CN113094983A - Online simulation method for multi-dimensional time-varying characteristics of direct-current fault electric arc of photovoltaic system - Google Patents
Online simulation method for multi-dimensional time-varying characteristics of direct-current fault electric arc of photovoltaic system Download PDFInfo
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Abstract
The invention discloses an online simulation method of multi-dimensional time-varying characteristics of a photovoltaic system direct-current fault arc, which comprises the steps of simulating the time domain characteristics of the direct-current fault arc through a U-I model, simulating the frequency domain characteristics of the direct-current fault arc through a pink noise model, constructing a filter to correct the pink noise model, and superposing the two models to obtain a static photovoltaic system direct-current fault arc model for synchronously simulating the characteristics of the multiple fault arcs; time factor variables are introduced into the U-I model and the pink noise model, and then dynamic association models which influence the key parameters of the characteristics of the fault arc and the time-frequency characteristics of the fault arc, such as arc generation gaps, loop currents and the like in the combustion evolution process of different arcs are respectively established. The method establishes a dynamic and static photovoltaic system direct-current fault arc mathematical model with a multidimensional time-varying fault arc characteristic evolution overall-process expression function, and realizes effective simulation of fault arc characteristic changes under different application scenes and system structure conditions.
Description
Technical Field
The invention belongs to the technical field of photovoltaic electrical fault detection, and particularly relates to an online simulation method for multi-dimensional time-varying characteristics of a photovoltaic system direct-current fault arc.
Background
With the large number of photovoltaic, energy storage and electric vehicles connected to the power distribution network, the number of power electronic devices is greatly increased, and the direct current of the power distribution and utilization system becomes an increasingly important development direction. Compared with an alternating current system, the direct current comprehensive energy system can conveniently realize the efficient access and flexible management of renewable energy, direct current and variable frequency loads and provide efficient and safe power utilization service.
The inherent composition of a photovoltaic system makes it prone to problems such as loose wiring, aging of components, and insulation damage, i.e., fault arcing may occur at multiple locations in the photovoltaic system, resulting in higher frequency of photovoltaic arcing faults than other types of dc arcing faults. The normal operation of the photovoltaic system and the power system is endangered by the power utilization safety problem caused by fault electric arcs in the long-term operation process of the photovoltaic system. And as the voltage grade of the photovoltaic system is continuously improved and the wiring form is increasingly complex, the occurrence probability of fault arcs is increased. Therefore, the method has very important significance for timely detecting fault arc and extinguishing arc. However, compared with widely-developed ac fault arc research, there are many problems in dc fault arc related research, for example, a dynamic photovoltaic system dc fault arc model that can realize a function of expressing the overall process of multidimensional fault arc characteristic evolution is lacking, development of a fault arc detection algorithm is limited, and further research on fault arc characteristic change mechanisms under different application scenarios and system structure conditions and development of a high-performance photovoltaic system dc fault arc detection device are not facilitated.
The commonly used generation methods of pink noise mainly include: paul Kellet's weighted sum filter method, Robert Bristow-Johnson's zero-pole filter method, Voss algorithm. The Paul Kellet weighting and filtering method is the best method in performance at present, but the calculation amount is large, and the calculation process is very complex. The pole-zero filter method of Robert Bristow-Johnson has the advantages of large calculation amount, complex calculation process and poor fitting effect. Although the Voss algorithm is small in calculation amount and simple in process, the implementation performance is extremely poor, and the Voss algorithm has a great deviation from ideal pink noise. Meanwhile, pink noise, when used for simulating the spectrum distribution of the fault arc in a certain period, cannot clearly depict the fluctuation of the spectrum along with the change of time.
Disclosure of Invention
The invention aims to provide an online simulation method for the multi-dimensional time-varying characteristics of a direct-current fault arc of a photovoltaic system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the fault arc simulation method comprises the following steps:
1) establishing a photovoltaic system simulation model, wherein the simulation model comprises a controlled current source module positioned at the output end of a photovoltaic array of a corresponding photovoltaic system, and the controlled current source module is selected from any one of the following direct current fault arc models:
a first direct-current fault arc model: simulating the static time domain characteristic of the direct-current fault arc by adopting a U-I model;
and D, direct-current fault arc model II: a pink noise model is adopted to simulate the static frequency domain characteristics of the direct-current fault arc;
a third direct-current fault arc model: synchronously and statically simulating the multi-dimensional fault arc characteristics of the direct-current fault arc of the photovoltaic system by superposing the first model and the second model;
d, a direct-current fault arc model IV: introducing time factor variables into each superposition part (model I and model II) of the model III, and synchronously and dynamically simulating the multi-dimensional fault arc time-varying characteristic of the direct-current fault arc of the photovoltaic system;
2) performing parameter fitting on the U-I model by using direct-current fault arc experimental data under different application scenes and/or system structure conditions, and performing parameter setting on a pink noise model;
3) and operating the simulation model, and carrying out the whole process simulation of the characteristic evolution of the fault arc.
Preferably, the U-I model is represented as:
where A, B, C, D is the arc constant (which can be obtained by fitting) depending on experimental conditions, L is the arc gap (in mm), VarcIs the fault arc voltage (in V), IarcIs the fault arc current (in a).
Preferably, the A, B, C, D are respectively 29.4-36.25, 0.19-0.25, 11.77-13.811 and 0.019-0.030.
Preferably, the A, B, C, D is obtained by fitting the fault arc current and the fault arc voltage data measured experimentally at a certain arcing gap.
Preferably, the construction method of the pink noise model comprises the following steps: white noise is converted to pink noise by a digital filter.
Preferably, the transfer function of the digital filter is expressed as:
the 7-order transfer function adopted by the invention has simple calculation process and higher fitting degree with ideal pink noise.
Preferably, the construction method of the pink noise model further comprises the following steps: and analyzing the frequency spectrum characteristics of the direct-current fault arc current in the experimental data, and correcting pink noise according to the analysis result.
Preferably, in the pink noise model, the pink noise is corrected by using a low-pass filter, a high-pass filter or a low-pass filter and a high-pass filter which are cascaded, so that the corrected pink noise forms a fault arc noise distribution form which is matched with the frequency spectrum characteristic of the direct-current fault arc current.
Preferably, the low-pass filter and the high-pass filter are selected from butterworth filters.
Preferably, the method for introducing the time factor variable into the U-I model comprises the following steps: the arcing gap L is constructed as a function of time t, which is expressed as:
wherein v is the separation velocity (in mm/s) and t0Time required for separation to maximum arcing gap, LmaxFor maximum arc generation clearance, the function is used as a correlation model of a model I (U-I model) and a time factor variable, and parameters (v, L) can be carried out under different application scenes and/or system structure conditionsmax) And (6) setting.
Preferably, the method for introducing the time factor variable into the pink noise model comprises the following steps: respectively establishing random forest models for the frequency spectrum characteristic value output of the direct current fault arc frequency spectrum which changes along with time on N frequency bands, and training the random forest models established on the frequency bands to jointly form a whole frequency band distribution model of a fault arc current frequency spectrum prediction result under a certain time window. The time factor variable introduced into the pink noise model means that the trend of the full-band frequency spectrum characteristic amplitude changing along with the time is determined by a machine learning method, so that the frequency spectrum amplitude determined by the pink noise can accurately change along with the time, the fault arc time-frequency information is well reflected, and the clearer and more precise fault arc characteristics are obtained.
Preferably, the training of the random forest model comprises the following steps: refining (for example, averagely dividing) 0-100kHz into N small frequency bands, taking a current value and an arc generating gap in each time window as sample characteristic quantities (wherein the current value is a system loop current value, and the arc generating gap can influence the output of a subsequent machine learning frequency spectrum, so that the subsequent machine learning frequency spectrum is taken as a necessary characteristic quantity in the machine learning process), taking a frequency spectrum analysis result of the current in the time window as a sample label, constructing a training set and a test set aiming at a single small frequency band, and adjusting random forest model parameters in the training process to enable the average prediction accuracy of the training model on the full frequency band to be not lower than 80%. The random forest model is used as a correlation model of a model II (pink noise model) and a time factor variable, and parameter setting can also be performed under different application scenes and/or system structure conditions.
Preferably, N is 8-12 (for example, 10), so that the characterization of the fault arc frequency spectrum is more precise and accurate; the corresponding time window is 1-4 ms (e.g., 3 ms).
The invention has the beneficial effects that:
1) the method can effectively simulate the time domain characteristics of the fault arc: the arc voltage and the arc current vary with time. The established fault arc model is introduced into a photovoltaic system for simulation, so that the influence of key parameters such as arc generation gaps, loop current and the like on the electrical parameters of the fault arc in the combustion evolution process of different arcs can be reflected, and a standardized simulation model of the time domain characteristic of the fault arc is established to reveal the state change rule of the key electrical parameters such as the voltage, the current and the like of the fault arc in a direct current system.
2) The method can effectively simulate the frequency domain characteristics of the fault arc: spectral signature of the fault arc. The pink noise model is built, the built pink noise model is corrected by utilizing the low-pass filter and the high-pass filter, the frequency domain model of the fault arc is obtained, the frequency spectrum characteristics of the fault arc under different working conditions can be correctly simulated, and therefore the standardized simulation model of the frequency domain characteristics of the fault arc is built and used for revealing the change rule of the frequency domain characteristics of the fault arc in the direct current system.
3) The method has wide applicability, a dynamic photovoltaic system direct-current fault arc model for realizing the whole process of multidimensional fault arc characteristic evolution is constructed by introducing a time factor variable into a U-I model and introducing the time factor variable into a pink noise model based on a trained random forest model, the time-varying characteristics of fault arcs under the influence of different factors can be correctly simulated, furthermore, an on-line simulation method of the fault arc is provided, the effective simulation of the fault arc under different working conditions is realized by changing the constructed model parameters, so that the on-line simulation of the whole process of the evolution of the direct current fault arc in the photovoltaic system is realized, therefore, by revealing the change mechanism of the fault arc characteristics under different application scenes and system structure conditions, a simulation technical means for effective sensitivity verification is provided for the development of the direct-current fault arc detection device of the high-performance photovoltaic system.
Drawings
FIG. 1 is a structural block diagram of a photovoltaic system direct-current fault arc multi-dimensional time-varying characteristic online simulation system constructed in an embodiment; wherein, Discrete represents Discrete time simulation (simulation time of one step is 1e-6s, namely simulation frequency is 1 MHz).
Fig. 2 is a block diagram of a fault arc model in the on-line simulation system shown in fig. 1.
Fig. 3 is a graph comparing simulation and experimental results of dc fault voltage and current of a photovoltaic system.
Fig. 4 is a spectral analysis diagram of the constructed pink noise model.
FIG. 5a is a graph comparing simulation and experimental results of DC fault arc noise in a photovoltaic system (ohm-Nik inverter: Omniksol-20k-TL 2).
FIG. 5b is a graph comparing simulation and experimental results of DC fault arc noise of a photovoltaic system (solid topology inverter: GW 8000-DT).
FIG. 6a is a graph of simulated versus actual frequency spectrum of a fault arc current waveform over time in the 10-20kHz band.
FIG. 6b is a graph showing the result of the prediction accuracy of the simulated spectrum and the actual spectrum of the fault arc current waveform in different frequency bands of 0-100 kHz.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. The examples are given solely for the purpose of illustration and are not intended to limit the scope of the invention.
According to the invention, multiple direct-current fault arc models are fused, multiple direct-current fault arc characteristics in the photovoltaic system are accurately simulated, the on-line simulation of the whole process of the evolution of the direct-current fault arc in the photovoltaic system is realized, and an effective sensitivity verification simulation technical means is provided for the development of a high-performance direct-current fault arc detection device for the photovoltaic system. Therefore, the problem of deep research on the change mechanism of the characteristics of the fault arc under different application scenes and system structure conditions is solved.
Referring to fig. 1, the on-line simulation system for the multi-dimensional time-varying characteristic of the direct-current fault arc of the photovoltaic system integrally comprises a photovoltaic array, a fault arc model, an MPPT controller, a BOOST converter, an inverter, a transformer and an alternating-current power grid. Wherein, the output of the photovoltaic array can be adjusted by adjusting the temperature and the illumination intensity.
Examples of online simulations: discrete time simulation is set in matlab, and the simulation time of one step is 1e-6s, namely the simulation frequency is 1 MHz. The photovoltaic array is a current source, generates main loop current, and can control the magnitude of the current output by the photovoltaic array according to temperature and illumination intensity. The MPPT controller operates the photovoltaic array at a maximum power point for maximum efficiency. The BOOST converter BOOSTs the voltage output by the photovoltaic array, and then inverts direct current into alternating current through the inverter and then the alternating current is merged into an alternating current power grid. The fault arc model is connected in series into a line bus, and the change of the Current in the loop is Controlled in a Controlled Current Source mode.
The fault arc model comprises: constructing a U-I model to simulate the static time domain characteristic of the direct-current fault arc; more than one pink noise model corrected by a filter is constructed to simulate the static frequency domain characteristics of the direct-current fault arc; introducing a time factor variable into the U-I model, introducing the time factor variable into the pink noise model based on the trained random forest model, and simulating the multi-dimensional time-varying characteristic of the direct-current fault arc of the photovoltaic system; therefore, the online simulation of the whole process of the evolution of the direct-current fault arc in the photovoltaic system is realized.
Referring to fig. 2, the construction of the fault arc model for the online simulation of the multidimensional time-varying characteristics of the dc fault arc of the photovoltaic system specifically includes the following steps:
1) and building a U-I model, fitting experimental data such as arc current and arc voltage measured by experiments to obtain parameters of the U-I model, and simulating the static time domain characteristics of the direct-current fault arc (for example, the online simulation of the static time domain characteristics of the direct-current fault arc can be realized).
The U-I model is as follows:
wherein A, B, C, D is the arc constant, L is the arc gap (in mm), V, depending on experimental conditionsarcIs the fault arc voltage (in V), IarcIs the fault arc current (in a).
The constant A, B, C, D can be fitted by utilizing fault arc current and fault arc voltage data measured by an arc generating gap under the condition of 0.8-6.4 mm, the fitting effect evaluation index Adjusted R-square is at least more than 0.95, the fitting parameters in the model are considered to be effective, and A, B, C, D is 32.83, 0.22, 12.79 and 0.024 respectively.
Examples of the experiments are: the arc generating gap of the fault arc generating device is set to be 3.2mm, and the speed is set to be 2 mm/s. The arc current is measured with a current sensor and the arc voltage is measured with a voltage sensor. When the system normally works, the fault arc generating device is started, a corresponding 0-3.2 mm fault arc is generated in an experiment loop, and the arc voltage and the arc current at the moment are recorded for data fitting. By adjusting the Arc generating gap and the current of the system in normal operation, data of different Arc currents, Arc generating gaps and Arc voltages can be obtained (reference: Time-Frequency Distribution characteristics and Model Simulation of Photovoltaic Series Arc Fault With Power Electronic Equipment).
Referring to fig. 3, at 0-2.4s, the photovoltaic system is operating normally and no fault arc occurs; at 2.4 seconds, the fault arc is ignited, the arcing gap L is linearly increased from 0 to 3.2mm at the speed of 2mm/s to form an arcing gap of 3.2mm, the arcing gap is kept at 3.2mm after 4s, and the arc combustion is stable. When a fault arc occurs, the current sharply decreases and the voltage sharply increases. With the increase of the arc generating gap, the arc current is gradually reduced, and the arc voltage is steadily increased. When the arcing gap reaches a set maximum, the arc voltage and current remain relatively stable. And comparing the simulation result with the fault arc current and voltage of the test result, wherein the result shows that the variation of the simulated fault arc voltage and current is consistent with the test result.
2) Constructing a pink noise model for simulating the static frequency domain characteristics of the direct-current fault arc; the construction method of the pink noise model is specifically as follows.
Designing a digital filter with a transfer function of:
white noise is converted into pink noise through a designed digital filter.
Referring to fig. 4, the pink noise is distributed at 3dB down per octave, and it can be seen that the constructed pink noise spectrum analysis graph conforms to this variation characteristic.
And constructing a filter, adjusting filter parameters according to a frequency spectrum result (experimental frequency spectrum) obtained by analyzing the DC fault arc current obtained by the experiment, and correcting the pink noise. Wherein, the filter that constructs is: a low-pass filter is connected with a high-pass filter in cascade; the low-frequency characteristic of pink noise is corrected by a low-pass filter, and the high-frequency characteristic of pink noise is corrected by a high-pass filter.
The high-pass filter and the low-pass filter are Butterworth filters, and filter parameters are adjusted according to a fault arc current spectrum analysis result obtained through experiments.
Referring to fig. 5a and 5b, the pink noise spectrum shown in fig. 4 is corrected by using the constructed low-pass filter and high-pass filter, and after the correction, the pink noise is corrected by the low-pass filter and the high-pass filter through the spectrum analysis of the fault arc waveform occurring under different inverter conditions, so that the simulation spectrum and the experimental spectrum of the fault arc have high goodness of fit.
In addition, if the fault arc current spectrum analysis result under the action condition of the photovoltaic system is a bimodal spectrum form, the corrected pink noise model is cascaded after the peak frequency and the amplitude are adjusted (by adjusting the parameters of the high-low filter), and then the fault arc noise distribution form is simulated more accurately. For example, two high-low pass filter modified pink noises are cascaded, wherein the first set of high-pass filter parameters: 1-order, cut-off frequency 1-20 kHz (for example, 8 kHz); first set of low pass filter parameters: 4-order, cut-off frequency 60-120 kHz (for example, 105 kHz); second set of high pass filter parameters: 9-order cut-off frequency of 100-140 kHz (for example, 120 kHz); second set of low pass filter parameters: 5-order, cut-off frequency 100-140 kHz (120 kHz).
3) And introducing a time factor variable into the U-I model, introducing the time factor variable into the pink noise model based on the trained random forest model, and then overlapping the two models with the introduced time factor variable to form a dynamic mathematical model for simulating the multiple characteristics of the direct-current fault arc of the photovoltaic system, so that the overall process expression function of the characteristic evolution of the multi-dimensional fault arc is realized.
The method for introducing the time factor variable into the U-I model comprises the following steps: the arcing gap L is constructed as a function of time t, according to the UL169 1699B standard established by the Underwriters Laboratories (UL), the function of time being selected as:
wherein v is the separation velocity (in mm/s) and t0Time required for separation to maximum arcing gap, LmaxIs the maximum arcing gap.
The method for introducing the time factor variable into the pink noise model based on the trained random forest model comprises the following steps:
s1) refining 0-100kHz into N (for example, N is 10) small frequency bands, so that the fault arc frequency spectrum can be characterized more accurately, and the fault arc frequency spectrum can be simulated more finely, corresponding to a time window of 3 ms.
S2) taking each time window as a sample, taking the current value and the arc generation gap in each time window as sample characteristic quantities, and taking the frequency spectrum analysis result of the current in the time window as a sample label to construct a database sample. Selecting 50% of samples as a training set, inputting the characteristics and labels of the training set into a model for training, using the remaining 50% of data as a test set, obtaining the prediction accuracy of the trained model according to the comparison effect of the prediction result and the actual result of the test set, and adjusting the parameters of the random forest model to ensure that the average prediction accuracy of the training model on the full frequency band is not lower than 80%.
S3) respectively establishing random forest models for the frequency spectrum characteristic value output varying with time on each small frequency band according to the step S2, and finally integrating the N small frequency band models to form a full-frequency-band distribution integral model corresponding to the 3ms fault arc current frequency spectrum prediction result, namely forming an integral prediction model group.
Referring to fig. 6a and 6b, by inputting current simulation data in a 3ms time window in real time, a spectrum prediction result of the time window can be output. The current frequency spectrum output by the random forest model based on the establishment method has the same change trend with the actual frequency spectrum in different frequency bands, and the numerical accuracy is higher and the frequency spectrum output is more accurate.
In a word, the invention relates to an online simulation method of a photovoltaic system direct-current fault arc multi-dimensional time-varying characteristic. The time domain characteristic of the direct-current fault arc is simulated by constructing a U-I model, the frequency domain characteristic of the direct-current fault arc is simulated by constructing a pink noise model corrected by a filter, a time factor variable is further introduced into the U-I model, and the time factor variable is introduced into the pink noise model based on a trained random forest model, so that the on-line simulation of the multi-dimensional time-varying characteristic of the direct-current fault arc of the photovoltaic system is realized. By changing the constructed model parameters, the fault arc electrical signals can be output on line, effective expression of the whole process of fault arc multi-dimensional characteristic evolution under different working conditions is realized, and qualitative influence trends of different sources, load types and system working voltage levels of the low-voltage direct-current power system on the fault arc electrical characteristics are revealed. According to the invention, through the online simulation of the whole process of the evolution of the direct-current fault arc in the photovoltaic system, the deep research is facilitated, and the change mechanism of the fault arc characteristic under different application scenes and system structure conditions is revealed, so that a reference basis is provided for the development of a fault arc detection algorithm and the development of a high-performance photovoltaic system direct-current fault arc detection device.
Claims (10)
1. An online simulation method for multi-dimensional time-varying characteristics of a photovoltaic system direct-current fault arc is characterized by comprising the following steps: the method comprises the following steps:
1) establishing a photovoltaic system simulation model, wherein the simulation model comprises a controlled current source module positioned in a corresponding photovoltaic system, and the controlled current source module is selected from any one of the following direct current fault arc models:
a first direct-current fault arc model: simulating the static time domain characteristic of the direct-current fault arc by adopting a U-I model;
and D, direct-current fault arc model II: a pink noise model is adopted to simulate the static frequency domain characteristics of the direct-current fault arc;
a third direct-current fault arc model: synchronously and statically simulating the multi-dimensional fault arc characteristics of the direct-current fault arc of the photovoltaic system by superposing the first model and the second model;
d, a direct-current fault arc model IV: introducing time factor variables into each superposition part of the model III, and synchronously and dynamically simulating the multi-dimensional fault arc time-varying characteristic of the direct-current fault arc of the photovoltaic system;
2) performing parameter fitting on the U-I model by using direct-current fault arc experimental data under different application scenes and/or system structure conditions;
3) and operating the simulation model, and carrying out the whole process simulation of the characteristic evolution of the fault arc.
2. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 1, wherein: the U-I model is represented as:
wherein A, B, C, D is the arc constant, L is the arc gap, VarcTo fault arc voltage, IarcIs a fault arc current.
3. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 2, wherein: the A, B, C, D are respectively 29.4-36.25, 0.19-0.25, 11.77-13.811 and 0.019-0.030.
4. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 1, wherein: the construction method of the pink noise model comprises the following steps: white noise is converted to pink noise by a digital filter.
5. The method for the on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 4, wherein the method comprises the following steps: the transfer function of the digital filter is expressed as:
6. the method for the on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 4, wherein the method comprises the following steps: the construction method of the pink noise model further comprises the following steps: and analyzing the frequency spectrum characteristics of the direct-current fault arc current in the experimental data, and correcting pink noise according to the analysis result.
7. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 6, wherein: in the pink noise model, a low-pass filter and/or a high-pass filter are/is adopted for correction, so that the corrected pink noise forms a fault arc noise distribution form which is matched with the frequency spectrum characteristic of the direct-current fault arc current.
8. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 1, wherein: the method for introducing the time factor variable into the U-I model comprises the following steps: the arcing gap L is constructed as a function of time t, which is expressed as:
wherein v is the separation velocity, t0Time required for separation to maximum arcing gap, LmaxIs the maximum arcing gap.
9. The method for on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 1, wherein: the method for introducing the time factor variable into the pink noise model comprises the following steps: respectively establishing random forest models for the frequency spectrum characteristic value output of the direct current fault arc frequency spectrum which changes along with time on N frequency bands, and training the random forest models established on the frequency bands to jointly form a whole frequency band distribution model of a fault arc current frequency spectrum prediction result under a certain time window.
10. The method for the on-line simulation of the multi-dimensional time-varying characteristics of the direct-current fault arc of the photovoltaic system according to claim 9, wherein: the training of the random forest model comprises the following steps: the method comprises the steps of refining 0-100kHz into N small frequency bands, using a current value and an arc generation gap in each time window as sample characteristic quantities, using a frequency spectrum analysis result of the current in the time window as a sample label, constructing a training set and a test set aiming at a single small frequency band, and adjusting parameters of a random forest model in the training process to enable the average prediction accuracy of the training model on the full frequency band to be not lower than 80%.
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