A method, apparatus, and medium for photovoltaic dc arc fault detection

Abstract

The application relates to the field of photovoltaic power generation and discloses a photovoltaic direct-current arc fault detection method, a photovoltaic direct-current arc fault detection device and a medium, which comprises the following steps: collecting a line current signal of a photovoltaic power generation system to be detected, performing time domain processing on the signal to obtain a time domain signal, meanwhile, performing calculation on the time domain signal to obtain a signal effective value. In addition, performing frequency domain processing on the time domain signal to obtain a frequency domain signal, and modifying a frequency domain comparison threshold value according to the signal effective value. Finally, determining whether an arc fault occurs according to the obtained signal effective value, frequency domain signal and frequency domain comparison threshold value. Thus, the frequency domain comparison threshold value is modified in real time through the collected time domain signal, and the occurrence of a refusal action and a false action is avoided. In addition, the direct-current arc fault is detected in combination with the signal effective value, the frequency domain signal and the frequency domain comparison threshold value, low reliability caused by comparison between the frequency domain signal and the frequency domain threshold value is avoided, and the accuracy of direct-current arc fault detection is improved.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation, and in particular to a method, apparatus and medium for detecting photovoltaic DC arc faults. Background Technology

[0002] With the continuous development of photovoltaic power generation, DC power supply systems have been widely used and received attention. Factors such as aging lines, animal bites, poor contact of contactors, and wear can easily cause photovoltaic DC arcs. Photovoltaic DC arcs do not have a zero-crossing point, are difficult to extinguish, and can easily expand the scope of the fault. If no measures are taken, they can easily cause fires. Therefore, the detection of photovoltaic DC arc faults is crucial to the safety and reliability of photovoltaic power generation systems.

[0003] Currently, the frequency domain threshold method is commonly used to detect DC arc faults. After a DC arc fault occurs, the high-frequency voltage and current generated by the fault are converted to the frequency domain and compared with a preset threshold to determine the occurrence of the arc fault. The selection of the preset threshold is crucial in arc fault diagnosis. If the preset threshold is too high, it may cause the protection equipment to fail to operate, meaning that even a minor arc fault on the DC side of the photovoltaic power generation system can escalate into a more serious problem, potentially even causing a fire. If the preset threshold is too low, it may cause the protection device to malfunction, leading to the shutdown of some or all of the photovoltaic power generation equipment. This necessitates alerting maintenance personnel to perform system repairs, which not only increases the difficulty of maintenance work but also takes time for the photovoltaic power generation system to stop generating electricity until the fault is resolved, resulting in reduced economic benefits or even losses for the photovoltaic power station.

[0004] In the frequency domain thresholding method, the Fast Fourier Transform (FFT) is a commonly used method for spectral analysis in the frequency domain. The FFT analysis results of the arc signal in the frequency domain are used to determine whether to change the preset threshold. However, photovoltaic power generation systems are affected by various operating conditions, such as adjustments by the Maximum Power Point Tracking (MPPT) solar controller, inverter startup, changes in irradiance, and inverter operation, causing abrupt changes in frequency domain characteristics. Therefore, determining whether to change the preset threshold based solely on the FFT analysis results of the arc signal may still lead to malfunctions and failures to operate, and the reliability of arc fault diagnosis based solely on the comparison between the frequency domain signal and the frequency domain threshold is low.

[0005] Therefore, how to avoid failure or false alarm during DC arc fault detection and improve the accuracy of photovoltaic DC arc fault detection is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, and medium for detecting photovoltaic DC arc faults. It corrects the frequency domain comparison threshold based on the time-domain signal corresponding to the current signal, avoiding abrupt changes in frequency domain characteristic quantities due to operating conditions such as MPPT adjustments when determining whether to change the preset threshold based on the FFT analysis results of the frequency domain arc signal. This avoids situations where the frequency domain characteristic quantities may change abruptly due to operating conditions such as MPPT adjustment, leading to failure to operate or false operation. Furthermore, it combines the effective value of the signal obtained from the time-domain signal, the frequency domain signal, and the frequency domain comparison threshold to determine whether a DC arc fault has occurred, avoiding the inefficiency of relying solely on the frequency domain signal to determine whether a DC arc fault has occurred.

[0007] To address the aforementioned technical problems, this application provides a method for detecting photovoltaic DC arc faults, comprising:

[0008] Collect the line current signal of the photovoltaic power generation system under test;

[0009] The line current signal is processed in the time domain to obtain the corresponding time domain signal, and the corresponding signal effective value is calculated from the time domain signal.

[0010] The time-domain signal is processed in the frequency domain to obtain the corresponding frequency-domain signal;

[0011] The frequency domain comparison threshold of the frequency domain signal is corrected based on the effective value of the signal;

[0012] Based on the effective value of the signal, the frequency domain signal and the corrected frequency domain comparison threshold are used to determine whether the photovoltaic power generation system under test has experienced a DC arc fault.

[0013] Preferably, the step of correcting the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal includes:

[0014] The second-order difference of the effective value is obtained by calculating the effective values ​​of adjacent signals;

[0015] Determine whether the second difference of the effective value is less than a preset value;

[0016] If it is less than the preset value, then the frequency domain comparison threshold is increased, and it is determined whether the current frequency domain comparison threshold is greater than the preset threshold. If it is greater, then the step of decreasing the frequency domain comparison threshold is entered.

[0017] If the signal is not less than the preset value, then determine whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is greater than the frequency domain comparison threshold, then decrease the frequency domain comparison threshold.

[0018] Preferably, determining whether a DC arc fault has occurred in the photovoltaic power generation system under test based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold includes:

[0019] The initial frequency domain comparison threshold is determined based on the effective value of the signal;

[0020] The second-order difference of the effective values ​​is calculated based on the effective values ​​of the signals in adjacent sampling windows to determine the time-domain fraction; wherein, sampling one of the time-domain signals constitutes one sampling window;

[0021] The frequency domain score is determined based on the frequency domain signal and the frequency domain comparison threshold.

[0022] Determine whether the current time domain score and frequency domain score corresponding to the first preset number of consecutive sampling windows meet preset conditions;

[0023] If the preset conditions are met, it is determined that the photovoltaic power generation system under test has a DC arc fault; wherein, the preset conditions are that the time domain score is greater than the time domain comparison threshold and the frequency domain score is greater than the frequency domain comparison threshold.

[0024] Preferably, after determining whether a DC arc fault has occurred in the photovoltaic power generation system to be detected based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold, the method further includes:

[0025] Determine whether the second-order difference of the effective value corresponding to the second consecutive preset number of sampling windows is in an increasing state. If so, increase the current frequency domain comparison threshold.

[0026] Preferably, determining the frequency domain score based on the frequency domain signal and the frequency domain comparison threshold includes:

[0027] Determine whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is, increase the frequency domain score; otherwise, decrease the frequency domain score.

[0028] Preferably, time-domain processing of the line current signal includes:

[0029] The current amplitude range corresponding to the line current signal is detected so that the magnitude of the line current signal can be adjusted according to a preset ratio;

[0030] The average value of the line current signal is calculated to eliminate the DC bias of the current.

[0031] Preferably, frequency domain processing of the time-domain signal includes:

[0032] The time-domain signal is subjected to a Hanning window FFT operation to obtain the corresponding FFT signal;

[0033] The magnitude of the FFT signal is restored according to the preset ratio.

[0034] To address the aforementioned technical problems, this application also provides a photovoltaic DC arc fault detection device, comprising:

[0035] The acquisition module is used to acquire the line current signal of the photovoltaic power generation system under test;

[0036] The first processing module is used to perform time-domain processing on the line current signal to obtain a corresponding time-domain signal, and to calculate the corresponding signal effective value from the time-domain signal.

[0037] The second processing module is used to perform frequency domain processing on the time domain signal to obtain the corresponding frequency domain signal;

[0038] The correction module is used to correct the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal;

[0039] The determination module is used to determine whether the photovoltaic power generation system under test has experienced a DC arc fault based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold.

[0040] To address the aforementioned technical problems, this application also provides a photovoltaic DC arc fault detection device, including a memory for storing a computer program;

[0041] A processor is configured to execute the computer program to implement the steps of the photovoltaic DC arc fault detection method as described above.

[0042] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the photovoltaic DC arc fault detection method as described above.

[0043] The present invention provides a method for detecting DC arc faults in photovoltaic systems, comprising: acquiring line current signals of the photovoltaic power generation system to be tested, performing time-domain processing on the acquired line current signals to obtain corresponding time-domain signals, and simultaneously calculating the corresponding effective value of the time-domain signals. After obtaining the time-domain signals, performing frequency-domain processing on the time-domain signals to obtain corresponding frequency-domain signals, and correcting the frequency-domain comparison threshold of the frequency-domain signals based on the effective value of the signals. Finally, determining whether a DC arc fault has occurred in the photovoltaic power generation system to be tested based on the obtained effective value of the signals, the frequency-domain signals, and the corrected frequency-domain comparison threshold. Therefore, the technical solution provided in this application corrects the frequency-domain comparison threshold in real time by acquiring the time-domain signals, avoiding the occurrence of sudden changes in frequency-domain characteristic quantities due to the influence of operating conditions such as MPPT adjustment, inverter startup, and changes in light intensity when determining whether to change the preset threshold based on the FFT analysis results of the arc signals in the frequency domain, which could lead to failure to operate or false operation. Furthermore, by combining the effective value of the signal obtained in the time domain with the frequency domain signal and the corrected frequency domain comparison threshold, DC arc faults in photovoltaic power generation systems can be detected. This avoids the low reliability caused by relying solely on the comparison results between the frequency domain signal and the frequency domain threshold for arc fault detection, thereby improving the accuracy of photovoltaic DC arc fault detection while avoiding failure to operate and false operation.

[0044] In addition, this application also provides a detection device and medium for photovoltaic DC arc faults, which corresponds to the above-mentioned detection method for photovoltaic DC arc faults and has the same effect. Attached Figure Description

[0045] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a photovoltaic DC arc fault detection method provided in an embodiment of this application;

[0047] Figure 2 A flowchart illustrating a photovoltaic DC arc fault detection method provided in another embodiment of this application;

[0048] Figure 3 A flowchart illustrating a photovoltaic DC arc fault detection method provided in another embodiment of this application;

[0049] Figure 4 A structural diagram of a photovoltaic DC arc fault detection device provided in an embodiment of this application;

[0050] Figure 5 This is a structural diagram of a photovoltaic DC arc fault detection device provided in another embodiment of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0052] The core of this application is to provide a method, device, and medium for detecting photovoltaic DC arc faults. It corrects the frequency domain comparison threshold in real time based on the acquired time-domain signal, avoiding the risk of abrupt changes in frequency domain characteristics due to MPPT adjustments and other operating conditions when correcting the frequency domain comparison threshold based on the FFT analysis results of the frequency domain signal. This prevents errors in the frequency domain comparison threshold correction, leading to malfunctions or failures to operate. Furthermore, it combines the frequency domain signal, the frequency domain comparison threshold, and the effective signal value calculated from the time-domain signal to detect DC arc faults, avoiding the low reliability caused by relying solely on the comparison between the frequency domain signal and the frequency domain threshold.

[0053] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] With the continuous development of photovoltaic power generation, DC power supply systems have been widely used and received attention. Factors such as aging lines, animal bites, poor contact of contactors, and wear can easily cause photovoltaic DC arcs. Photovoltaic DC arcs do not have a zero-crossing point, are difficult to extinguish, and can easily expand the scope of the fault. If no measures are taken, they can easily cause fires. Therefore, the detection of photovoltaic DC arc faults is crucial to the safety and reliability of photovoltaic power generation systems.

[0055] Currently, the frequency domain threshold method is commonly used to detect DC arc faults. After a DC arc fault occurs, the high-frequency voltage and current generated by the fault are converted to the frequency domain and compared with a preset threshold to determine the occurrence of the arc fault. The selection of the preset threshold is crucial in arc fault diagnosis. If the preset threshold is too high, it may cause the protection equipment to fail to operate, meaning that even a minor arc fault on the DC side of the photovoltaic power generation system can escalate into a more serious problem, potentially even causing a fire. If the preset threshold is too low, it may cause the protection device to malfunction, leading to the shutdown of some or all of the photovoltaic power generation equipment. This necessitates alerting maintenance personnel to perform system repairs, which not only increases the difficulty of maintenance work but also takes time for the photovoltaic power generation system to stop generating electricity until the fault is resolved, resulting in reduced economic benefits or even losses for the photovoltaic power station.

[0056] In frequency domain thresholding, Fast Fourier Transform (FFT) is a commonly used method for spectral analysis in the frequency domain. The FFT analysis results of the arc signal in the frequency domain are used to determine whether to change the preset threshold. However, photovoltaic power generation systems are affected by operating conditions such as MPPT adjustments, inverter startup, changes in irradiance, and inverter operation, causing abrupt changes in frequency domain characteristics. Therefore, determining whether to change the preset threshold based solely on the FFT analysis results of the arc signal may still lead to malfunctions and failures to operate, and the reliability of arc fault diagnosis based solely on the comparison between the frequency domain signal and the frequency domain threshold is low.

[0057] To avoid abrupt changes in frequency domain characteristics due to MPPT adjustments, inverter startup, changes in irradiance, and inverter operating conditions when correcting preset thresholds based on FFT analysis results of arc signals in the frequency domain, which could lead to failure to operate or false operation, and to address the low reliability of relying solely on comparisons between frequency domain signals and frequency domain thresholds for arc fault detection, this application provides a photovoltaic DC arc fault detection method. This method real-time corrects the frequency domain comparison threshold based on the acquired time domain signal to prevent failure to operate or false operation. The effective signal value obtained from the time domain signal, the frequency domain signal, and the frequency domain comparison threshold are combined to determine whether a DC arc fault has occurred, thereby improving the accuracy of photovoltaic DC arc fault detection.

[0058] Figure 1 A flowchart of a photovoltaic DC arc fault detection method provided in an embodiment of this application is shown below. Figure 1 As shown, the method includes:

[0059] S10: Collect the line current signal of the photovoltaic power generation system under test.

[0060] In step S10, when acquiring the line current signal of the photovoltaic power generation system under test, either a PV analog source or a photovoltaic array can be used; this application does not limit the choice. In specific implementation, the current acquisition device and the arc generator used to acquire the line current signal are respectively installed at the positive and negative buses on the DC side of the DC / AC grid-connected inverter. The line current signal is sampled by an external chip's AD converter, and the AD sampling data is transmitted externally through the SPI interface. Since high sampling rate and high sampling accuracy can preserve high-frequency information in the line current signal as much as possible, when acquiring the line current signal of the photovoltaic power generation system under test, a high sampling rate and high sampling accuracy should be selected as much as possible, provided that the equipment operating conditions permit.

[0061] S11: Perform time-domain processing on the line current signal to obtain the corresponding time-domain signal, and calculate the corresponding effective signal value from the time-domain signal.

[0062] After acquiring the line current signal in step S10, in step S11, the acquired line current signal is processed in the time domain to obtain the corresponding time domain signal. Due to the limitation of the AD sampling amplitude range, it is necessary to detect the range of the current amplitude. If the peak value of the line current signal exceeds half of the AD sampling amplitude range, it will affect the subsequent FFT operation. Therefore, the line current signal needs to be reduced according to a preset ratio, and the signal is restored according to the preset ratio after the FFT operation is completed. It can be understood that the AD sampling acquires the signal of the current transformer. There is a certain DC bias in the output signal of the current transformer. When the DC bias is large, it will affect the frequency detection range of the FFT. Therefore, it is necessary to eliminate the DC bias of the current. This is done by calculating the average value of the line current signal and then subtracting the average value from all line current signals.

[0063] In addition, in step S11, after obtaining the time-domain signal, it is necessary to perform window calculation on the time-domain signal to obtain the corresponding effective signal value. It should be noted that the effective signal value is the time-domain data used for DC arc fault identification, thereby achieving the purpose of judging whether a DC arc fault has occurred based on the time-domain data.

[0064] S12: Perform frequency domain processing on the time domain signal to obtain the corresponding frequency domain signal.

[0065] When performing frequency domain processing on the time domain signal obtained in step S11, since the line current signal is a non-periodic continuous signal, directly performing FFT operation can easily cause spectral leakage. Therefore, windowed FFT operation is required. The Hanning window is well applicable to non-periodic signals. Therefore, the conversion from time domain signal to frequency domain signal is achieved by adding Hanning window FFT operation. Since the signal is scaled according to a preset ratio during the time domain processing of the line current signal, the data after FFT needs to be restored according to the preset ratio.

[0066] S13: Correct the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal.

[0067] In step S13, when the frequency domain comparison threshold of the frequency domain signal is corrected based on the effective value of the signal obtained in step S11, the second difference of the effective value is first calculated through the effective value of each signal. When the second difference of the effective value is less than the preset value, the frequency domain comparison threshold is increased. In practice, the preset value is set very small. It can be understood that when the second difference of the effective value is close to zero, it is determined that there is no DC arc fault in the photovoltaic power generation system to be tested, that is, there is no change in the operating conditions such as MPPT adjustment. At this time, the frequency domain comparison threshold can be increased to avoid false operation or failure to operate.

[0068] Furthermore, when the second-order difference of the effective value is less than a preset value, if it is determined that the current frequency domain comparison threshold is greater than the preset threshold, then the frequency domain comparison threshold is decreased. That is, the frequency domain comparison threshold is continuously increased, and if it is determined that the current frequency domain comparison threshold is greater than the preset threshold, the frequency domain comparison threshold is decreased regardless of whether the second-order difference of the effective value is less than the preset value. In other words, when the frequency domain comparison threshold is increased to the preset threshold, regardless of whether the second-order difference of the effective value is close to zero, a DC arc fault is determined, and the frequency domain comparison threshold is decreased.

[0069] When the second-order difference of the effective value is not less than a preset value, it is determined whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is greater, the frequency domain comparison threshold is reduced. It can be understood that when the second-order difference of the effective value is large and the frequency domain signal is greater than the current frequency domain comparison threshold, it is determined that there is a DC arc fault, and thus the frequency domain comparison threshold is reduced to correct the frequency domain comparison threshold.

[0070] S14: Determine whether a DC arc fault has occurred in the photovoltaic power generation system under test based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold.

[0071] In step S11, the effective value of the signal corresponding to the time-domain signal is obtained. This effective value, the frequency-domain signal, and the corrected frequency-domain comparison threshold are combined to determine whether a DC arc fault has occurred in the photovoltaic power generation system under test. First, an initial frequency-domain comparison threshold is determined based on the effective value of the signal. The second-order difference of the effective value is calculated based on the effective values ​​of adjacent sampling windows to determine the time-domain score. It should be noted that sampling one time-domain signal constitutes one sampling window. Then, the frequency-domain score is determined based on the frequency-domain signal and the frequency-domain comparison threshold. That is, when the frequency-domain signal is greater than the current frequency-domain comparison threshold, the frequency-domain score is increased; otherwise, the frequency-domain score is decreased. After obtaining the time-domain score and the frequency-domain score, they are combined to detect DC arc faults. When the time-domain score corresponding to a first preset number of consecutive sampling windows is greater than the time-domain comparison threshold, and the frequency-domain score is also greater than the frequency-domain comparison threshold, it can be determined that a DC arc fault has occurred in the photovoltaic power generation system under test.

[0072] In photovoltaic power generation systems, both DC bus noise and DC arc signal can be considered as random sequences. Among them, the DC bus noise signal is a stationary random sequence. A stationary random sequence is a time series whose mean, variance, and correlation do not change with time. Its process is without beginning or end in time, and its energy is infinite. Therefore, its frequency domain characteristics can only be described by power spectral density (PSD).

[0073] A non-stationary random sequence refers to the states experienced by an infinite number of samples at a certain moment, equivalent to the states experienced by a single sample over an infinite period of time. Since DC arc faults are determined by the physical properties of the arc and their occurrence time is unpredictable, exhibiting strong randomness, DC arc signals are non-stationary random sequences, and their characteristics are characterized using PSD (Power Distribution Scale). The different random characteristics of DC bus noise and DC arc signals result in significant differences in their PSDs. Therefore, in the detection of DC arc faults, the PSD can be calculated based on the differences in their random characteristics to distinguish between normal signals and DC arc signals.

[0074] It should be noted that PSD refers to the power spectral density characteristic of a random signal in an average sense, representing the signal energy per unit frequency in a random process. PSD is the mean square value (E[x]) of a random variable. 2 (t)], a measure of the square of the effective value, E[x 2 [(t)] corresponds to the area enclosed by the curves relating PSD and frequency (the horizontal axis is PSD, and the vertical axis is frequency, i.e., the PSD-f curve).

[0075] For a stationary random sequence process with zero mean, according to Parseval's theorem, as shown in formula (1), the integral (signal energy) of the square of the square of the square integrable function x(t) is equivalent to the sum of the squares of its Fourier coefficients X(w). Therefore, for an energy signal, the signal energy calculated in the time domain is equal to the signal energy calculated in the frequency domain.

[0076]

[0077] According to the definition of PSD:

[0078]

[0079] As can be seen from formula (2), the square of the effective value RMS of the signal corresponds to the PSD of the signal.

[0080] For stationary random sequence processes with non-zero mean, they do not possess square integrability, but they can be transformed into Fourier transforms of the signal PSD based on the Wiener-Khinchin theorem, as shown in Equation (3):

[0081]

[0082] In formula (3), when τ=0, the autocorrelation function is equal to the energy of the signal, where R x (0) represents the maximum value of the autocorrelation function. Comparing formulas (1) and (3), it can be seen that the square of the effective value RMS corresponds to the integral of the PSD-f curve. The DC bus noise is a stationary random signal, therefore, there is a correspondence between the PSD of the DC bus noise and the effective value RMS of the signal. Thus, the effective value RMS can be used instead of PSD for time-domain judgment.

[0083] When determining the variation law of DC bus noise signal over time, the DC bus background noise signal is a stationary random signal. When performing frequency domain analysis on the DC bus background noise, if the time window is too long, the signal characteristics at the start of the arc may be missed, while if the time window is too short, it may not be possible to obtain a sufficiently long voltage or current waveform to extract effective information. Therefore, it is necessary to select an appropriate time window length.

[0084] The variation law of DC bus noise signal with time was determined. The Burg algorithm was used to analyze the line current signal under three operating conditions: PV simulator and resistor combination, PV simulator and inverter combination, and actual photovoltaic system. The same length of time signal was selected for different operating conditions, and the signal under each operating condition was windowed. The Burg algorithm was used to analyze the DC bus noise PSD under the three operating conditions, and the differences between PSD under different operating conditions and under the same operating condition in different windows were analyzed.

[0085] In fact, under the same operating conditions, the DC bus noise PSD does not change much over time, meaning that the effective signal value (RMS) of the DC bus noise is relatively fixed under specific operating conditions. This characteristic can be used to distinguish whether a signal is DC bus noise. However, the DC bus noise PSD varies significantly under different operating conditions. The noise level is highest under the operating conditions of the PV simulator and inverter combination, while the noise level is lowest under the actual photovoltaic system operating conditions.

[0086] The above analysis shows that when the photovoltaic power generation system does not experience a DC arc fault, the DC bus noise is a stationary random signal, and the PSD spectrum of each time window is very similar, meaning the area under the PSD-f curves is comparable, i.e., the square of the effective RMS value is similar. Therefore, the difference in the effective RMS value between adjacent windows is close to zero. When the photovoltaic power generation system experiences a DC arc fault, the DC arc signal is no longer a stationary random signal, and the difference in the effective RMS value between adjacent windows is no longer zero. Therefore, the DC arc fault can be determined based on whether the difference in the effective RMS value between adjacent windows changes.

[0087] Based on the relatively fixed distribution of DC bus noise power at various frequency points, the effective value (RMS) of the signal is calculated in units of a preset time window. Since the signal distribution patterns of adjacent windows are similar, the DC bus noise is identified by whether the difference in the effective value (RMS) of the signal after adjacent windows is close to zero.

[0088] Ideally, the difference in the effective values ​​(RMS) of adjacent window signals can determine whether a DC arc fault has occurred. However, due to the influence of changes in sunlight, MPPT adjustments, and inverter operating conditions on photovoltaic power generation systems, DC bus noise may suddenly change. Therefore, relying solely on the difference in the RMS of adjacent window signals to determine arc faults reduces reliability and easily leads to false alarms and failures to operate. Furthermore, simply adjusting the frequency domain comparison threshold based on the FFT analysis results of the arc signal in the frequency domain to adapt to changes in photovoltaic power generation system operating conditions can also cause false alarms and failures to operate when actual arc faults occur.

[0089] Therefore, this application detects DC arc faults by combining time and frequency domain methods, and uses the characteristic that the second-order difference of the effective value of the signal RMS is independent of the background noise level to correct the frequency domain comparison threshold.

[0090] In practice, after determining whether a DC arc fault has occurred in the photovoltaic power generation system to be detected based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold, it is determined whether the second-order difference of the effective value corresponding to the second consecutive preset number of sampling windows is in an upward state. If it is in an upward state, it can be determined that the MPPT is in an upward state. In order to avoid false action or failure to act due to the rise of MPPT, the current frequency domain comparison threshold is increased.

[0091] The photovoltaic DC arc fault detection method provided in this application includes: acquiring the line current signal of the photovoltaic power generation system to be tested, performing time-domain processing on the acquired line current signal to obtain a corresponding time-domain signal, and simultaneously calculating the corresponding signal RMS value from the time-domain signal. After obtaining the time-domain signal, performing frequency-domain processing on the time-domain signal to obtain a corresponding frequency-domain signal, and correcting the frequency-domain comparison threshold of the frequency-domain signal based on the signal RMS value. Finally, determining whether a DC arc fault has occurred in the photovoltaic power generation system to be tested based on the obtained signal RMS value, the frequency-domain signal, and the corrected frequency-domain comparison threshold. Therefore, the technical solution provided in this application corrects the frequency-domain comparison threshold in real time by acquiring the time-domain signal, avoiding the occurrence of sudden changes in frequency-domain characteristic quantities due to the influence of operating conditions such as MPPT adjustment, inverter startup, and changes in light intensity when determining whether to change the preset threshold based on the FFT analysis results of the arc signal in the frequency domain, which could lead to failure to operate or false operation. Furthermore, by combining the effective value of the signal obtained in the time domain with the frequency domain signal and the corrected frequency domain comparison threshold, DC arc faults in photovoltaic power generation systems can be detected. This avoids the low reliability caused by relying solely on the comparison results between the frequency domain signal and the frequency domain threshold for arc fault detection, thereby improving the accuracy of photovoltaic DC arc fault detection while avoiding failure to operate and false operation.

[0092] Figure 2 The flowchart below shows a method for detecting photovoltaic DC arc faults according to another embodiment of this application. In a specific embodiment, correcting the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal includes:

[0093] S100: Calculate the second-order difference of the effective value by calculating the effective values ​​of adjacent signals.

[0094] S101: Determine whether the second difference of the effective value is less than the preset value. If it is less than the preset value, proceed to step S102. If it is not less than the preset value, proceed to step S103.

[0095] S102: Increase the frequency domain comparison threshold and determine whether the current frequency domain comparison threshold is greater than the preset threshold. If it is greater, proceed to step S104.

[0096] After calculating the second-order difference of the effective value by the effective value of each signal, when the second-order difference of the effective value is less than the preset value, the frequency domain comparison threshold is increased. In fact, in specific implementation, the preset value is set very small. That is, when the second-order difference of the effective value is close to zero, it is determined that there is no DC arc fault in the photovoltaic power generation system under test, and thus it is determined that there is no change in operating conditions such as MPPT adjustment. At this time, the frequency domain comparison threshold can be increased to avoid false operation or failure to operate.

[0097] In addition, if it is determined that the current frequency domain comparison threshold is greater than the preset threshold, the frequency domain comparison threshold is decreased. That is, the frequency domain comparison threshold is continuously increased, and when it is determined that the current frequency domain comparison threshold is greater than the preset threshold, the frequency domain comparison threshold is decreased regardless of whether the second-order difference of the effective value is smaller than the preset value at this time. In other words, when the frequency domain comparison threshold is increased to the preset threshold, it is determined that a DC arc fault exists regardless of whether the second-order difference of the effective value is close to zero, and the frequency domain comparison threshold is decreased at this time.

[0098] S103: determining whether the frequency domain signal is greater than the current frequency domain comparison threshold, and if the frequency domain signal is greater than the frequency domain comparison threshold, proceeding to step S104.

[0099] S104: decreasing the frequency domain comparison threshold.

[0100] When the second-order difference of the effective value is not less than the preset value, it is determined whether the frequency domain signal is greater than the current frequency domain comparison threshold, and if the frequency domain signal is greater than the frequency domain comparison threshold, the frequency domain comparison threshold is decreased. It can be understood that when the second-order difference of the effective value is large and the frequency domain signal is greater than the current frequency domain comparison threshold, it is determined that a DC arc fault exists, and then the frequency domain comparison threshold is decreased to realize correction of the frequency domain comparison threshold.

[0101] In the detection method for photovoltaic DC arc fault provided by the embodiments of the present application, the frequency domain comparison threshold is corrected in real time through the collected time-domain signal, which avoids the problem that when determining whether to change the preset threshold according to the FFT analysis result of the arc signal in the frequency domain, the frequency domain characteristic quantity will mutate due to the influence of operating conditions such as MPPT adjustment, inverter startup and illumination intensity change of the photovoltaic power generation system, thereby causing occurrence of refusal to operate and false operation. That is, the frequency domain comparison threshold is dynamically adjusted according to the change of the time-domain signal, thereby reducing false operations and refusal to operate.

[0102] Figure 3 it is a flow chart of the detection method for photovoltaic DC arc fault provided by another embodiment of the present application. Determining whether a DC arc fault occurs in the photovoltaic power generation system to be detected according to the signal effective value, the frequency domain signal and the corrected frequency domain comparison threshold comprises:[+END]]

[0103] S200: determining an initial frequency domain comparison threshold according to the signal effective value.

[0104] In a specific embodiment, one collected time-domain signal is taken as one sampling window. When the signal effective value RMS of each window is obtained, reference values a1 and a2 are set. When RMS>a1, the frequency domain comparison threshold is set as b1; when a1<RMS<a2, the frequency domain comparison threshold is set as b2; when RMS>a2, the frequency domain comparison threshold is set as b3. After the initial value of the frequency domain comparison threshold is determined, the frequency domain comparison threshold is dynamically adjusted subsequently according to the change condition of the time-domain signal.

[0105] S201: Calculate the second-order difference of effective values based on adjacent signal effective values to determine a time-domain score; wherein, one time-domain signal sampled corresponds to one sampling window.

[0106] First, calculate the difference of signal effective values between adjacent windows, define the signal effective value of the current window as RMS(i), the signal effective value of the previous window as RMS(i-1), the increment of the signal effective value difference as up1, and the decrement of the signal effective value difference as down1. When RMS(i)>RMS(i-1), up1=RMS(i)-RMS(i-1); otherwise, down1=RMS(i-1)-RMS(i).

[0107] Second, when it is determined that down1>up1, add 2 to the time-domain score; when down1<up1, subtract 1 from the time-domain score. The time-domain score obtained after each accumulation is the time-domain score of the current time-domain window. It should be noted that the initial value of the time-domain score is zero, and the present application does not limit the specific values and manners of addition and subtraction when calculating the time-domain score.

[0108] S202: Determine a frequency-domain score according to a frequency-domain signal and a frequency-domain comparison threshold.

[0109] When determining the frequency-domain score according to the frequency-domain signal and the frequency-domain comparison threshold, if the frequency-domain signal is greater than the frequency-domain comparison threshold, add 2 to the frequency-domain score; if the frequency-domain signal is greater than N times the frequency-domain comparison threshold, add 50 to the frequency-domain score; if the frequency-domain signal is less than the frequency-domain comparison threshold, subtract 1 from the frequency-domain score. It should be noted that N is a positive integer, and the present application does not limit the value of N. In addition, the present application also does not limit the specific values and manners of addition and subtraction when calculating the frequency-domain score.

[0110] S203: Determine whether the current time-domain score and frequency-domain score corresponding to a consecutive first preset number of sampling windows satisfy a preset condition, and if the preset condition is satisfied, proceed to step S204; wherein the preset condition is that the time-domain score is greater than a time-domain comparison threshold and the frequency-domain score is greater than the frequency-domain comparison threshold.

[0111] S204: Determine that a DC arc fault occurs in the photovoltaic power generation system to be detected.

[0112] When it is determined that the current time-domain score and frequency-domain score corresponding to a consecutive first preset number of sampling windows satisfy the preset condition, it is determined that a DC arc fault occurs in the photovoltaic power generation system to be detected. For example, if the first preset number is 15, when the time-domain score in 15 consecutive windows is greater than the time-domain comparison threshold and the frequency-domain score is greater than the frequency-domain comparison threshold, it is determined that a DC arc fault occurs in the photovoltaic power generation system to be detected.

[0113] It is worth noting that when the time-domain score within 15 consecutive windows is greater than the time-domain threshold, it can only be determined that a DC arc fault may occur in the photovoltaic power generation system to be detected. Therefore, it is necessary to further determine the situation of the frequency-domain score, that is, the preset condition is that the time-domain score is greater than the time-domain comparison threshold and the frequency-domain score is greater than the frequency-domain comparison threshold.

[0114] The detection method for photovoltaic DC arc faults provided by the embodiments of the present application detects DC arc faults of the photovoltaic power generation system by combining the effective value of the signal obtained in the time domain, the frequency-domain signal and the corrected frequency-domain comparison threshold, which avoids low reliability caused by determining arc faults only based on the comparison result between the frequency-domain signal and the frequency-domain threshold, thereby improving the detection accuracy of photovoltaic DC arc faults.

[0115] Based on the above embodiment, in order to avoid the mutation of frequency-domain characteristic quantities caused by the influence of operating conditions such as MPPT adjustment on the photovoltaic power generation system, which further leads to the occurrence of missing operation and false operation. Therefore, after determining whether a DC arc fault occurs in the photovoltaic power generation system to be detected according to the effective value of the signal, the frequency-domain signal and the corrected frequency-domain comparison threshold, it is judged whether the second-order difference of the effective value corresponding to a consecutive second preset number of sampling windows is in an ascending state. If yes, the current frequency-domain comparison threshold is increased. For ease of understanding, an example is given below.

[0116] For example, the second preset number is 400, that is, if it is determined that there are 400 consecutive sampling windows whose corresponding second-order differences of effective values are in an ascending state, that is, up1>dowm1 in 400 consecutive sampling windows, it is determined that MPPT is in an ascending state. In order to avoid false operation and missing operation caused by mutation of frequency-domain characteristic quantities, the frequency-domain comparison threshold is appropriately corrected, that is, the current frequency-domain comparison threshold is increased to avoid false operation and missing operation. Of course, if there are one or more dowm1<up1 in 400 consecutive sampling windows, it is determined that MPPT is not in an ascending state.

[0117] The detection method for photovoltaic DC arc faults provided by the embodiments of the present application, after determining whether a DC arc fault occurs in the photovoltaic power generation system to be detected according to the effective value of the signal, the frequency-domain signal and the corrected frequency-domain comparison threshold, determines whether the second-order difference of the effective value corresponding to a consecutive second preset number of sampling windows is in an ascending state. If yes, the current frequency-domain comparison threshold is increased, thereby avoiding false operation and missing operation caused by the influence of MPPT operating conditions on the photovoltaic power generation system.

[0118] In specific implementation, when determining the frequency-domain score according to the frequency-domain signal and the frequency-domain comparison threshold, it is judged whether the frequency-domain signal is greater than the current frequency-domain comparison threshold. If it is greater, the frequency-domain score is increased; otherwise, the frequency-domain score is decreased. It should be noted that the present application does not limit the specific values and manners of increase and decrease when calculating the frequency-domain score.

[0119] The photovoltaic DC arc fault detection method provided in this application determines the frequency domain score by comparing the frequency domain signal with the frequency domain comparison threshold, and combines the frequency domain score with the time domain score to determine whether the photovoltaic power generation system has generated a DC arc fault. This avoids the low reliability caused by relying solely on the comparison result of the frequency domain signal and the frequency domain threshold for arc fault detection, thereby improving the accuracy of photovoltaic DC arc fault detection.

[0120] In a specific embodiment, time-domain processing of the line current signal includes detecting the current amplitude range and eliminating DC bias. Since the line current signal is acquired by an external chip's AD serial port via a current transformer, if the peak value of the line current signal exceeds half of the AD detection range, it will affect the subsequent FFT operation in the frequency domain. Therefore, during time-domain processing of the line current signal, the line current signal is adjusted according to a preset ratio, i.e., the current signal is reduced by a preset ratio. After the FFT operation is completed, the signal is restored according to this preset ratio.

[0121] Furthermore, since there is a certain DC bias in the output signal of the current transformer, a large DC bias can affect the frequency detection range of the FFT. Therefore, the DC bias is eliminated by calculating the average value of the line current signal and subtracting the average value from each line current signal. The FFT operation is then performed after the DC bias is eliminated.

[0122] The photovoltaic DC arc fault detection method provided in this application adjusts the line current signal according to a preset ratio during time-domain processing. This prevents the peak value of the line current signal from affecting the subsequent FFT operation in the frequency domain processing when it exceeds half of the AD detection range. Furthermore, the time-domain processing of the line current signal also includes eliminating the DC bias of the current, thereby avoiding any impact on the FFT frequency detection range.

[0123] Based on the above embodiments, after obtaining the time-domain signal, the signal is first filtered and amplified. When converting the time-domain signal to the frequency-domain signal, in order to avoid the line current signal being a non-periodic continuous signal, directly performing FFT operation can easily cause spectral leakage. Since the Hanning window has good applicability to non-periodic signals, the time-domain signal is subjected to Hanning window FFT operation to obtain the corresponding FFT signal, thereby realizing the conversion of the time-domain signal to the frequency-domain signal.

[0124] Furthermore, since the signal is scaled according to a preset ratio during the time-domain processing of the line current signal, it is necessary to restore the data after FFT according to the preset ratio. The photovoltaic DC arc fault detection method provided in this application adds a Hanning window FFT operation when converting the time-domain signal to a frequency-domain signal to avoid spectral leakage that can easily occur with direct FFT operations.

[0125] The above embodiments have described in detail the method for detecting photovoltaic DC arc faults. This application also provides embodiments of a photovoltaic DC arc fault detection device. It should be noted that this application describes the device embodiments from two perspectives: one based on functional modules and the other based on hardware structure.

[0126] Figure 4 This is a structural diagram of a photovoltaic DC arc fault detection device provided in an embodiment of this application, as shown below. Figure 4 As shown, the device includes:

[0127] The acquisition module 10 is used to acquire the line current signal of the photovoltaic power generation system under test.

[0128] The first processing module 11 is used to perform time-domain processing on the line current signal to obtain the corresponding time-domain signal, and to calculate the corresponding signal effective value from the time-domain signal.

[0129] The second processing module 12 is used to perform frequency domain processing on the time domain signal to obtain the corresponding frequency domain signal.

[0130] The correction module 13 is used to correct the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal.

[0131] The determination module 14 is used to determine whether a DC arc fault has occurred in the photovoltaic power generation system under test based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold.

[0132] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0133] The photovoltaic DC arc fault detection device provided in this application includes: acquiring the line current signal of the photovoltaic power generation system to be tested, performing time-domain processing on the acquired line current signal to obtain a corresponding time-domain signal, and simultaneously calculating the corresponding signal RMS value from the time-domain signal. After obtaining the time-domain signal, performing frequency-domain processing on the time-domain signal to obtain a corresponding frequency-domain signal, and correcting the frequency-domain comparison threshold of the frequency-domain signal based on the signal RMS value. Finally, determining whether a DC arc fault has occurred in the photovoltaic power generation system to be tested based on the obtained signal RMS value, the frequency-domain signal, and the corrected frequency-domain comparison threshold. Therefore, the technical solution provided in this application corrects the frequency-domain comparison threshold in real time by acquiring the time-domain signal, avoiding the occurrence of sudden changes in frequency-domain characteristic quantities due to the influence of operating conditions such as MPPT adjustment, inverter startup, and changes in light intensity when determining whether to change the preset threshold based on the FFT analysis results of the arc signal in the frequency domain, which could lead to failure to operate or false operation. Furthermore, by combining the effective value of the signal obtained in the time domain with the frequency domain signal and the corrected frequency domain comparison threshold, DC arc faults in photovoltaic power generation systems can be detected. This avoids the low reliability caused by relying solely on the comparison results between the frequency domain signal and the frequency domain threshold for arc fault detection, thereby improving the accuracy of photovoltaic DC arc fault detection while avoiding failure to operate and false operation.

[0134] Figure 5 This is a structural diagram of a photovoltaic DC arc fault detection device provided in another embodiment of this application, as shown below. Figure 5 As shown, the photovoltaic DC arc fault detection device includes: a memory 20 for storing computer programs;

[0135] The processor 21 is used to execute a computer program to implement the steps of the photovoltaic DC arc fault detection method as described in the above embodiments.

[0136] The photovoltaic DC arc fault detection device provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0137] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0138] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the photovoltaic DC arc fault detection method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, relevant data involved in the photovoltaic DC arc fault detection method.

[0139] In some embodiments, the photovoltaic DC arc fault detection device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0140] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the detection device for photovoltaic DC arc faults and may include more or fewer components than shown.

[0141] The photovoltaic DC arc fault detection device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: photovoltaic DC arc fault detection method.

[0142] The photovoltaic DC arc fault detection device provided in this application corrects the frequency domain comparison threshold in real time using the acquired time-domain signal. This avoids the problem of sudden changes in frequency domain characteristic quantities caused by the influence of operating conditions such as MPPT adjustment, inverter startup, and changes in light intensity on the photovoltaic power generation system when determining whether to change the preset threshold based on the FFT analysis results of the arc signal in the frequency domain. This can lead to failure to operate or false operation. Furthermore, by combining the effective value of the signal obtained in the time domain with the frequency domain signal and the corrected frequency domain comparison threshold, the device detects DC arc faults in the photovoltaic power generation system. This avoids the low reliability caused by relying solely on the comparison results of the frequency domain signal and the frequency domain threshold for arc fault detection, thereby improving the accuracy of photovoltaic DC arc fault detection while avoiding failure to operate and false operation.

[0143] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0144] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] The foregoing provides a detailed description of a photovoltaic DC arc fault detection method, apparatus, and medium. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0146] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for detecting photovoltaic DC arc faults, characterized in that, include: Collect the line current signal of the photovoltaic power generation system under test; The line current signal is processed in the time domain to obtain the corresponding time domain signal, and the corresponding signal effective value is calculated from the time domain signal. The time-domain signal is processed in the frequency domain to obtain the corresponding frequency-domain signal; The frequency domain comparison threshold of the frequency domain signal is corrected based on the effective value of the signal; Based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold, it is determined whether the photovoltaic power generation system under test has experienced a DC arc fault. The step of correcting the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal includes: The second-order difference of the effective value is obtained by calculating the effective values ​​of adjacent signals; Determine whether the second difference of the effective value is less than a preset value; If it is less than the preset value, then the frequency domain comparison threshold is increased, and it is determined whether the current frequency domain comparison threshold is greater than the preset threshold. If it is greater, then the step of decreasing the frequency domain comparison threshold is entered. If the signal is not less than the preset value, then determine whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is greater than the frequency domain comparison threshold, then decrease the frequency domain comparison threshold.

2. The method for detecting photovoltaic DC arc faults according to claim 1, characterized in that, The step of determining whether a DC arc fault has occurred in the photovoltaic power generation system under test based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold includes: The initial frequency domain comparison threshold is determined based on the effective value of the signal; The second-order difference of the effective values ​​is calculated based on the effective values ​​of the signals in adjacent sampling windows to determine the time-domain fraction; wherein, sampling one of the time-domain signals constitutes one sampling window; The frequency domain score is determined based on the frequency domain signal and the frequency domain comparison threshold. Determine whether the current time domain score and frequency domain score corresponding to the first preset number of consecutive sampling windows meet preset conditions; If the preset conditions are met, it is determined that the photovoltaic power generation system under test has a DC arc fault; wherein, the preset conditions are that the time domain score is greater than the time domain comparison threshold and the frequency domain score is greater than the frequency domain comparison threshold.

3. The method for detecting photovoltaic DC arc faults according to claim 2, characterized in that, After determining whether a DC arc fault has occurred in the photovoltaic power generation system under test based on the effective value of the signal, the frequency domain signal, and the corrected frequency domain comparison threshold, the method further includes: Determine whether the second-order difference of the effective value corresponding to the second consecutive preset number of sampling windows is in an increasing state. If so, increase the current frequency domain comparison threshold.

4. The method for detecting photovoltaic DC arc faults according to claim 2, characterized in that, Determining the frequency domain score based on the frequency domain signal and the frequency domain comparison threshold includes: Determine whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is, increase the frequency domain score; otherwise, decrease the frequency domain score.

5. The method for detecting photovoltaic DC arc faults according to claim 1, characterized in that, Time-domain processing of the line current signal includes: The current amplitude range corresponding to the line current signal is detected so that the magnitude of the line current signal can be adjusted according to a preset ratio; The average value of the line current signal is calculated to eliminate the DC bias of the current.

6. The method for detecting photovoltaic DC arc faults according to claim 5, characterized in that, Frequency domain processing of the time-domain signal includes: The time-domain signal is subjected to a Hanning window FFT operation to obtain the corresponding FFT signal; The magnitude of the FFT signal is restored according to the preset ratio.

7. A device for detecting photovoltaic DC arc faults, characterized in that, include: The acquisition module is used to acquire the line current signal of the photovoltaic power generation system under test; The first processing module is used to perform time-domain processing on the line current signal to obtain a corresponding time-domain signal, and to calculate the corresponding signal effective value from the time-domain signal. The second processing module is used to perform frequency domain processing on the time domain signal to obtain the corresponding frequency domain signal; The correction module is used to correct the frequency domain comparison threshold of the frequency domain signal based on the effective value of the signal; The determination module is used to determine whether the photovoltaic power generation system under test has a DC arc fault based on the effective value of the signal, the frequency domain signal and the corrected frequency domain comparison threshold. The correction module is specifically used for: The second-order difference of the effective value is obtained by calculating the effective values ​​of adjacent signals; Determine whether the second difference of the effective value is less than a preset value; If it is less than the preset value, then the frequency domain comparison threshold is increased, and it is determined whether the current frequency domain comparison threshold is greater than the preset threshold. If it is greater, then the step of decreasing the frequency domain comparison threshold is entered. If the signal is not less than the preset value, then determine whether the frequency domain signal is greater than the current frequency domain comparison threshold. If it is greater than the frequency domain comparison threshold, then decrease the frequency domain comparison threshold.

8. A device for detecting photovoltaic DC arc faults, characterized in that, Includes memory used to store computer programs; A processor, configured to execute the computer program to implement the steps of the photovoltaic DC arc fault detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the photovoltaic DC arc fault detection method as described in any one of claims 1 to 6.

Images