trained random forest model, support vector machine model, and decision tree model; use the current collection device and follow the scheduled sampling time and scheduled sampling Collect the real-time current signal obtained by the photovoltaic array on the DC side of the photovoltaic system at intervals; analyze and process the collected real-time current signal to obtain the time-domain and frequency-domain characteristics of the real-time current signal; and obtain the time-domain and frequency-domain characteristics The features are respectively input into the trained random forest model, support vector machine model and decision tree model to obtain their respective state label values; the sum of all state label values obtained is set as the total state label value, and the total state label is judged If the value is greater than or equal to 2, when the judgment result is yes, the judgment times value of the judgment times counter is incremented by 1, and it is further judged whether the incremented judgment times value is equal to the predetermined judgment times value, if the incremented judgment times value is equal to the predetermined judgment The value of the number of times, the circuit breaker action causes the switch to open, and an alarm message is issued. Therefore, the detection method in the present invention adopts a detection algorithm based on machine learning, which can improve the accuracy of DC fault arc detection. Practical, it can also avoid the false detection operation caused by the threshold setting can not adapt to all situations, and effectively reduce the false detection rate.

Description of the drawings

Fig. 1 is a schematic diagram of a real-time current collection position for detecting a DC fault arc of a photovoltaic system in an embodiment of the present invention;

2 is an action flow chart of a method for detecting DC fault arcs of a photovoltaic system based on machine learning detection in an embodiment of the present invention; and

Fig. 3 is an action flow chart of constructing and training a random forest model, a support vector machine model, and a decision tree model in an embodiment of the present invention.

In the figure, 1 is a photovoltaic array, 2 is a combiner box, 3 is an inverter, 4 is an AC power grid, and 5 is a real-time current acquisition device.

detailed description

In the following, the concept, specific structure and technical effects of the present invention will be further described in conjunction with the accompanying drawings to fully understand the purpose, features and effects of the machine learning-based photovoltaic system DC fault arc detection method of the present invention.

<Example>

In this embodiment, the experimental data of a 10kW rooftop photovoltaic power station is taken as an example to detect the DC fault arc of the photovoltaic system.

Figure 1 is a schematic diagram of the real-time current collection location for detecting the DC fault arc of the photovoltaic system.

As shown in Figure 1, the photovoltaic system in this embodiment includes a photovoltaic array 1, a combiner box 2, an inverter 3, and an AC power grid 4 that are connected to each other in sequence. A switch S1 is provided between the photovoltaic array 1 and the combiner box 2, and A current collecting device 5 is provided between the circuit connected to the box 2 and the inverter 3. Photovoltaic array 1 outputs DC current, and multiple DC branches are connected in parallel in combiner box 2, and the total DC current is input into inverter 3. Inverter 3 converts DC power to AC power and transmits it to AC power grid 4. Inverter 3 controls to send out a detection signal. In this embodiment, the current collecting device 5 is a real-time current collecting device of a series-in-coil induction type.

Fig. 2 is an action flow chart of a method for detecting a DC fault arc of a photovoltaic system based on machine learning detection.

As shown in Figure 2, the photovoltaic system DC fault arc detection method based on machine learning detection in this embodiment is used to detect whether the photovoltaic system has a DC fault arc, including the following steps:

Step S1: Build a random forest model, a support vector machine model, and a decision tree model respectively, set the state label values of the normal working state and the fault arc state to 0 and 1, respectively, and compare the random forest model, the support vector machine model, and the decision tree model. The tree model is trained, and the trained random forest model, support vector machine model and decision tree model are obtained.

Step S2: Collect the real-time current signal obtained by the photovoltaic array on the DC side of the photovoltaic system by using the current collecting device and according to the predetermined sampling time and the predetermined sampling interval. In this embodiment, the predetermined sampling time is 0.1s, and the initial value of the predetermined sampling interval is 0.5s.

In step S3, the real-time current signal collected in step S2 is analyzed and processed to obtain the time-domain and frequency-domain characteristics of the real-time current signal.

The time domain features include current average value a1 and current variance a2,

Among them, N is the number of samples. In this embodiment, N is 10000; A _i is the i-th current sampled value.

Frequency domain features include wavelet coefficient variance a3, wavelet coefficient energy a4, and wavelet coefficient fluctuation degree a5 in a specific frequency band,

a4=d ²

Wherein, D _i is the i-th coefficients of the sampled current signal wavelet decomposition,

Is the average value of the coefficients after wavelet decomposition of N current signals, d is the coefficient after wavelet decomposition of the current signal in a specific frequency band, and d _max is the maximum value of the wavelet decomposition coefficient of the current signal in a specific frequency band. In this embodiment, the specific frequency band is 40kHz-80kHz; the optimal wavelet base used in wavelet decomposition is db10 wavelet.

Step S4, input the time domain features and frequency domain features obtained in step S3 into the random forest model, support vector machine model, and decision tree model trained in step S1, respectively, to obtain respective state label values.

Step S5: Set the sum of all the state tag values obtained in step S4 as the total state tag value, and determine whether the total state tag value is greater than or equal to 2. When the judgment result is no, go to step S6, when the judgment result If yes, go to step S7.

Step S6: It is further judged whether the total status tag value is equal to 1. If the total status tag value is equal to 1, step S2 is repeated after reducing the predetermined sampling interval; otherwise, step S2 is repeated. The reduced value of the predetermined sampling interval is 0.1-0.3s. In this embodiment, the reduced value of the predetermined sampling interval is 0.2s.

Step S7: Increment the determination times value of the determination times counter by 1, and further determine whether the incremented determination times value is equal to the predetermined determination times value; if the incremented determination times value is equal to the predetermined determination times value, go to step S8; otherwise, decrement Step S2 is repeated after the predetermined sampling interval is small. In this embodiment, the value of the number of times of user identification is 2 or 3; the initial value of the total status tag value is 0.

In step S8, the circuit breaker operates to open the switch S1, and an alarm message is issued.

Fig. 3 is an action flow chart of constructing and training a random forest model, a support vector machine model, and a decision tree model in an embodiment of the present invention.

As shown in Figure 3, in this embodiment, the above step S1 is implemented according to the following steps:

Step S1-1: Use the current acquisition device to collect multiple sets of current signals obtained by the photovoltaic array on the DC side of the photovoltaic system according to the predetermined sampling time and predetermined sampling interval. Each set of current signals includes a normal current signal in a normal working state and a The fault current signal in a fault arc state.

In step S1-2, the normal current signal and the fault current signal in each group of current signals are analyzed and processed separately to obtain respective time-domain characteristics and frequency-domain characteristics.

The time domain characteristics of the normal current signal include the current average value a11 and the current variance a21,

A _1i is the i-th normal current sampling value,

The time domain characteristics of the fault current signal include the current average value a12 and the current variance a22,

A _2i is the i-th fault current sampling value,

The frequency domain characteristics of the normal current signal include the wavelet coefficient variance a31, the wavelet coefficient energy a41, and the wavelet coefficient fluctuation degree a51 in a specific frequency band.

d _1i is the coefficient after wavelet decomposition of the i-th normal current signal,

Is the average value of the coefficients after wavelet decomposition of N normal current signals, d ₁ is the coefficient after wavelet decomposition of normal current signals _{in a specific frequency band, d 1max} is the maximum value of the wavelet decomposition coefficients of normal current signals in a specific frequency band,

The frequency domain characteristics of the fault current signal include the wavelet coefficient variance a32, the wavelet coefficient energy a42, and the wavelet coefficient fluctuation degree a52 in a specific frequency band.

d _2i is the coefficient after wavelet decomposition of the i-th fault current signal,

Is the average value of the coefficients after wavelet decomposition of N fault current signals, d ₂ is the coefficient after wavelet decomposition of the fault current signal _{in a specific frequency band, and d 2max} is the maximum value of the wavelet decomposition coefficient of the fault current signal in a specific frequency band.

Table 1 lists the calculation results of a group of fault current signals and a normal current signal.

Table 1 Example calculation results of fault current and normal current

To	Fault current	Normal current
Average current	4.0891	4.4271
Current variance	0.0024	0.0015
Wavelet coefficient variance	2.98E-04	2.78E-04
Wavelet coefficient energy	0.3768	0.3521
Wavelet coefficient fluctuation degree	0.0776	0.0543

It can be seen from Table 1 that when a DC fault arc occurs, the average current of the photovoltaic system will suddenly decrease, and the current variance, wavelet coefficient variance, wavelet coefficient energy, and wavelet coefficient fluctuation degree will suddenly increase, and the degree of sudden change in each feature Determined by the specific power level of the photovoltaic system.

In step S1-3, a random forest model, a support vector machine model, and a decision tree model are constructed respectively, and the state label values of the normal working state and the faulted arc state are set to 0 and 1, respectively.

Step S1-4, using the time-domain and frequency-domain features of the normal current signal obtained in step S1-2, and the time-domain and frequency-domain features of the fault current information as the training set, respectively, to the random forest model, support vector machine model, and The decision tree model is trained to obtain the trained random forest model, support vector machine model, and decision tree model.

In this embodiment, the maximum depth used by the random forest model is 2, and the number of sub-models is 10; the kernel function used by the support vector machine model is the radial basis function, and the regularization weight is 10; The regularization parameter is 11, and the regularization weight is 1.

The function and effect of the embodiment

According to the DC fault arc detection method of photovoltaic system based on machine learning involved in this embodiment, because the random forest model, support vector machine model, and decision tree model are constructed separately, the state label values of the normal working state and the fault arc state are set as 0 and 1, and train the random forest model, support vector machine model, and decision tree model respectively to obtain the trained random forest model, support vector machine model, and decision tree model; use the current collection device and follow the scheduled sampling time and schedule The real-time current signal obtained by the photovoltaic array on the DC side of the photovoltaic system is collected at sampling intervals; the real-time current signal obtained is analyzed and processed to obtain the time-domain and frequency-domain characteristics of the real-time current signal; the time-domain characteristics and frequency are obtained The domain features are respectively input into the trained random forest model, support vector machine model and decision tree model to obtain their respective state label values; set the sum of all state label values obtained as the total state label value, and judge the total state Whether the tag value is greater than or equal to 2, when the judgment result is yes, increment the judgment times value of the judgment times counter by 1, and further judge whether the incremented judgment times value is equal to the predetermined judgment times value, if the incremented judgment times value is equal to the predetermined Determine the value of the number of times, the circuit breaker action causes the switch to open, and an alarm message is issued. Therefore, the detection method in this embodiment adopts a detection algorithm based on machine learning, which can improve the accuracy of DC fault arc detection, low current and high current situations The following are practical, and it can also avoid the false detection operation caused by the threshold setting cannot adapt to all situations, and effectively reduce the false detection rate.

The foregoing embodiments are preferred cases of the present invention, and are not used to limit the protection scope of the present invention.

Claims (10)

1. A photovoltaic system DC fault arc detection method based on machine learning is characterized in that the detection method is implemented by a photovoltaic system, and the photovoltaic system includes a photovoltaic array, a combiner box, an inverter, and an AC power grid that are sequentially connected to each other. A current collecting device is provided between the combiner box and the circuit connected to the inverter, and a switch is provided between the photovoltaic array and the combiner box, and the detection method includes the following steps:

   Step S1: Build a random forest model, a support vector machine model, and a decision tree model respectively, set the state label values of the normal working state and the fault arc state to 0 and 1, respectively, and compare the random forest model and the support vector Machine model and the decision tree model are trained to obtain trained random forest model, support vector machine model and decision tree model;

   Step S2, collecting real-time current signals obtained by the photovoltaic array on the DC side of the photovoltaic system by using the current collecting device and according to a predetermined sampling time and a predetermined sampling interval;

   Step S3, analyzing and processing the real-time current signal collected in the step S2 to obtain the time-domain characteristics and frequency-domain characteristics of the real-time current signal,

   The time domain features include current average a1 and current variance a2,

   

   

   The N is the number of samples, and the _Ai is the i-th current sampled value,

   The frequency domain features include wavelet coefficient variance a3, wavelet coefficient energy a4, and wavelet coefficient fluctuation degree a5 in a specific frequency band,

   

   a4=d ²

   

   D _i is the i-th coefficients of the sampled current signal wavelet decomposition, the

   

   Is the average value of the coefficients after wavelet decomposition of N current signals, d is the coefficient after wavelet decomposition of the current signal in the specific frequency band, and the d _max is the maximum value of the wavelet decomposition coefficient of the current signal in the specific frequency band ；

   Step S4, input the time domain features and the frequency domain features obtained in step S3 into the random forest model, support vector machine model, and decision tree model trained in step S1, respectively, to obtain respective state label values;

   Step S5: Set the sum of all status tag values obtained in step S4 as the total status tag value, and determine whether the total status tag value is greater than or equal to 2. When the result of the determination is no, go to step S6, when When the judgment result is yes, go to step S7;

   Step S6: It is further judged whether the total status tag value is equal to 1, and if the total status tag value is equal to 1, step S2 is repeated after reducing the predetermined sampling interval; otherwise, step S2 is repeated;

   Step S7: Increment the determination times value of the determination times counter by 1, and further determine whether the incremented determination times value is equal to the predetermined determination times value, and if the incremented determination times value is equal to the predetermined determination times value, go to step S8 ; Otherwise, repeat step S2 after reducing the predetermined sampling interval;

   Step S8, the circuit breaker operates to turn off the switch, and an alarm message is issued.
2. The method for detecting DC fault arc of photovoltaic system based on machine learning according to claim 1, characterized in that:

   Wherein, the step S1 includes the following steps:

   Step S1-1, using the current collecting device and collecting multiple sets of current signals obtained by the photovoltaic array on the DC side of the photovoltaic system according to a predetermined sampling time and a predetermined sampling interval, each set of the current signals includes being in a normal working state The normal current signal and the fault current signal in a fault arc state;

   Step S1-2, analyzing and processing the normal current signal and the fault current signal in each group of the current signals respectively to obtain respective time domain characteristics and frequency domain characteristics,

   The time domain characteristics of the normal current signal include current average a11 and current variance a21,

   

   

   The A _1i is the i-th normal current sampling value,

   The time domain characteristics of the fault current signal include current average a12 and current variance a22,

   

   

   The A _2i is the i-th fault current sampling value,

   The frequency domain characteristics of the normal current signal include wavelet coefficient variance a31, wavelet coefficient energy a41, and wavelet coefficient fluctuation degree a51 in a specific frequency band,

   

   

   

   The d _1i is the coefficient after wavelet decomposition of the i-th normal current signal, and the

   

   Is the average value of the coefficients after wavelet decomposition of N normal current signals, d ₁ is the coefficient after wavelet decomposition of the normal current signal in the specific frequency band, and the d _1max is the wavelet decomposition coefficient of the normal current signal in the specific frequency band The maximum value of,

   The frequency domain characteristics of the fault current signal include wavelet coefficient variance a32, wavelet coefficient energy a42, and wavelet coefficient fluctuation degree a52 in a specific frequency band,

   

   

   

   The d _2i is the coefficient after wavelet decomposition of the i-th fault current signal, and the

   

   Is the average value of the coefficients after wavelet decomposition of N fault current signals, d ₂ is the coefficient after wavelet decomposition of the fault current signal in the specific frequency band, and the d _2max is the wavelet decomposition coefficient of the fault current signal in the specific frequency band The maximum value of

   Step S1-3: Build a random forest model, a support vector machine model, and a decision tree model respectively, and set the state label values of the normal working state and the faulty arc state to 0 and 1, respectively;

   Step S1-4, using the time-domain feature and frequency-domain feature of the normal current signal obtained in the step S1-2, and the time-domain feature and frequency-domain feature of the fault current information as a training set for the random The forest model, the support vector machine model, and the decision tree model are trained to obtain a trained random forest model, a support vector machine model, and a decision tree model.
3. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 2, characterized in that:

   Wherein, the maximum depth adopted by the random forest model is 2, and the number of sub-models is 10.
4. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 2, characterized in that:

   Wherein, the kernel function adopted by the support vector machine model is a radial basis function, and the regularization weight is 10.
5. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 2, characterized in that:

   Wherein, the regularization parameter adopted by the decision tree model is 11, and the regularization weight is 1.
6. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 1, characterized in that:

   Wherein, the predetermined sampling time is 0.1s,

   The initial value of the predetermined sampling interval is 0.5s,

   The reduced value of the predetermined sampling interval is 0.1-0.3s.
7. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 1, characterized in that:

   Wherein, the specific frequency band is 40kHz-80kHz.
8. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 1, characterized in that:

   Wherein, the value of the predetermined number of times of identification is 2 or 3.
9. The DC fault arc detection method of photovoltaic system based on machine learning according to claim 1, characterized in that:

   Wherein, in the step S3, the optimal wavelet base used in the wavelet decomposition is a db10 wavelet.
10. The method for detecting DC fault arc of photovoltaic system based on machine learning according to claim 1, characterized in that:

    Wherein, the current collecting device is a real-time current collecting device of inductive coil in series.

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