Photovoltaic power generation direct current arc fault detection method and device, storage medium and equipment

Abstract

The invention discloses a photovoltaic power generation direct current arc fault detection method and device, a storage medium and equipment, and belongs to the technical field of current arc fault detection. According to the photovoltaic power generation direct current arc fault detection method, a sliding time window sampling mode is adopted, current signal segments in different time periods in a target photovoltaic circuit are obtained, the corresponding current signal segment is sampled based on each sliding time window, and the fault of the target photovoltaic circuit is detected by analyzing local path length characteristics of current signals. The waveform roughness is calculated to judge whether an arc event occurs or not, the noise influence is effectively inhibited, the detection accuracy and the response speed are improved, efficient identification of the direct-current arc fault is achieved, and the problems that a current mainstream detection method is high in false alarm rate and low in response speed are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to a photovoltaic power generation DC arc fault detection method, device, storage medium and equipment, belonging to the current arc fault detection technical field. BACKGROUND

[0002] In a photovoltaic system, the DC side is usually composed of multiple photovoltaic components in series or parallel, and the output voltage can reach hundreds of volts or even thousands of volts. Due to the long-term operation of the photovoltaic system in the outdoor complex environment, factors such as loose joints, insulation aging, and water intrusion are prone to cause DC arc faults. Once formed, it is difficult to extinguish itself and may ignite surrounding materials, causing a fire accident.

[0003] However, the current mainstream detection method still has the problems of high false alarm rate and slow response speed; for example, by analyzing the time-frequency domain features of the photovoltaic current signal, the DC side current is collected, after filtering preprocessing, the arc features such as high-frequency noise are extracted by means such as fast Fourier transform (FFT) or wavelet transform, and compared with the preset threshold to determine whether a fault occurs. In this method, complex signal processing and multi-level confirmation mechanism set to reduce false alarms result in a long response delay of several seconds, making it difficult to provide timely warning; on the other hand, the interference signals such as sudden changes in light and inverter switching noise during normal operation of the photovoltaic system are prone to produce similar arc features, resulting in a high false alarm rate of the system. SUMMARY

[0004] The purpose of the present application is to overcome the deficiencies in the prior art, and provide a photovoltaic power generation DC arc fault detection method, device, storage medium and equipment, which realizes efficient identification of DC arc faults by analyzing the local path length characteristics of the current signal, solving the problem of high false alarm rate and slow response speed of the current mainstream detection method.

[0005] To solve the above technical problems, the present application is realized by the following technical scheme:

[0006] The present application provides a photovoltaic power generation DC arc fault detection method, which comprises:

[0007] Sliding time window sampling is performed on the target photovoltaic circuit to obtain a current signal segment of each sliding time window;

[0008] According to the current signal segment of each sliding time window, the waveform roughness corresponding to the current signal segment is calculated;

[0009] Based on the waveform roughness, the number of arc events occurring in each sliding time window is determined, and if a preset number of arc events occur in a single sliding time window, an arc fault is determined to occur.

[0010] Further, the sampling of the target photovoltaic circuit in the sliding time window obtains a current signal segment in each sliding time window, and the sampling comprises:

[0011] The sampling of the target photovoltaic circuit in the sliding time window is expressed by the following formula: ; Wherein, represents the current signal segment including sample points in the th sliding time window, represents a total of sample points in the current signal segment, represents the current value of the 1st sample point in the current signal segment, represents the current value of the 2nd sample point in the current signal segment, represents the current value of the th sample point in the current signal segment.

[0012] Further, the calculation of the waveform roughness corresponding to the current signal segment in each sliding time window comprises:

[0013] The waveform roughness corresponding to the current signal segment is calculated by the following formula:

[0014] ;

[0015] Wherein, represents the waveform roughness corresponding to the current signal segment in the th sliding time window; represents the current value of the 1st sample point in the current signal segment; represents the current value of the 2nd sample point in the current signal segment. represents the current value of the th sample point in the current signal segment.

[0016] Further, the determination of the number of arc events occurring in each sliding time window based on the waveform roughness comprises:

[0017] The mean value and the standard deviation of the waveform roughness corresponding to all sliding time windows are calculated;

[0018] The threshold value of the waveform roughness is determined according to the mean value and the standard deviation of the waveform roughness;

[0019] The waveform roughness corresponding to each sliding time window is compared with the threshold value of the waveform roughness, and if the waveform roughness of a certain sliding time window is greater than the threshold value of the waveform roughness, it is determined that the arc event occurs in the sliding time window;

[0020] If preset number of arc events are accumulated in a single sliding time window, it is determined that arc fault occurs.

[0021] Further, the mean and standard deviation of the waveform roughness corresponding to all sliding time windows are calculated, comprising:

[0022] The mean of the waveform roughness is calculated by the following formula:

[0023] ;

[0024] Wherein, represents the mean of the waveform roughness; represents the total number of sliding time windows, represents the number of sliding time windows, represents the waveform roughness corresponding to the current signal segment in the i-th sliding time window; The standard deviation of the waveform roughness is calculated by the following formula:

[0025]

[0026] ;

[0027] Wherein, represents the standard deviation of the waveform roughness.

[0028] Further, the waveform roughness threshold is determined according to the mean and standard deviation of the waveform roughness, comprising:

[0029] The waveform roughness threshold is calculated by the following formula:

[0030] ;

[0031] Wherein, represents the waveform roughness threshold, represents the mean of the waveform roughness, represents the standard deviation of the waveform roughness; represents an adjustment coefficient.

[0032] Further, if preset number of arc events are accumulated in a single sliding time window, it is determined that arc fault occurs, comprising:

[0033] The determination results of the latest n sliding time windows are stored to form a determination result queue; wherein, the arc event occurrence determination result is stored as "1", otherwise as "0"; When a new determination result occurs, the new determination result is inserted into the tail of the determination result queue, and the existing old determination result is moved forward in turn;

[0034]

[0035] ​​When the number of "1" in the judgment result queue reaches a preset value, an arc fault alarm is triggered.

[0036] The second aspect of the present application provides a photovoltaic power generation direct current arc fault detection device for realizing the photovoltaic power generation direct current arc fault detection method.

[0037] The data acquisition module is configured to sample the target photovoltaic circuit in a sliding time window to obtain a current signal segment of each sliding time window;

[0038] The waveform roughness calculation module is configured to calculate waveform roughness corresponding to the current signal segment according to the current signal segment of each sliding time window.

[0039] The fault discrimination module is configured to determine the number of arc events occurring in each sliding time window based on the waveform roughness, and determine that an arc fault occurs if a preset number of arc events occur in a single sliding time window.

[0040] The third aspect of the present application provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to realize the photovoltaic power generation direct current arc fault detection method.

[0041] The present application also provides a computer device, comprising:

[0042] The memory is used to store instructions;

[0043] The processor is used to execute the instructions to enable the device to perform the photovoltaic power generation direct current arc fault detection method.

[0044] Compared with the prior art, the present application has the following beneficial effects:

[0045] 1. The present application uses a sliding time window sampling method to obtain current signal segments of different time periods in the target photovoltaic circuit, calculates waveform roughness based on the current signal segment sampled for each sliding time window to determine whether an arc event occurs, effectively suppresses noise influence, and improves detection accuracy and response speed.

[0046] 2. The present application uses a dynamic threshold comparison mechanism to update the waveform roughness threshold in combination with the mean value, standard deviation and sliding time window of the waveform roughness, and further reduces the false alarm rate through the form of single sliding time window cumulative arc event number and continuous numerical comparison result for comprehensive judgment of arc fault. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a flowchart of a photovoltaic power generation direct current arc fault detection method provided by an embodiment of the present application;

[0048] Figure 2 is a flowchart for determining an arc fault provided by an embodiment of the present application;

[0049] Figure 3 is an original current waveform diagram provided by an embodiment of the present application;

[0050] Figure 4 is a current waveform diagram for detecting an arc event provided by an embodiment of the present application;

[0051] Figure 5 is a waveform roughness diagram corresponding to a current signal provided by an embodiment of the present application;

[0052] Figure 6 is a structural diagram of a photovoltaic power generation direct current arc fault detection device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] The present application will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0054] Embodiment 1

[0055] As shown in the figure, the present embodiment 1 provides a photovoltaic power generation direct current arc fault detection method, comprising: Figure 1

[0056] sliding time window sampling is performed on the target photovoltaic circuit to obtain a current signal segment of each sliding time window, specifically:

[0057] The continuous sliding time window can track the whole process of arc development, and the sliding time window sampling on the target photovoltaic circuit is expressed by the following formula:

[0058] represents a current signal segment including sample points in the i-th sliding time window, represents a current value of the 1st sample point in the current signal segment, represents a current value of the 2nd sample point in the current signal segment, represents a current value of the i-th sample point in the current signal segment. According to the current signal segment of each sliding time window, the waveform roughness corresponding to the current signal segment is calculated, specifically: The waveform roughness corresponding to the current signal segment is calculated by the following formula:

[0059]

[0060]

[0061] ​​​​​ ;

[0062] Indicates the first Waveform roughness corresponding to a current signal segment within a sliding time window This indicates the first segment of the current signal. Current values ​​at each sample point This indicates the first segment of the current signal. The current value at each sample point.

[0063] Compare the waveform roughness corresponding to each sliding time window With waveform roughness threshold If the waveform roughness is greater than the waveform roughness threshold, then it is determined that an arc event has occurred within the sliding time window.

[0064] In this embodiment, a dynamic threshold mechanism is adopted. The waveform roughness threshold is set based on the historical statistical results of waveform roughness within the current sliding time window, such as... Figure 2 As shown, specifically: calculate the mean and standard deviation of waveform roughness for all current sliding time windows, including: calculating the mean waveform roughness using the following formula: ;

[0065] This represents the mean value of the waveform roughness. Indicates shared ownership A sliding time window.

[0066] The standard deviation of waveform roughness is calculated using the following formula:

[0067] ;

[0068] The standard deviation represents the roughness of the waveform.

[0069] Based on the mean and standard deviation of the waveform roughness, the waveform roughness threshold is determined, including:

[0070] The waveform roughness threshold is calculated using the following formula. : ;

[0071] This represents the adjustment factor.

[0072] Based on waveform roughness, the number of arc events occurring in each sliding time window is determined. If a preset number of arc events occur in a single sliding time window, an arc fault occurs. Specifically:

[0073] Store recent The judgment results of the plurality of sliding time windows form a judgment result queue; wherein, the arc event judgment result is stored as "1"; when a new judgment result appears, the new judgment result is inserted into the tail of the judgment result queue, and the existing old judgment result is moved forward in turn;

[0074] When the number of "1" in the judgment result queue reaches a preset value, an arc fault alarm is triggered.

[0075] The information of the plurality of time windows is comprehensively used for decision-making, and the sensitivity can be flexibly adjusted. In addition, the queue length and the preset value of the cumulative arc event number can be modified to adapt to different application scenarios.

[0076] As shown in Figure 3 , a Hall effect sensor is used to collect the current on the DC side of the circuit to obtain the original current waveform, which provides basic data for subsequent waveform roughness calculation. The sampling frequency is set to 1250 kHz, and the sliding time window is set to 1.6 s, which includes 200 sample points, and the sliding step is 1 sample point each time.

[0077] When a DC arc occurs in the photovoltaic circuit, i.e., an arc event occurs, the current will jump or oscillate sharply, as shown in Figure 4 , at this time, the waveform roughness will also increase significantly, as shown in Figure 5 , especially at the moment of arc generation, the roughness increases significantly, and the current waveform changes significantly before and after the arc occurs. The collection accuracy meets the arc recognition requirements.

[0078] In this embodiment, an array Y with a length of is created as a judgment result queue, and the array Y stores the judgment results of the last sliding time windows. When a new result appears, the new result is inserted into the tail of the array Y. If , it is marked as an arc event point, and the count is , otherwise , indicates whether an arc event point corresponding to appears in the array Y.

[0079] In this embodiment, it is set that if more than 20 arc events appear in the array Y, it is determined that an arc fault occurs. When the number of "1" in the array Y reaches 21, an arc fault alarm is triggered. In the form of consistent over-limit of multiple points, the criterion reliability is enhanced, the false judgment caused by temporary disturbance is avoided, and the robustness is improved.

[0080] Embodiment 2

[0081] As shown in Figure 6As shown, the embodiment 2 provides a photovoltaic power generation direct current arc fault detection device, comprising:

[0082] The data acquisition module is configured to sample the target photovoltaic circuit in a sliding time window, and acquire a current signal segment of each sliding time window;

[0083] The waveform roughness calculation module is configured to calculate waveform roughness corresponding to the current signal segment according to the current signal segment of each sliding time window;

[0084] The fault discrimination module is configured to determine the number of arc events occurring in each sliding time window based on the waveform roughness, and determine that an arc fault occurs if a preset number of arc events occur in a single sliding time window.

[0085] It is worth pointing out that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation manners of the above-mentioned method embodiment are all applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be described here.

[0086] Embodiment 3

[0087] The embodiment 3 provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following method steps:

[0088] The target photovoltaic circuit is sampled in a sliding time window, and a current signal segment of each sliding time window is acquired;

[0089] Waveform roughness corresponding to the current signal segment is calculated according to the current signal segment of each sliding time window;

[0090] The number of arc events occurring in each sliding time window is determined based on the waveform roughness, and it is determined that an arc fault occurs if a preset number of arc events occur in a single sliding time window.

[0091] Embodiment 4

[0092] The embodiment 4 provides a computer device, comprising:

[0093] A memory is used to store instructions;

[0094] A processor is used to execute the instructions, so that the device executes the following method steps:

[0095] The target photovoltaic circuit is sampled in a sliding time window, and a current signal segment of each sliding time window is acquired;

[0096] Waveform roughness corresponding to the current signal segment is calculated according to the current signal segment of each sliding time window;

[0097] Based on the waveform roughness, the number of arc events occurring in each sliding time window is determined, and if a preset number of arc events occurs in a single sliding time window, it is determined that an arc fault occurs.

[0098] Those skilled in the art will understand that embodiments of the present application can be provided as methods, apparatus, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0099] The present application is described with reference to the flowcharts according to the methods, apparatus (systems), and computer program products of embodiments of the present application. It should be understood that each flow in the flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts. Figure 1 An apparatus for performing each flow or multiple flows or the functions specified in the block Figure 1 An apparatus for performing each flow or multiple flows or the functions specified in the block

[0100] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flow Figure 1 An apparatus for performing each flow or multiple flows or the functions specified in the block

[0101] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide steps for implementing the functions specified in the flow Figure 1 An apparatus for performing each flow or multiple flows or the functions specified in the block

[0102] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the specific embodiments described above, which are merely illustrative and not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which are all within the protection of the present application.

Claims

1. A method for detecting DC arc faults in photovoltaic power generation, characterized in that, include: The target photovoltaic circuit is sampled using a sliding time window to obtain the current signal segment for each sliding time window; Calculate the waveform roughness corresponding to each current signal segment based on the current signal segment of each sliding time window; Based on waveform roughness, the number of arc events occurring in each sliding time window is determined. If a preset number of arc events occur in a single sliding time window, an arc fault is determined to have occurred.

2. The photovoltaic power generation DC arc fault detection method according to claim 1, characterized in that, The step of sampling the target photovoltaic circuit using a sliding time window to obtain the current signal segment for each sliding time window includes: The following formula represents the sliding time window sampling of the target photovoltaic circuit: ； in, Indicates the first Each sliding time window includes Current signal segment at each sample point This indicates that there are a total of [number] segments of current signal. One sample point, This represents the current value at the first sample point in the current signal segment. This represents the current value at the second sample point in the current signal segment. This indicates the first segment of the current signal. The current value at each sample point.

3. The photovoltaic power generation DC arc fault detection method according to claim 2, characterized in that, The step of calculating the waveform roughness corresponding to the current signal segment based on the current signal segment of each sliding time window includes: The waveform roughness corresponding to the current signal segment is calculated using the following formula: ； in, Indicates the first Waveform roughness corresponding to a current signal segment within a sliding time window; This indicates the first segment of the current signal. Current values ​​at each sample point; This indicates the first segment of the current signal. The current value at each sample point.

4. The photovoltaic power generation DC arc fault detection method according to claim 1, characterized in that, The method of determining the number of arc events occurring in each sliding time window based on waveform roughness, and determining that an arc fault has occurred if a preset number of arc events occur in a single sliding time window, includes: Calculate the mean and standard deviation of the waveform roughness corresponding to all sliding time windows; The waveform roughness threshold is determined based on the mean and standard deviation of the waveform roughness. Compare the waveform roughness corresponding to each sliding time window with the waveform roughness threshold. If the waveform roughness of a certain sliding time window is greater than the waveform roughness threshold, it is determined that an electric arc event has occurred in that sliding time window. If a preset number of arc events occur within a single sliding time window, an arc fault is determined to have occurred.

5. The photovoltaic power generation DC arc fault detection method according to claim 4, characterized in that, The calculation of the mean and standard deviation of waveform roughness corresponding to all sliding time windows includes: The mean value of waveform roughness is calculated using the following formula: ； in, This represents the mean value of the waveform roughness; Indicates shared ownership A sliding time window, Indicates the first Waveform roughness corresponding to a current signal segment within a sliding time window; The standard deviation of waveform roughness is calculated using the following formula: ； in, The standard deviation represents the roughness of the waveform.

6. The photovoltaic power generation DC arc fault detection method according to claim 4, characterized in that, The step of determining the waveform roughness threshold based on the mean and standard deviation of the waveform roughness includes: The waveform roughness threshold is calculated using the following formula: ； in, Indicates the waveform roughness threshold. This represents the mean value of the waveform roughness. The standard deviation represents the waveform roughness; This represents the adjustment factor.

7. The photovoltaic power generation DC arc fault detection method according to claim 4, characterized in that, The step of determining that an arc fault has occurred if a preset number of arc events accumulate within a single sliding time window includes: Store recent The judgment results of each sliding time window are used to form a judgment result queue; among them, the judgment result of the occurrence of an electric arc event is stored as "1", and otherwise it is stored as "0". Whenever a new judgment result appears, the new judgment result is inserted at the end of the judgment result queue, and the existing old judgment results are moved forward in turn. When the number of "1"s in the judgment result queue reaches a preset value, an arc fault alarm is triggered.

8. A photovoltaic power generation DC arc fault detection device, characterized in that, The apparatus for implementing the photovoltaic power generation DC arc fault detection method of claim 1 includes: The data acquisition module is configured to perform sliding time window sampling on the target photovoltaic circuit and acquire the current signal segment of each sliding time window; The waveform roughness calculation module is configured to calculate the waveform roughness corresponding to each current signal segment based on the current signal segment of each sliding time window. The fault detection module is configured to determine the number of arc events occurring in each sliding time window based on waveform roughness. If a preset number of arc events occur in a single sliding time window, an arc fault is determined to have occurred.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic power generation DC arc fault detection method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform the photovoltaic power generation DC arc fault detection method as described in any one of claims 1 to 7.

Images