A method and device for identifying DC arc faults in a distributed photovoltaic system

Abstract

The present invention discloses a method and device for identifying DC arc faults in a distributed photovoltaic system in the field of photovoltaic system technology. The method comprises: performing non-overlapping sliding window sampling on the arc current of the target line to generate current data of each time window; calculating the current autocorrelation square sum of each time window according to the current data of each time window; and t The current autocorrelation square sum of each time window is normalized as the standard value, and the arc fault characteristic quantity of each time window is calculated; if the continuous n There are m If the arc fault characteristic quantity of more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists; wherein, t,n,m The present invention can accurately and efficiently realize DC arc fault identification and ensure the safe operation of the distributed photovoltaic system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic systems, and in particular to a method and device for identifying DC arc faults in a distributed photovoltaic system. Background Art

[0002] During the use of photovoltaic systems, as the equipment in power stations age, failures are inevitable. The photovoltaic system has a low insulation level, a small conductor cross-section, and low mechanical strength. During operation, insulation damage, conductor disconnection, or loose contact are prone to occur, which may cause DC arcs and cause fire hazards. The existing detection methods are limited in sensitivity and specificity, and there is a risk of false alarms or missed detections. For example, the arc is judged only by comparing the decibel value of the high-frequency signal with the threshold, which may cause the signal to be oversensitive or insensitive in certain current ranges, resulting in false alarms. Another method is to control the output current of the photovoltaic panel to zero, and then judge whether an arc occurs based on whether the operating status of the working point can be restored, but this method may miss the fault because the arc is not completely melted. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method and device for identifying DC arc faults in a distributed photovoltaic system, which can accurately and efficiently identify DC arc faults and ensure the safe operation of the distributed photovoltaic system.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for identifying a DC arc fault in a distributed photovoltaic system, comprising:

[0006] Perform non-overlapping sliding window sampling on the arc current of the target line to generate current data for each time window;

[0007] Calculate the current autocorrelation square sum of each time window according to the current data of each time window;

[0008] Before each time window t The current autocorrelation square sum of each time window is normalized as the standard value, and the arc fault characteristic quantity of each time window is calculated;

[0009] If continuous n There are m If the arc fault characteristic quantity of more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists;

[0010] in, t, n, m All are constant parameters.

[0011] Optionally, after acquiring the current data of each time window, the non-overlapping sliding window sampling enters an interruption process, performs DC arc fault identification during the interruption process, and samples the current data of the next time window at the same time.

[0012] Optionally, before calculating the current autocorrelation square sum of each time window, the method further includes: performing bandpass filtering on the current data to filter out direct current signals and high-frequency signals.

[0013] Optionally, calculating the current autocorrelation square sum of each time window according to the current data of each time window includes:

[0014] Performing a fast Fourier transform on the current data of each time window to obtain corresponding current spectrum data, wherein the current spectrum data is a vector sequence containing frequency information;

[0015] The current power spectrum density is obtained by performing a dot multiplication of the current spectrum data and its complex conjugate vector;

[0016] Performing an inverse fast Fourier transform on the current power spectrum density to obtain a current autocorrelation function;

[0017] The square sum of all autocorrelation functions in the time window is calculated to obtain the current autocorrelation square sum of each time window.

[0018] Optionally, the current autocorrelation function is:

[0019] ;

[0020] In the formula, For the The time window The autocorrelation function, Current spectrum data Middle Spectrum information at each location, Current spectrum data The number of positions, is the complex conjugate function;

[0021] The current autocorrelation sum of squares is:

[0022] ;

[0023] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The number of autocorrelation functions in the time window.

[0024] Optionally, the arc fault characteristic quantity is:

[0025] ;

[0026] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The arc fault characteristic quantity of a time window.

[0027] In a second aspect, the present invention provides a distributed photovoltaic system DC arc fault identification device, comprising:

[0028] A data acquisition module is configured to perform non-overlapping sliding window sampling on the arc current of the target line to generate current data of each time window;

[0029] An autocorrelation calculation module is configured to calculate the current autocorrelation square sum of each time window according to the current data of each time window;

[0030] The feature calculation module is configured to calculate the t The current autocorrelation square sum of each time window is normalized as the standard value, and the arc fault characteristic quantity of each time window is calculated;

[0031] The fault identification module is configured to n There are m If the arc fault characteristic quantity in more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists.

[0032] In a third aspect, the present invention provides an electronic device, including a processor and a storage medium;

[0033] The storage medium is used to store instructions;

[0034] The processor is used to operate according to the instructions to execute the steps according to the above method.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0036] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention provides a method and device for identifying DC arc faults in a distributed photovoltaic system. The identification method proposes an autocorrelation function as a characteristic parameter for realizing the identification of DC series arcs in a distributed photovoltaic system; at the same time, the autocorrelation function is normalized by energy normalization to improve the universality and detection effect of the characteristic parameters; through comparative analysis of the characteristic parameters, accurate and efficient DC arc fault identification is realized to ensure the safe operation of the distributed photovoltaic system. In addition, non-overlapping sliding window sampling is adopted for the arc current, and the energy normalized autocorrelation function of each time window is calculated with the time window as the minimum unit. The DC series arc fault is identified by the difference between the normalized autocorrelation function in the normal and arcing processes, which is suitable for distributed photovoltaic systems. The identification method can achieve the corresponding technical effects by adopting the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of a flow chart of a method for identifying DC arc faults in a distributed photovoltaic system provided by an embodiment of the present invention;

[0040] Figure 2 It is a schematic diagram of a flow chart of calculating the current autocorrelation square sum of each time window according to the current data of each time window provided by an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of arc current-current autocorrelation square sum comparison provided by the embodiment of the present invention Figure 1 ;

[0042] Figure 4 Schematic diagram of arc current-current autocorrelation square sum comparison provided by the embodiment of the present invention Figure 2 ;

[0043] Figure 5 Schematic diagram of arc current-current autocorrelation square sum comparison provided by the embodiment of the present invention Figure 3 . DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0045] Embodiment 1:

[0046] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying DC arc faults in a distributed photovoltaic system, comprising the following steps:

[0047] Step S1: Perform non-overlapping sliding window sampling on the arc current of the target line to generate current data of each time window.

[0048] Specifically, during execution, after the non-overlapping sliding window sampling obtains the current data of each time window, it enters the interruption process, performs DC arc fault identification during the interruption process, and samples the current data of the next time window at the same time, thereby realizing continuous identification and continuous sampling.

[0049] Step S2: Calculate the current autocorrelation square sum of each time window according to the current data of each time window.

[0050] Before calculating the current autocorrelation square sum of each time window, the method also includes: performing bandpass filtering on the current data to filter out DC signals and high-frequency signals to facilitate subsequent correlation calculations.

[0051] like Figure 2 As shown, the process of calculating the current autocorrelation square sum of each time window according to the current data of each time window includes:

[0052] Step S2.1: Perform fast Fourier transform (FFT) on the current data of each time window to obtain the corresponding current spectrum data. , current spectrum data is a vector sequence containing frequency information.

[0053] Step S2.2: Current spectrum data The dot product of its complex conjugate vector gives the current power spectrum density.

[0054] Step S2.3, performing inverse fast Fourier transform (iFFT) on the current power spectrum density to obtain the current autocorrelation function.

[0055] The expression of the current autocorrelation function is:

[0056] ;

[0057] In the formula, For the The time window The autocorrelation function, Current spectrum data Middle Spectrum information at each location, Current spectrum data The number of positions, is the complex conjugate function;

[0058] Step S2.4: Perform a square sum operation on all autocorrelation functions in the time window to obtain the current autocorrelation square sum of each time window.

[0059] The expression of the current autocorrelation sum of squares is:

[0060] ;

[0061] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The number of autocorrelation functions in the time window.

[0062] Step S3: t The sum of squares of the current autocorrelation in each time window is normalized as a standard value, and the arc fault characteristic value of each time window is calculated.

[0063] The expression of arc fault characteristic quantity is:

[0064] ;

[0065] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The arc fault characteristic quantity of the time window, is a constant parameter.

[0066] Step S4: If continuous n There are m If the arc fault characteristic quantity in more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists.

[0067] Specifically in this embodiment, by constructing a length n Array Y To store the discrimination data, each time a discrimination is made, , ; For array Y Middle element value, so that the new element value is added to the array Y When, the array Y Shift left to push out the leftmost element value to ensure that the length is always n .

[0068] The element value is determined according to the arc fault characteristic quantity and characteristic threshold. If the current judgment is ,but , ,but , in the process of continuous sampling and calculation, the array Y The element value in is continuously updated. If there is a value greater than or equal to m 1, it is determined that there is an arc fault. Among them, n, m All are constant parameters.

[0069] like Figure 3 , Figure 4 , Figure 5 As shown in the figure, the above algorithm can be used to obtain the current autocorrelation sum of squares at different times for the arc current in the actual circuit. After an arc occurs in the loop, the current will suddenly decrease, and then slowly decrease and tend to be flat. Compared with the normal situation, when an arc occurs in the loop, the autocorrelation function value increases, and the autocorrelation sum of squares will increase, which can meet the threshold. If m of the consecutive n time windows exceed the threshold, it is determined that an arc has occurred. If there is no arc in the loop, but only interference signals, although the sum of squares of the autocorrelation function increases, there will not be m of the consecutive n time windows that exceed the threshold, so there will be no false alarms, which meets the detection requirements of DC arc faults in distributed photovoltaic systems.

[0070] Embodiment 2:

[0071] An embodiment of the present invention provides a distributed photovoltaic system DC arc fault identification device, comprising:

[0072] The data acquisition module is configured to perform non-overlapping sliding window sampling on the arc current of the target line to generate current data of each time window.

[0073] The autocorrelation calculation module is configured to calculate the current autocorrelation square sum of each time window according to the current data of each time window. The current autocorrelation square sum of each time window is calculated according to the current data of each time window.

[0074] Before calculating the current autocorrelation square sum of each time window, the method also includes: performing bandpass filtering on the current data to filter out DC signals and high-frequency signals to facilitate subsequent correlation calculations.

[0075] The process of calculating the current autocorrelation square sum of each time window according to the current data of each time window includes:

[0076] The feature calculation module is configured to calculate the t The sum of squares of the current autocorrelation in each time window is normalized as a standard value, and the arc fault characteristic value of each time window is calculated.

[0077] The fault identification module is configured to n There are m If the arc fault characteristic quantity in more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists.

[0078] The current autocorrelation square sum of each time window is calculated according to the current data of each time window.

[0079] Before calculating the current autocorrelation square sum of each time window, the method also includes: performing bandpass filtering on the current data to filter out DC signals and high-frequency signals to facilitate subsequent correlation calculations.

[0080] The process of calculating the current autocorrelation square sum of each time window according to the current data of each time window includes:

[0081] Step S2.1: Perform fast Fourier transform (FFT) on the current data of each time window to obtain the corresponding current spectrum data. , current spectrum data is a vector sequence containing frequency information.

[0082] Step S2.2: Current spectrum data The dot product of its complex conjugate vector gives the current power spectrum density.

[0083] Step S2.3, performing inverse fast Fourier transform (iFFT) on the current power spectrum density to obtain the current autocorrelation function.

[0084] The expression of the current autocorrelation function is:

[0085] ;

[0086] In the formula, For the The time window The autocorrelation function, Current spectrum data Middle Spectrum information at each location, Current spectrum data The number of positions, is the complex conjugate function;

[0087] Step S2.4: Perform a square sum operation on all autocorrelation functions in the time window to obtain the current autocorrelation square sum of each time window.

[0088] The expression of the current autocorrelation sum of squares is:

[0089] ;

[0090] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The number of autocorrelation functions in the time window.

[0091] Step S3: t The sum of squares of the current autocorrelation in each time window is normalized as a standard value, and the arc fault characteristic value of each time window is calculated.

[0092] The expression of arc fault characteristic quantity is:

[0093] ;

[0094] In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The arc fault characteristic quantity of the time window, is a constant parameter.

[0095] Embodiment three:

[0096] Based on the distributed photovoltaic system DC arc fault identification method provided in the first embodiment, the embodiment of the present invention provides an electronic device, including a processor and a storage medium;

[0097] The storage medium is used to store instructions;

[0098] The processor is used to operate according to the instructions to execute the steps according to the above method.

[0099] Embodiment 4:

[0100] Based on the distributed photovoltaic system DC arc fault identification method provided in Example 1, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented.

[0101] Embodiment five:

[0102] Based on the distributed photovoltaic system DC arc fault identification method provided in Example 1, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0103] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying DC arc faults in a distributed photovoltaic system, characterized in that: include: Perform non-overlapping sliding window sampling on the arc current of the target line to generate current data for each time window; Calculate the current autocorrelation square sum of each time window according to the current data of each time window; Before each time window t The current autocorrelation square sum of each time window is normalized as the standard value, and the arc fault characteristic quantity of each time window is calculated; If continuous n There are m If the arc fault characteristic quantity of more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists; in, t, n, m All are constant parameters; The step of calculating the current autocorrelation square sum of each time window according to the current data of each time window includes: Performing a fast Fourier transform on the current data of each time window to obtain corresponding current spectrum data, wherein the current spectrum data is a vector sequence containing frequency information; The current power spectrum density is obtained by performing a dot multiplication of the current spectrum data and its complex conjugate vector; The current power spectrum density is subjected to an inverse fast Fourier transform to obtain a current autocorrelation function; the current autocorrelation function is: ； In the formula, For the The time window The autocorrelation function, Current spectrum data Middle Spectrum information at each location, Current spectrum data The number of positions, is the complex conjugate function; All autocorrelation functions in the time window are squared and calculated to obtain the current autocorrelation square sum of each time window; the current autocorrelation square sum is: ； In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The number of autocorrelation functions in the time window; Wherein, the arc fault characteristic quantity is: ； In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The arc fault characteristic quantity of a time window.

2. The method for identifying DC arc faults in a distributed photovoltaic system according to claim 1, characterized in that: After acquiring the current data of each time window, the non-overlapping sliding window sampling enters an interruption process, performs DC arc fault identification during the interruption process, and samples the current data of the next time window at the same time.

3. The method for identifying DC arc faults in a distributed photovoltaic system according to claim 1, characterized in that: Before calculating the current autocorrelation square sum of each time window, the method further includes: performing bandpass filtering on the current data to filter out direct current signals and high-frequency signals.

4. A distributed photovoltaic system DC arc fault identification device, characterized in that: include: A data acquisition module is configured to perform non-overlapping sliding window sampling on the arc current of the target line to generate current data of each time window; The autocorrelation calculation module is configured to calculate the current autocorrelation square sum of each time window according to the current data of each time window; specifically includes: Performing a fast Fourier transform on the current data of each time window to obtain corresponding current spectrum data, wherein the current spectrum data is a vector sequence containing frequency information; The current power spectrum density is obtained by performing a dot multiplication of the current spectrum data and its complex conjugate vector; The current power spectrum density is subjected to an inverse fast Fourier transform to obtain a current autocorrelation function; the current autocorrelation function is: ； In the formula, For the The time window The autocorrelation function, Current spectrum data Middle Spectrum information at each location, Current spectrum data The number of positions, is the complex conjugate function; All autocorrelation functions in the time window are squared and calculated to obtain the current autocorrelation square sum of each time window; the current autocorrelation square sum is: ； In the formula, For the The sum of squares of the current autocorrelations in time windows is For the The number of autocorrelation functions in the time window; The feature calculation module is configured to calculate the t The current autocorrelation square sum of each time window is normalized as a standard value, and the arc fault characteristic quantity of each time window is calculated; the arc fault characteristic quantity is: ； In the formula, For the The sum of squares of the current autocorrelations in time windows is For the Arc fault characteristic quantity of a time window; The fault identification module is configured to n There are m If the arc fault characteristic quantity in more than one time window exceeds the characteristic threshold, it is determined that an arc fault exists.

5. An electronic device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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