Frequency Band Selection Method and Device, Electronic Device, Computer-Readable Storage Medium

Abstract

The present invention discloses a frequency band selection method and apparatus, an electronic device, and a computer-readable storage medium. The frequency band selection method includes: obtaining a frequency domain signal of an alternating current signal for direct current arc detection; dividing a portion of the frequency domain signal from a first frequency to a second frequency into N sub-bands; where N≥2; obtaining arc characteristic values for the N sub-bands respectively and sorting them; selecting an optimal frequency band according to a preset optimization process, where the preset optimization process includes: if the sorting position of the arc characteristic value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determining that the Mth sub-band is the optimal frequency band for arc detection; i≥2, M<N. The present invention can determine the optimal frequency band for arc detection.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of signal processing, and in particular, to a frequency band selection method and apparatus, an electronic device, and a computer-readable storage medium. Background Art

[0002] An arc is a gas discharge phenomenon. In a photovoltaic system or other systems, if an arc occurs and no effective measures are taken for protection, the high temperature generated by the continuous DC arc is extremely likely to cause a fire and result in major safety accidents.

[0003] In existing DC arc detection technologies, extracting frequency domain characteristic signals for processing is the mainstream technology for judging the occurrence of an arc. However, the accuracy of the frequency domain characteristic signals mainly depends on whether the selected frequency band is appropriate. Affected by the interference of the actual operating environment, the characteristics of each frequency band are inconsistent when an arc occurs, and the optimal frequency band may shift during operation in different power station system environments. All of the above will pose great challenges to the arc recognition ability. Therefore, in order to improve the arc detection rate and ensure the safe and reliable operation of the power station system, the problem of selecting the optimal frequency band in frequency domain analysis needs to be solved urgently. Summary of the Invention

[0004] The present invention provides a frequency band selection method and apparatus, an electronic device, and a computer-readable storage medium to determine the optimal frequency band for arc detection.

[0005] In a first aspect, embodiments of the present invention provide a frequency band selection method, including:

[0006] Obtaining a frequency domain signal of an AC signal for DC arc detection;

[0007] Dividing the part of the frequency domain signal from a first frequency to a second frequency into N sub-frequency bands; where N≥2;

[0008] Calculating arc characteristic values for the N sub-frequency bands respectively and sorting them;

[0009] Selecting an optimal frequency band according to a preset optimization process, where the preset optimization process includes: if the sorting position of the arc characteristic value of the Mth sub-frequency band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determining the Mth sub-frequency band as the optimal frequency band for arc detection; i≥2, M<N.

[0010] Optionally, calculating arc characteristic values for the N sub-frequency bands respectively and sorting them includes:

[0011] Calculating arc characteristic values for the N sub-frequency bands respectively and sorting them according to the numerical size.

[0012] Optionally, the preset optimization process further includes:

[0013] If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared to the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the top k sorted from the largest arc eigenvalue, then determine that the Mth sub-band is the optimal band for arc detection; i≥2, M<N.

[0014] Optionally, the obtaining of arc eigenvalues for N sub-bands respectively and sorting them by numerical value includes:

[0015] Sort the arc eigenvalues from largest to smallest;

[0016] The preset optimization process includes:

[0017] If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared to the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the top k, then determine that the Mth sub-band is the optimal band for arc detection;

[0018] Alternatively, the obtaining of arc eigenvalues for N sub-bands respectively and sorting them by numerical value includes:

[0019] Sort the arc eigenvalues from smallest to largest;

[0020] The preset optimization process includes:

[0021] If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared to the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the last k, then determine that the Mth sub-band is the optimal band for arc detection.

[0022] Optionally, the band selection method further includes: If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared to the sorting position in the previous sampling period, and at least one of the sorting positions in i consecutive sampling periods starting from the current sampling period is among the top N-k sorted from the smallest arc eigenvalue, then determine that no arc occurs in the AC signal.

[0023] Optionally, the arc eigenvalue includes one of mean value, root mean square value, variance, and dispersion, or a weighted value of at least two of them.

[0024] Optionally, the obtaining of the frequency-domain signal of the AC signal for DC arc detection includes:

[0025] Obtain the AC signal for DC arc detection;

[0026] Perform Fourier transform, fast Fourier transform or wavelet transform on the AC signal to obtain the frequency domain signal.

[0027] Optionally, the obtaining of the AC signal for DC arc detection includes:

[0028] Obtain an AC signal for DC arc detection from an electrical device;

[0029] Both the first frequency and the second frequency are greater than the fundamental frequency of the switching frequency of the electrical device.

[0030] In a second aspect, an embodiment of the present invention further provides a frequency band selection device, including:

[0031] An acquisition module, configured to acquire a frequency domain signal of an AC signal for DC arc detection;

[0032] A division module, configured to divide the part of the frequency domain signal from the first frequency to the second frequency into N sub-bands; where N≥2;

[0033] A sorting module, configured to calculate and sort arc feature values for each of the N sub-bands;

[0034] A determination module, configured to select an optimal frequency band according to a preset optimization process, where the preset optimization process includes: if the sorting position of the arc feature value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determine that the Mth sub-band is the optimal frequency band for arc detection; i≥2, M<N.

[0035] Optionally, the arc feature value includes one of mean value, root mean square value, variance and dispersion, or a weighted value of at least two of them.

[0036] In a third aspect, an embodiment of the present invention further provides an electronic device, including:

[0037] At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the frequency band selection method described in the first aspect.

[0038] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, characterized in that when the program is executed by a processor, it implements the frequency band selection method described in the first aspect.

[0039] The technical solution of the embodiment of the present invention adopts a frequency band selection method that can dynamically select the sub-band with the largest change in the arc characteristic value and a relatively large numerical value of the arc characteristic value according to the arc characteristic values of different sub-bands as the optimal frequency band for arc detection. Compared with single fixed-frequency band detection and fixed-frequency band detection, it can greatly improve the detection rate of arcs. In addition, due to different sampling periods, the sorting of the arc characteristic values of N sub-bands may change, so that the value of M may be in a dynamic change, that is, the optimal frequency band for arc detection is always in a dynamic change process, and the frequency band with the most obvious arc characteristics can always be selected for accurate detection, thereby improving the anti-interference ability of the frequency band selection method. And because of the dynamic optimization, the anti-interference ability for the switching frequencies of different inverters is also good, that is, the applicability is also good. Brief Description of the Drawings

[0040] Figure 1 It is a flowchart of a frequency band selection method provided by an embodiment of the present invention;

[0041] Figure 2 It is a schematic structural diagram of a power generation system provided by an embodiment of the present invention;

[0042] Figure 3 It is a schematic diagram of the frequency domain signal frequency band division provided by an embodiment of the present invention;

[0043] Figure 4 It is a schematic structural diagram of a frequency band selection device provided by an embodiment of the present invention;

[0044] Figure 5 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed Embodiment

[0045] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that, for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0046] Figure 1 It is a flowchart of a frequency band selection method provided by an embodiment of the present invention. This embodiment is applicable to the situation of detecting direct current arcs. This method can be executed by a frequency band selection device, and specifically includes the following steps:

[0047] Step S101, obtain the frequency domain signal of the alternating current signal for direct current arc detection;

[0048] Exemplarily, Figure 2 It is a schematic structural diagram of a power generation system provided by an embodiment of the present invention. Combining Figure 1 andFigure 2 The power generation system may include a photovoltaic module 201. The photovoltaic module 201 converts light energy into direct current electrical energy, and the inverter 202 converts the direct current signal into an alternating current signal, which is incorporated into the power grid 203 through the live wire L and the neutral wire N, or used by the load 204. The frequency band selection method may be executed by the arc detection device 205. Among them, the arc detection device 205 obtains the frequency domain signal of the direct current signal at the inverter. It may be that the arc detection device 205 detects the direct current signal and performs frequency domain conversion to obtain the frequency domain signal, or it may be that other devices detect the direct current signal and perform frequency domain conversion to obtain the frequency domain signal. This direct current signal may be current, voltage, power, etc., and this embodiment does not make specific limitations on this.

[0049] Step S102: Divide the part of the frequency domain signal from the first frequency to the second frequency into N sub-bands, where N≥2;

[0050] Specifically, Figure 3 is a schematic diagram of the frequency band division of the frequency domain signal provided by the embodiment of the present invention. As Figure 3 shown, the first frequency f0 may be the preset lowest frequency in the frequency domain signal. This lowest frequency may be zero or other values. The second frequency f N is the preset highest frequency in the frequency domain signal. This highest frequency may be the detected highest frequency or the preset highest frequency; The part between the first frequency f0 and the second frequency f N is evenly divided into N sub-bands, that is, the 1st sub-band, the 2nd sub-band, …… the Nth sub-band; The part between the first frequency and the second frequency may be evenly divided into N sub-bands. At this time, the bandwidths of the N sub-bands are the same. Of course, it may also not be evenly divided into N sub-bands.

[0051] Step S103: Calculate the arc characteristic values for each of the N sub-bands and sort them;

[0052] Specifically, the arc characteristic value of each sub-band may include the statistical characteristics of the frequency domain signal in this sub-band. The frequency domain signal of the direct current signal may be continuously obtained within consecutive sampling periods, and the arc characteristic values are calculated for each of the N sub-bands, and the arc characteristic values corresponding to each sub-band in each sampling period are sorted.

[0053] Step S104: Select the optimal frequency band according to a preset optimization process. The preset optimization process includes: If the sorting position of the arc characteristic value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determine that the Mth sub-band is the optimal frequency band for arc detection; i≥2, M<N.

[0054] Specifically, if the sorting position of the arc eigenvalue in the M-th sub-band in the current sampling period changes the most compared to the previous sampling period, for example, the arc eigenvalue of the M-th sub-band ranks first in the current sampling period while it ranked last in the previous period, it indicates that the statistical characteristics of the M-th sub-band change the most, and an arc may have occurred in this sub-band. Additionally, since the arc eigenvalues are obtained and sorted for N sub-bands respectively, and then dynamic optimization is performed based on the sorting results, in different sampling periods of the same system that requires arc detection, or in different systems that require arc detection, the sorting of the arc eigenvalues of the N sub-bands may change dynamically, resulting in the value of M being possibly in a dynamic change. That is, the optimal frequency band for arc detection finally determined is in a dynamic change process for different sampling periods of the same system or for different systems. It can always dynamically select the frequency band with the most obvious arc characteristics for accurate detection, thereby improving the anti-interference ability of the frequency band selection method. Moreover, due to dynamic optimization, the anti-interference ability for the switching frequencies of different inverters is also good, that is, the applicability is also good.

[0055] The technical solution of this embodiment uses a frequency band selection method that can dynamically select the sub-band with the largest change in arc eigenvalue and a relatively large numerical value of the arc eigenvalue as the optimal frequency band for arc detection according to the arc eigenvalues of different sub-bands. Compared with single fixed-frequency band detection and fixed-frequency band detection, it can greatly improve the detection rate of arcs. Additionally, due to different sampling periods, the sorting of the arc eigenvalues of the N sub-bands may change, resulting in the value of M being possibly in a dynamic change. That is, the optimal frequency band for arc detection is always in a dynamic change process, and it can always select the frequency band with the most obvious arc characteristics for accurate detection, thereby improving the anti-interference ability of the frequency band selection method. Moreover, due to dynamic optimization, the anti-interference ability for the switching frequencies of different inverters is also good, that is, the applicability is also good.

[0056] Optionally, obtaining and sorting the arc eigenvalues for N sub-bands respectively includes: obtaining the arc eigenvalues for N sub-bands respectively and sorting them according to the numerical magnitudes.

[0057] Specifically, the numerical magnitude can more intuitively and concisely reflect the characteristics of each sub-band. Therefore, when sorting the arc eigenvalues of the N sub-bands, the sorting can be directly performed using the numerical magnitudes. The calculation method is simple and can also better reflect the arc characteristics of each sub-band.

[0058] Optionally, the preset optimization process further includes: If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the top k sorted from the largest arc eigenvalue, then determine that the Mth sub-band is the optimal band for arc detection; i≥2, M<N.

[0059] Specifically, since an arc is usually a pulse impact with a relatively long duration, it is possible that the change of the arc eigenvalue from the largest to the smallest is not an arc. Therefore, if in i consecutive sampling periods starting from the current sampling period, the arc eigenvalues of the Mth sub-band are all among the top k starting from the largest arc eigenvalue, that is, the difference between the sorting position of the arc eigenvalue of the Mth sub-band and the sorting position of the sub-band with the largest arc eigenvalue is always less than k - 1, it indicates that the arc eigenvalues of the Mth sub-band are relatively large in i consecutive sampling periods, and it can be determined that the probability of an arc occurring in the Mth sub-band is relatively high. Thus, determine that the Mth sub-band is the optimal band for arc detection for subsequent detection, such as further analyzing the Mth sub-band to determine whether an arc has occurred. Compared with single fixed-band detection and fixed-band detection, it can greatly improve the detection rate of arcs.

[0060] Exemplarily, the sorting positions of each sub-band in the current sampling period can be compared with the sorting positions of each sub-band in the previous sampling period in sequence to find the sub-band with the largest change in sorting position.

[0061] Exemplarily, obtaining the arc eigenvalues for each of the N sub-bands and sorting them by numerical value includes:

[0062] Sorting the arc eigenvalues from largest to smallest;

[0063] The preset optimization process includes:

[0064] If the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the top k, then determine that the Mth sub-band is the optimal band for arc detection.

[0065] Specifically, after obtaining the arc characteristic values of each sub-band, they can be sorted in descending order of the arc characteristic values. Preferably, a sorting rank value can be assigned to each sub-band according to the sorting position. For example, when N is 5, the arc characteristic values of the 5 sub-bands are 5, 7, 9, 3, and 6 respectively. Then the sorting rank values of the 5 sub-bands are 4, 2, 1, 5, and 3. In the next sampling period, if the sorting rank values of the 5 sub-bands become 5, 3, 2, 1, and 4, it is determined that the sorting position of the fourth sub-band changes the most. And in the subsequent i sampling periods, if the sorting position of the fourth sub-band is always among the top k, it indicates that the possibility of an arc occurring in the fourth sub-band is the greatest. Thus, the fourth sub-band can be determined as the optimal band. It should be noted that the value of i can be determined according to the length of the sampling period and the duration of the arc occurrence, so as to ensure that the entire process of arc occurrence can be sampled within i periods. The duration of the arc occurrence can be set according to different systems, such as determined according to the empirical values of the arc generation duration of different systems. This embodiment does not make specific limitations in this regard. The value of k can be determined according to the value of N. For example, it can be set that the value of k is less than N / 2, so as to ensure that the arc characteristic values of the selected optimal band are always relatively large.

[0066] In some other embodiments, obtaining the arc characteristic values of N sub-bands respectively and sorting them according to the numerical values includes:

[0067] Sorting the arc characteristic values from small to large;

[0068] The preset optimization process includes:

[0069] If the sorting position of the arc characteristic value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and the sorting positions in the consecutive i sampling periods starting from the current sampling period are all among the last k, then it is determined that the Mth sub-band is the optimal band for arc detection.

[0070] Specifically, after obtaining the arc characteristic values of each sub-band, they can be sorted in ascending order of the arc characteristic values. Preferably, a sorting rank value can be assigned to each sub-band according to the sorting position. For example, when N is 5, the arc characteristic values of the 5 sub-bands are 5, 7, 9, 3, and 6 respectively. Then the sorting rank values of the 5 sub-bands are 2, 4, 5, 1, and 3. In the next sampling period, if the sorting rank values of the 5 sub-bands become 1, 3, 4, 5, and 2, it is determined that the sorting position of the fourth sub-band changes the most. And in the subsequent i sampling periods, if the sorting position of the fourth sub-band is always among the last k, it indicates that the possibility of an arc occurring in the fourth sub-band is the greatest. Thus, the fourth sub-band can be determined as the optimal band.

[0071] Optionally, the frequency band selection method further includes: if the sorting position of the arc eigenvalue of the M-th sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and at least one of the sorting positions in i consecutive sampling periods starting from the current sampling period is among the first N-k sorted from the smallest arc eigenvalue, it is determined that no arc occurs in the AC signal.

[0072] Specifically, although the sorting position of the arc eigenvalue of the M-th sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, since within i consecutive sampling periods, at least one of the sorting positions of the arc eigenvalue of the M-th sub-band is among the first N-k sorted from the smallest arc eigenvalue; in other words, within i consecutive sampling periods, the sorting positions of the arc eigenvalue of the M-th sub-band are not all among the first k sorted from the largest arc eigenvalue (the first k when sorted from large to small, and the last k when sorted from small to large), indicating that the duration of the interference signal added to the AC signal is short, and it may be other noise interference rather than the occurrence of an arc. At this time, it can be judged that no arc occurs in the AC signal, and there is no need to further judge whether an arc occurs.

[0073] Optionally, the arc eigenvalue includes: the weighted value of one or at least two of the mean value, root mean square value, variance, and dispersion.

[0074] Specifically, in some embodiments, one of the mean value, root mean square value, variance, and dispersion can be used as the arc eigenvalue, which has the advantages of less calculation amount and fast calculation speed; in some other embodiments, the weighted value of at least two can also be selected as the arc eigenvalue, and the weights of the mean value, root mean square value, variance, and dispersion can be adjusted according to the specific application scenario. This embodiment does not limit this. Selecting the weighted value of at least two as the arc eigenvalue can further improve the accuracy of the optimal frequency band selection, and thus is beneficial to further improving the detection rate of arc detection.

[0075] Preferably, the value of N can be determined according to the bandwidth between the first frequency and the second frequency, so that there are more discrete frequency points in each sub-band. For example, there are at least 10 discrete frequency points in each sub-band, so as to improve the conformity of the arc eigenvalues of each sub-band.

[0076] Optionally, obtaining the frequency domain signal of the AC signal for DC arc detection includes:

[0077] Obtaining an AC signal for DC arc detection;

[0078] Performing Fourier transform, fast Fourier transform or wavelet transform on the AC signal to obtain the frequency domain signal.

[0079] Specifically, Fourier transform, fast Fourier transform, and wavelet transform can all transform the acquired AC signal from the frequency domain to the time domain, and the fast Fourier transform and wavelet transform also have advantages such as fast calculation speed, further improving the method for the frequency band selection method to find the optimal frequency band; the principles and calculation formulas of Fourier transform, fast Fourier transform, and wavelet transform are well-known to those skilled in the art and will not be elaborated here.

[0080] Optionally, as Figure 2 shown, obtaining the AC signal for DC arc detection includes:

[0081] Obtaining the AC signal for DC arc detection from an electrical device;

[0082] The first frequency and the second frequency are greater than the fundamental frequency of the switching frequency of the electrical device.

[0083] Specifically, in Figure 2 , the arc detection device 205 executes the frequency band selection method, and can obtain the AC signal from the electrical device. The electrical device can be, for example, the inverter 202, and the AC signal can be obtained from the DC side of the inverter 202. The specific obtaining method can be through a magnetic ring or other means; since the switching frequency of the inverter 202 seriously affects the accuracy of arc detection, and arcs generally occur in the high frequency band, the first frequency and the second frequency can be set to be greater than the fundamental frequency of the switching frequency, so as to filter out the influence of the switching frequency of the inverter and greatly improve the accuracy of selecting the optimal frequency band. On the other hand, since the frequency span range of the acquired frequency domain signal is relatively large, it may exist from extremely low frequencies to extremely high frequencies. If the entire frequency band is processed, the calculation amount is large, and the lower frequencies and higher frequencies are basically noise. Therefore, setting the first frequency and the second frequency to be greater than the fundamental frequency of the switching frequency of the inverter can also reduce the calculation amount and speed up the calculation speed.

[0084] The embodiment of the present invention also provides a frequency band selection device, as Figure 4 shown, Figure 4 is a schematic structural diagram of a frequency band selection device provided by an embodiment of the present invention. The frequency band selection device includes: an acquisition module 310, configured to acquire the frequency domain signal of the AC signal for DC arc detection;

[0085] A division module 320, configured to divide the part of the frequency domain signal from the first frequency to the second frequency into N sub-frequency bands; where N≥2;

[0086] A sorting module 330, configured to calculate and sort the arc characteristic values for each of the N sub-frequency bands;

[0087] A determination module 340 is configured to select an optimal frequency band according to a preset optimization process. The preset optimization process includes: if the sorting position of the arc eigenvalue of the Mth sub-frequency band in the current sampling period changes most significantly compared with the sorting position in the previous sampling period, then determine that the Mth sub-frequency band is the optimal frequency band for arc detection; i≥2, M<N.

[0088] The frequency band selection device provided by the embodiments of the present invention can execute the frequency band selection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. The working principle and beneficial effects are not elaborated herein.

[0089] Optionally, the arc eigenvalue includes one or a weighted value of at least two of: mean value, root mean square value, variance, and dispersion.

[0090] Specifically, in some embodiments, one of the mean value, root mean square value, variance, and dispersion can be used as the arc eigenvalue, which has the advantages of less calculation amount and fast calculation speed; in some other embodiments, a weighted value of at least two can also be selected as the arc eigenvalue. The weights of the mean value, root mean square value, variance, and dispersion can be adjusted according to specific application scenarios. This embodiment does not limit this. Selecting a weighted value of at least two as the arc eigenvalue can further improve the accuracy of selecting the optimal frequency band, and thus is beneficial to further improving the detection rate of arc detection.

[0091] Embodiments of the present invention also provide an electronic device. Figure 5 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, as Figure 5 shown. The electronic device includes a processor 61, a memory 71, an input device 72, and an output device 73. The number of processors 70 in the electronic device can be one or more. Figure 5 Here, one processor 70 is taken as an example. The processor 70, memory 71, input device 72, and output device 73 in the electronic device can be connected through a bus or other means. Figure 5 Here, connection through a bus is taken as an example.

[0092] The memory 71, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions corresponding to the frequency band selection method in the embodiments of the present invention. The processor 70 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 71, that is, implements the above-mentioned frequency band selection method.

[0093] The memory 71 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 71 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 71 may further include a memory remotely set relative to the processor 70, and these remote memories may be connected to the device / terminal / server through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0094] The input device 72 may be used to receive input digital or character information and generate key signal inputs related to user settings and function controls of the electronic device. The output device 73 may include display devices such as a display screen.

[0095] An embodiment of the present invention also provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute a frequency band selection method when executed by a computer processor. The method includes:

[0096] Obtain the frequency domain signal of the AC signal for DC arc detection;

[0097] Divide the part of the frequency domain signal from the first frequency to the second frequency into N sub-bands; where N≥2;

[0098] Calculate the arc eigenvalue for each of the N sub-bands and sort them;

[0099] Select the optimal frequency band according to a preset optimization process. The preset optimization process includes: if the sorting position of the arc eigenvalue of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determine that the Mth sub-band is the optimal frequency band for arc detection; i≥2, M<N.

[0100] Of course, the computer-executable instructions of a storage medium provided by an embodiment of the present invention are not limited to the method operations as described above, and can also execute related operations in the frequency band selection methods provided by any embodiment of the present invention.

[0101] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0102] It should be noted that in the embodiments of the above search device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0103] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A frequency band selection method, characterized in that, Including: Obtaining a frequency-domain signal of an alternating-current signal for direct-current arc detection; Dividing the part of the frequency-domain signal from a first frequency to a second frequency into N sub-bands; where N≥2; Respectively obtaining arc feature values for the N sub-bands and sorting them; Selecting an optimal band according to a preset optimization process, where the preset optimization process includes: if the sorting position of the arc feature value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determining the Mth sub-band as the optimal band for arc detection.

2. The frequency band selection method according to claim 1, wherein The respectively obtaining arc feature values for the N sub-bands and sorting them includes: Respectively obtaining arc feature values for the N sub-bands and sorting them according to the numerical magnitude.

3. The frequency band selection method according to claim 1, wherein The preset optimization process further includes: If the sorting position of the arc feature value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and the sorting positions in i consecutive sampling periods starting from the current sampling period are all among the top k sorted from the largest arc feature value, then determining the Mth sub-band as the optimal band for arc detection; i≥2, M<N.

4. The frequency band selection method according to claim 1, wherein The band selection method further includes: if the sorting position of the arc feature value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, and at least one of the sorting positions in i consecutive sampling periods starting from the current sampling period is among the top N-k sorted from the smallest arc feature value, then determining that no arc occurs in the alternating-current signal.

5. The frequency band selection method according to claim 1, wherein The arc feature value includes one of mean value, root-mean-square value, variance and dispersion, or a weighted value of at least two of them.

6. The frequency band selection method according to claim 1, characterized in that The obtaining a frequency-domain signal of an alternating-current signal for direct-current arc detection includes: Obtaining an alternating-current signal for direct-current arc detection; Performing Fourier transform, fast Fourier transform or wavelet transform on the alternating-current signal to obtain the frequency-domain signal.

7. The frequency band selection method according to claim 1, wherein The obtaining an alternating-current signal for direct-current arc detection includes: Obtaining an alternating-current signal for direct-current arc detection from an electrical device; Both the first frequency and the second frequency are greater than the fundamental frequency of the switching frequency of the electrical device.

8. A frequency band selection device, characterized in that Including: An obtaining module, configured to obtain a frequency-domain signal of an alternating-current signal for direct-current arc detection; A dividing module, configured to divide the part of the frequency-domain signal from a first frequency to a second frequency into N sub-bands; where N≥2; A sorting module, configured to respectively obtain arc feature values for the N sub-bands and sort them; A determining module, configured to select an optimal band according to a preset optimization process, where the preset optimization process includes: if the sorting position of the arc feature value of the Mth sub-band in the current sampling period changes the most compared with the sorting position in the previous sampling period, then determining the Mth sub-band as the optimal band for arc detection; i≥2, M<N.

9. The frequency band selection device according to claim 8, wherein The arc feature value includes one of mean value, root-mean-square value, variance and dispersion, or a weighted value of at least two of them.

10. An electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the frequency band selection method according to any one of claims 1-7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the frequency band selection method according to any one of claims 1-7.

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