CN113792088A - Fault detection method and device for photovoltaic string and storage medium - Google Patents

Fault detection method and device for photovoltaic string and storage medium Download PDF

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CN113792088A
CN113792088A CN202111062017.6A CN202111062017A CN113792088A CN 113792088 A CN113792088 A CN 113792088A CN 202111062017 A CN202111062017 A CN 202111062017A CN 113792088 A CN113792088 A CN 113792088A
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崔鑫
鲁晨鹏
宋诗
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Sungrow Power Supply Co Ltd
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Abstract

The application discloses a fault detection method and device for a photovoltaic string and a storage medium, relates to the technical field of photovoltaic power generation, and can predict an arc discharge fault of the photovoltaic string, so that potential safety hazards caused by the arc discharge fault can be avoided. The method comprises the following steps: acquiring arc discharge characteristic parameters of each photovoltaic group string in at least two photovoltaic group strings; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string; according to the arc discharge characteristic parameters, determining abnormal photovoltaic string from at least two photovoltaic strings; obtaining a prediction time sequence of arc discharge characteristic parameters of the abnormal photovoltaic group strings after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group strings; and detecting whether the arc discharge fault occurs to the abnormal photovoltaic string or not according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment.

Description

Fault detection method and device for photovoltaic string and storage medium
Technical Field
The embodiment of the application relates to the technical field of photovoltaic power generation, in particular to a method and a device for detecting faults of a photovoltaic string and a storage medium.
Background
With the development of the photovoltaic power generation technology, the working efficiency of the solar cell is gradually improved, and the probability that each branch in the combiner box generates the arc discharge phenomenon is increased. When the arc discharge phenomenon is serious, the photovoltaic string can be heated at high temperature to cause fire, and serious power generation loss and safety hazard are brought to the photovoltaic power station. At present, a training model is generally established according to a machine learning algorithm, detected arc discharge characteristic data of a photovoltaic string is input into the established training model, and then a detection result of whether the photovoltaic string has an arc discharge fault or not is obtained from the training model.
However, the existing detection method for the arc discharge fault is detection after the fault occurs, so that the potential safety hazard caused by the arc discharge fault still exists.
Disclosure of Invention
The application provides a fault detection method, a fault detection device and a storage medium for a photovoltaic string, which can predict an arc discharge fault of the photovoltaic string, so that potential safety hazards caused by the arc discharge fault can be avoided.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for detecting a fault of a photovoltaic string, including: acquiring arc discharge characteristic parameters of each photovoltaic group string in at least two photovoltaic group strings; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string; according to the arc discharge characteristic parameters, determining abnormal photovoltaic string from at least two photovoltaic strings; obtaining a prediction time sequence of arc discharge characteristic parameters of the abnormal photovoltaic group strings after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group strings; and detecting whether the arc discharge fault occurs to the abnormal photovoltaic string or not according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment.
In the technical scheme provided by the application, the arc discharge characteristic parameters can represent the arc discharge degree of each photovoltaic group string, so that the arc discharge characteristic parameters of at least two photovoltaic group strings can be longitudinally compared, and abnormal photovoltaic group strings with possible arc discharge faults in the at least two photovoltaic group strings are determined. Then, the abnormal photovoltaic string with the possibility of arc discharge fault can be further analyzed, a predicted time sequence of a future time period (namely, after the current moment of the application) is deduced through the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string, and the arc discharge characteristic parameters of the abnormal photovoltaic string are monitored in the future time period to obtain an actual time sequence. Because the time sequence can represent the change trend of arc discharge characteristic parameters along with the change of time, the comparison analysis and prediction of the time sequence and the actual time sequence can determine whether the abnormal photovoltaic string develops towards the trend of arc discharge faults, and further determine whether the abnormal photovoltaic string has the possibility of arc discharge faults. Therefore, the technical scheme of the application can predict the arc discharge fault of the photovoltaic string, so that a fault early warning signal can be timely sent out when the arc discharge fault is predicted, and potential safety hazards caused by the arc discharge fault are avoided. In addition, according to the technical scheme, the arc discharge fault of the photovoltaic string is determined by combining longitudinal contrastive analysis (contrastive analysis of at least two photovoltaic string) and transverse analysis (analysis of the time sequence of the abnormal photovoltaic string), the fault detection accuracy of the photovoltaic string can be improved, and therefore the reliability of the detection result is improved.
Optionally, in a possible design manner, the "determining an abnormal photovoltaic group string from at least two photovoltaic group strings according to the arc discharge characteristic parameter" may include:
determining arc discharge characteristic deviation parameters of each photovoltaic group string according to the arc discharge characteristic parameters; the arc discharge characteristic deviation parameter is used for representing the deviation degree of the arc discharge characteristic parameter of the current photovoltaic string and the arc discharge characteristic parameters of other photovoltaic strings; the current photovoltaic string is any one of at least two photovoltaic strings;
and determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter.
Optionally, in another possible design manner, the "determining an arc discharge characteristic deviation parameter of each photovoltaic string according to the arc discharge characteristic parameter" may include:
determining an arc discharge characteristic parameter mean value of at least two photovoltaic group strings according to the arc discharge characteristic parameters;
determining an arc discharge characteristic deviation parameter according to the arc discharge characteristic parameter and the average value of the arc discharge characteristic parameter;
according to the arc discharge characteristic deviation parameter, determining an abnormal photovoltaic string from at least two photovoltaic strings, comprising:
and determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as an abnormal photovoltaic string.
Optionally, in another possible design manner, before obtaining the predicted time sequence of the arc discharge characteristic parameter of the abnormal photovoltaic group string after the current time according to the historical time sequence of the arc discharge characteristic parameter of the abnormal photovoltaic group string, "the method may include:
determining the gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string according to the historical time sequence; the gradient parameter is used for representing the increasing degree of the arc-drawing characteristic parameter in the historical time sequence along with the change of time;
determining that the gradient parameter is greater than or equal to a preset gradient threshold.
Optionally, in another possible design manner, the obtaining a predicted time sequence of arc discharge characteristic parameters of the abnormal photovoltaic string after the current time according to the historical time sequence of arc discharge characteristic parameters of the abnormal photovoltaic string may include:
obtaining a historical time sequence according to arc discharge characteristic parameters of abnormal photovoltaic group strings in X sampling periods in a preset historical time period; x is a positive integer greater than or equal to 2;
determining arc discharge characteristic parameters of abnormal photovoltaic group strings in Y sampling periods after the current moment according to a preset time sequence algorithm and a historical time sequence to obtain a predicted time sequence; y is a positive integer greater than or equal to 1.
Optionally, in another possible design manner, the "detecting whether the abnormal photovoltaic string has the arc discharge fault" may include:
determining sequence deviation parameters of the time sequence and the actual time sequence;
and under the condition that the sequence deviation parameters meet the preset conditions, determining that arc discharge faults occur in the abnormal photovoltaic string.
Optionally, in another possible design manner, the "acquiring an arc discharge characteristic parameter of each of the at least two photovoltaic string" may include:
acquiring a time domain variation curve and a frequency domain variation curve of alternating current components of direct current of each photovoltaic group string in the same sampling period;
extracting arc discharge characteristic data of each photovoltaic group string according to the time domain variation curve and the frequency domain variation curve; the arc discharge characteristic data comprises at least one of an average amplitude, a skewness factor, a kurtosis factor, an average frequency and a frequency root mean square;
and determining arc discharge characteristic parameters according to the arc discharge characteristic data and the preset weight.
In a second aspect, the present application provides a device for detecting a fault in a photovoltaic string, comprising: the device comprises an acquisition module, a determination module and a detection module;
the acquisition module is used for acquiring arc discharge characteristic parameters of each photovoltaic group string in at least two photovoltaic group strings; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string;
the determining module is used for determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic parameters acquired by the acquiring module;
the determining module is further used for obtaining a prediction time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group strings after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group strings;
and the detection module is used for detecting whether the arc discharge fault occurs in the abnormal photovoltaic string or not according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment.
Optionally, in a possible design manner, the determining module is specifically configured to:
determining arc discharge characteristic deviation parameters of each photovoltaic group string according to the arc discharge characteristic parameters; the arc discharge characteristic deviation parameter is used for representing the deviation degree of the arc discharge characteristic parameter of the current photovoltaic string and the arc discharge characteristic parameters of other photovoltaic strings; the current photovoltaic string is any one of at least two photovoltaic strings;
and determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter.
Optionally, in another possible design manner, the determining module is specifically configured to:
determining an arc discharge characteristic parameter mean value of at least two photovoltaic group strings according to the arc discharge characteristic parameters;
determining an arc discharge characteristic deviation parameter according to the arc discharge characteristic parameter and the average value of the arc discharge characteristic parameter;
and determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as an abnormal photovoltaic string.
Optionally, in another possible design, the determining module is further configured to:
determining the gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string according to the historical time sequence; the gradient parameter is used for representing the increasing degree of the arc-drawing characteristic parameter in the historical time sequence along with the change of time;
determining that the gradient parameter is greater than or equal to a preset gradient threshold.
Optionally, in another possible design, the determining module is further configured to:
obtaining a historical time sequence according to arc discharge characteristic parameters of abnormal photovoltaic group strings in X sampling periods in a preset historical time period; x is a positive integer greater than or equal to 2;
determining arc discharge characteristic parameters of abnormal photovoltaic group strings in Y sampling periods after the current moment according to a preset time sequence algorithm and a historical time sequence to obtain a predicted time sequence; y is a positive integer greater than or equal to 1.
Optionally, in another possible design, the detection module is specifically configured to:
determining sequence deviation parameters of the time sequence and the actual time sequence;
and under the condition that the sequence deviation parameters meet the preset conditions, determining that arc discharge faults occur in the abnormal photovoltaic string.
Optionally, in another possible design manner, the obtaining module is specifically configured to:
acquiring a time domain variation curve and a frequency domain variation curve of alternating current components of direct current of each photovoltaic group string in the same sampling period;
extracting arc discharge characteristic data of each photovoltaic group string according to the time domain variation curve and the frequency domain variation curve; the arc discharge characteristic data comprises at least one of an average amplitude, a skewness factor, a kurtosis factor, an average frequency and a frequency root mean square;
and determining arc discharge characteristic parameters according to the arc discharge characteristic data and the preset weight.
In a third aspect, the present application provides a device for detecting a fault of a photovoltaic string, including a memory, a processor, a bus, and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the fault detection device of the photovoltaic string is operating, the processor executes the computer-executable instructions stored in the memory to cause the fault detection device of the photovoltaic string to perform the fault detection method of the photovoltaic string as provided in the first aspect above.
Optionally, the fault detection apparatus for a pv string may further include a transceiver, and the transceiver is configured to perform a step of transceiving data, signaling or information under the control of the processor of the fault detection apparatus for a pv string, for example, to obtain arc discharge characteristic parameters of each of at least two pv strings.
Further optionally, the fault detection device for the photovoltaic string may be a physical machine for implementing fault detection for the photovoltaic string, or may be a part of a device in the physical machine, for example, a system on chip in the physical machine. The system-on-chip is configured to support the fault detection device of the pv string to implement the functions referred to in the first aspect, for example, to receive, transmit or process data and/or information referred to in the fault detection method of the pv string. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a fourth aspect, the present application provides a computer-readable storage medium having instructions stored therein, which when executed by a computer, cause the computer to perform the method for detecting a fault in a string of photovoltaic groups as provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method of fault detection of a string of photovoltaic groups as provided in the first aspect.
It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer-readable storage medium may be packaged with the processor of the fault detection device of the photovoltaic string, or may be packaged separately from the processor of the fault detection device of the photovoltaic string, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the above-mentioned fault detection means of the photovoltaic string do not constitute a limitation on the devices or functional modules themselves, which may appear under other names in practical implementations. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic flowchart of a method for detecting a fault of a photovoltaic string according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another method for detecting a fault of a photovoltaic string according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a further method for detecting a fault of a photovoltaic string according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a further method for detecting a fault of a photovoltaic string according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a further method for detecting a fault of a photovoltaic string according to an embodiment of the present application;
fig. 6 is a schematic flowchart of a further method for detecting a fault of a photovoltaic string according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a fault detection apparatus for a photovoltaic string according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of another fault detection apparatus for a photovoltaic string according to an embodiment of the present application.
Detailed Description
The method, the apparatus and the storage medium for detecting a fault of a photovoltaic string provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
With the development of the photovoltaic power generation technology, the working efficiency of the solar cell is gradually improved, and the probability that each branch in the combiner box generates the arc discharge phenomenon is increased. When the arc discharge phenomenon is serious, the photovoltaic string can be heated at high temperature to cause fire, and serious power generation loss and safety hazard are brought to the photovoltaic power station. At present, a training model is generally established according to a machine learning algorithm, detected arc discharge characteristic data of a photovoltaic string is input into the established training model, and then a detection result of whether the photovoltaic string has an arc discharge fault or not is obtained from the training model.
However, the existing detection method for the arc discharge fault is detection after the fault occurs, so that the potential safety hazard caused by the arc discharge fault still exists.
In view of the problems in the prior art, the embodiments of the present application provide a method for detecting a fault of a photovoltaic string, where arc discharge characteristic parameters of at least two photovoltaic strings are longitudinally compared to determine an abnormal photovoltaic string with a possible arc discharge fault. And then, comparing and analyzing the predicted time sequence and the actual time sequence of the abnormal photovoltaic string to further determine whether the abnormal photovoltaic string has the possibility of arc discharge fault. Therefore, a fault early warning signal can be sent out in time when the arc discharge fault is predicted, and potential safety hazards caused by the arc discharge fault are avoided.
The method for detecting the faults of the photovoltaic string can be suitable for a device for detecting the faults of the photovoltaic string. The fault detection device of the photovoltaic string may be a physical machine (e.g., a server), or may also be a Virtual Machine (VM) deployed on the physical machine. The fault detection device of the photovoltaic group strings is used for monitoring each photovoltaic group string so as to realize fault detection of each photovoltaic group string.
The following describes a method for detecting a fault of a photovoltaic string provided by the present application in detail.
Referring to fig. 1, a method for detecting a fault of a photovoltaic string provided in an embodiment of the present application includes S101 to S104:
s101, a fault detection device of the photovoltaic group strings acquires arc discharge characteristic parameters of each of at least two photovoltaic group strings.
The photovoltaic string can be formed by connecting a certain number of photovoltaic cells with the same specification in series. And the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string.
At present, the photovoltaic string arcing phenomenon mainly has three forms including series arcing, parallel arcing and ground arcing, and because the probability of occurrence of the series arcing is very high and the probability of occurrence of the other two arcing is very low, the method for detecting the faults of the photovoltaic string provided by the embodiment of the application is optional and can be mainly used for detecting the faults of the series arcing. Of course, in practical application, the method and the device can also be applied to detection of other arcing faults, and the method and the device are not limited in the embodiment of the present application.
Optionally, when detecting a series arc discharge fault, the fault detection device of the photovoltaic string may obtain a time domain variation curve and a frequency domain variation curve of an alternating current component of a direct current of each photovoltaic string in the same sampling period; then extracting arc discharge characteristic data of each photovoltaic group string according to the time domain variation curve and the frequency domain variation curve; and then, determining arc discharge characteristic parameters according to the arc discharge characteristic data and the preset weight.
Because the arc discharge fault is mainly reflected in the abnormal change of the direct current side current of the photovoltaic string, whether the arc discharge fault exists in the photovoltaic string can be determined by analyzing the time domain change curve and the frequency domain change curve of the direct current. In addition, since the arc discharge belongs to a noise waveform and the disturbance is an alternating current component, a time domain variation curve and a frequency domain variation curve of the alternating current component of the direct current can be obtained.
For example, a time-domain variation curve of an alternating current component of a direct current of each photovoltaic group string in the same sampling period may be collected from a core device of the photovoltaic power generation system, that is, a photovoltaic inverter, and then Fast Fourier Transform (FFT) may be performed on all the obtained time-domain variation curves to obtain a frequency-domain variation curve corresponding to each photovoltaic group string. The time domain variation curve can correspondingly obtain a group of one-dimensional time sequence arrays, and the frequency domain variation curve can correspondingly obtain a group of frequency spectrum arrays.
In different sampling periods of the same photovoltaic string, the acquisition states of the photovoltaic string during acquisition of the direct current may be different, for example, the acquisition may be performed in a state where the inverter is fully loaded with full rated current, or in a state where the inverter is half loaded with 0.5 times of rated current of the component. Therefore, optionally, after the time sequence array of each photovoltaic group string and the frequency spectrum array of each photovoltaic group string are obtained, normalization processing may be performed on data in the time sequence array and the frequency spectrum array of each photovoltaic group string, respectively.
Illustratively, the data may be normalized using a Z-Score (Z-Score) normalization method. Specifically, the data of the time sequence array of each photovoltaic group string may be normalized according to expression (1), and the data of the frequency spectrum array of each photovoltaic group string may be normalized according to expression (2):
Figure BDA0003257018930000111
Figure BDA0003257018930000112
wherein, TxRepresenting a time sequence array, N being the number of data in the time sequence array, FxAnd representing a spectrum array, wherein M is the number of data in the spectrum array, and x is the xth data in the time sequence array or the spectrum array.
Figure BDA0003257018930000113
Is the average, σ, of all data in the time series arrayTIs the standard deviation of all the data in the time series array,
Figure BDA0003257018930000114
is the average, σ, of all data in the spectral arrayFIs the standard deviation of all data in the spectral array.
Figure BDA0003257018930000115
The time sequence array after the normalization processing is shown,
Figure BDA0003257018930000116
representing the normalized spectral array.
After the time sequence array and the frequency spectrum array of each photovoltaic group string are normalized, arc discharge characteristic data of each photovoltaic group string, such as an average amplitude, a skewness factor, a kurtosis factor, and the like, can be extracted from the time sequence array of each photovoltaic group string. And arc discharge characteristic data of each photovoltaic string, such as average frequency, frequency root mean square and the like, can be extracted from the frequency spectrum array of each photovoltaic string. Then, according to the preset weight of each kind of arc-drawing feature data, the arc-drawing feature parameters can be determined according to expression (3):
Figure BDA0003257018930000117
wherein the content of the first and second substances,
Figure BDA0003257018930000118
representing the average amplitude of the time sequence array, wherein the average amplitude is used for representing the integral energy degree, omega, of the AC component on the DC side1A preset weight representing the average amplitude;
Figure BDA0003257018930000121
representing skewness factors of the time sequence array, wherein the skewness factors are used for representing the deviation degree of the time domain waveform curve from the central axis, omega2A preset weight representing a skewness factor;
Figure BDA0003257018930000122
representing kurtosis factors of the time sequence array, the kurtosis factors being used to characterize the smoothness, omega, of the time domain curve3A preset weight representing a kurtosis factor;
Figure BDA0003257018930000123
representing the average frequency of the spectral array, which is used to characterize the magnitude of the frequency domain curve assignment, ω4A preset weight representing the average frequency;
Figure BDA0003257018930000124
the frequency root mean square of the frequency spectrum array is represented and used for representing the dispersion degree, omega, of the frequency domain curve waveform5A preset weight representing the root mean square of the frequency. The preset weight of each arc discharge characteristic data can be a numerical value determined manually in advance, in practical application, the influence degree of different arc discharge characteristic data on an arc discharge phenomenon can be analyzed through a design experiment, and then the preset weight is determined according to the influence degree obtained through the experiment.
It can be seen that the larger the value of the arc discharge characteristic parameter obtained according to the expression (3), the more obvious the arc discharge characteristic representing the photovoltaic string is, and the higher the possibility of the arc discharge fault is.
S102, the fault detection device of the photovoltaic group strings determines abnormal photovoltaic group strings from at least two photovoltaic group strings according to the arc discharge characteristic parameters.
Optionally, the fault detection device of the photovoltaic string may determine the arc discharge characteristic deviation parameter of each photovoltaic string according to the arc discharge characteristic parameter; and then determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter.
The arc discharge characteristic deviation parameter is used for representing the deviation degree of the arc discharge characteristic parameter of the current photovoltaic string and the arc discharge characteristic parameters of other photovoltaic strings; the current photovoltaic group string is any one of the at least two photovoltaic group strings.
Optionally, the fault detection device of the photovoltaic string may determine an average of arc discharge characteristic parameters of at least two photovoltaic strings according to the arc discharge characteristic parameters; then, determining arc discharge characteristic deviation parameters according to the arc discharge characteristic parameters and the average value of the arc discharge characteristic parameters; and then, determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as an abnormal photovoltaic string.
For example, the arc discharge characteristic deviation parameter of each photovoltaic string may be calculated according to expression (4):
Figure BDA0003257018930000131
wherein R represents the number of the photovoltaic group strings, y represents the y-th photovoltaic group string in the R photovoltaic group strings,
Figure BDA0003257018930000132
representing the mean value, k, of the characteristic parameters of the arc discharge of the R photovoltaic stringsyRepresenting arc characteristic parameter, eta, of the y-th string of photovoltaic modulesyAnd representing the deviation parameter of the arc discharge characteristic of the ith photovoltaic string.
It can be seen that the larger the deviation parameter of the arc discharge characteristic of the photovoltaic string is, the larger the deviation degree of the arc discharge characteristic parameter of the photovoltaic string from the arc discharge characteristic parameters of other photovoltaic strings is. When the arc discharge characteristic deviation parameter of the photovoltaic string exceeds a preset deviation threshold value, the photovoltaic string has obvious abnormal characteristics, and the photovoltaic string can be determined as an abnormal photovoltaic string. The preset deviation threshold may be a parameter determined in advance by a human, which is not limited in the embodiment of the present application.
S103, the fault detection device of the photovoltaic string obtains a prediction time sequence of arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string.
Optionally, in a possible implementation manner, the fault detection device for the photovoltaic string may obtain a historical time sequence according to arc discharge characteristic parameters of the abnormal photovoltaic string in X sampling periods within a preset historical time period, where X is a positive integer greater than or equal to 2; and then, according to a preset time sequence algorithm and a historical time sequence, arc discharge characteristic parameters of the abnormal photovoltaic group strings in Y sampling periods after the current moment are determined, and a predicted time sequence is obtained, wherein Y is a positive integer greater than or equal to 1.
The preset historical time period may be a historical time period before the current time, which is determined in advance by a human. Illustratively, the preset historical time period is a T1 time period, the T1 time period includes X sampling periods, the fault detection device of the photovoltaic string can acquire arc discharge characteristic parameters of the abnormal photovoltaic string in each sampling period of the X sampling periods, and the historical time sequence k is obtained according to the acquired X arc discharge characteristic parameters and corresponding acquisition timei，T1(i＝1～X)。
The preset time-series algorithm may be a time-series algorithm determined in advance by human. For example, the predetermined time series algorithm may be a differential Autoregressive Moving Average algorithm (ARIMA). Of course, in practical application, the preset time series algorithm may also be other time series algorithms, which is not limited in this application embodiment. Based on preset time sequence algorithm and historical time sequence ki，T1(i is 1 to X), a prediction time series of Y sampling periods within a period T2 after the current time can be obtained
Figure BDA0003257018930000141
Optionally, in order to further improve the accuracy of detecting the arcing fault of the photovoltaic string, after the abnormal photovoltaic string is determined, the gradient parameter of the arcing characteristic parameter of the abnormal photovoltaic string may also be determined according to the historical time sequence; and then analyzing the gradient parameters, and further deducing the predicted time sequence under the condition that the gradient parameters are determined to be greater than or equal to a preset gradient threshold value.
The gradient parameter is used for characterizing the growth degree of the arc-pulling characteristic parameter in the historical time sequence along with the change of time, and the gradient parameter can be a first-order difference continuous accumulated value. Specifically, the gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string can be determined according to the expression (5):
Figure BDA0003257018930000142
wherein G is used to denote a gradient parameter, kiRepresenting the ith arc discharge characteristic parameter, k, in the historical time sequencei-1And the (i-1) th arc discharge characteristic parameter in the historical time sequence is shown, and X is the number of sampling periods of the T1 time period.
The preset gradient threshold may be a parameter determined in advance manually, and when it is determined that the gradient parameter exceeds the preset gradient threshold, the arc discharge characteristic parameter of the abnormal photovoltaic string has an obvious increase trend within a time period T1, and has a possibility of evolving into an arc discharge fault, so that the prediction time sequence may be further deduced under the condition that the gradient parameter is determined to be greater than or equal to the preset gradient threshold, thereby verifying the change characteristic of the arc discharge characteristic parameter of the abnormal photovoltaic string for the second time.
And S104, detecting whether the arc discharge fault occurs in the abnormal photovoltaic string by the fault detection device of the photovoltaic string according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameter of the abnormal photovoltaic string after the current moment.
Optionally, the fault detection device of the photovoltaic string may determine a sequence deviation parameter of the time sequence and the actual time sequence; and under the condition that the sequence deviation parameters meet the preset conditions, determining that arc discharge faults occur in the abnormal photovoltaic string.
In a T2 time period after the current moment, the abnormal photovoltaic string can be monitored, arc discharge characteristic parameters of Y sampling periods in the T2 time period are obtained, and an actual time sequence k can be obtained according to the obtained Y arc discharge characteristic parameters and corresponding acquisition timei，T2(i is 1 to Y). The method for obtaining arc discharge characteristic parameters of Y sampling periods within the T2 time period may refer to the foregoing description of obtaining arc discharge characteristic parameters of each of at least two photovoltaic group strings, and is not described herein again.
For example, the sequence deviation parameter may be a predicted timing sequence
Figure BDA0003257018930000151
And the actual time series ki，T2(i is the sum of the deviation values of 1 to Y). Specifically, it can be determined according to expression (6):
Figure BDA0003257018930000152
wherein ε represents a sequence deviation parameter, ki，T2Representing the ith arc discharge characteristic parameter in the actual time sequence,
Figure BDA0003257018930000153
and Y is the number of sampling cycles of the T2 time segment.
For example, the preset condition may be that the sequence deviation parameter exceeds a preset deviation parameter threshold. Under the condition that the sequence deviation parameter exceeds a preset deviation parameter threshold value, the actual arc discharge characteristic change trend of the abnormal photovoltaic string is basically consistent with the prediction trend or even more serious, so that the abnormal photovoltaic string really has an arc discharge risk and can be evolved into an actual arc discharge fault in a short time; on the contrary, when the sequence deviation parameter does not exceed the preset deviation parameter threshold, the actual arc discharge characteristic variation trend of the abnormal photovoltaic string is not continuously serious, no arc discharge risk exists temporarily, and the abnormal photovoltaic string can be continuously monitored and further evaluated. A further method of evaluation may be to predict the predicted timing sequence within the T3 time period after the T2 time period, and determine whether an arcing fault has occurred in the abnormal string of photovoltaic groups based on the predicted timing sequence within the T3 time period and the actual timing sequence within the T3 time period.
Optionally, the fault detection device of the photovoltaic string may send alarm information or display the alarm information when determining that the arc discharge fault occurs in the abnormal photovoltaic string, so as to remind operation and maintenance personnel to troubleshoot the fault in time, thereby avoiding potential safety hazards caused by the arc discharge fault.
In summary, in the method for detecting a fault of a photovoltaic string provided in the embodiment of the present application, since the arc discharge characteristic parameter can represent the arc discharge degree of each photovoltaic string, the arc discharge characteristic parameters of at least two photovoltaic strings can be longitudinally compared, and an abnormal photovoltaic string with a possible arc discharge fault in the at least two photovoltaic strings is determined. Then, the abnormal photovoltaic string with the possibility of arc discharge fault can be further analyzed, a predicted time sequence of a future time period (namely, after the current moment of the application) is deduced through the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string, and the arc discharge characteristic parameters of the abnormal photovoltaic string are monitored in the future time period to obtain an actual time sequence. Because the time sequence can represent the change trend of arc discharge characteristic parameters along with the change of time, the comparison analysis and prediction of the time sequence and the actual time sequence can determine whether the abnormal photovoltaic string develops towards the trend of arc discharge faults, and further determine whether the abnormal photovoltaic string has the possibility of arc discharge faults. Therefore, the technical scheme of the application can predict the arc discharge fault of the photovoltaic string, so that a fault early warning signal can be timely sent out when the arc discharge fault is predicted, and potential safety hazards caused by the arc discharge fault are avoided. In addition, according to the technical scheme, the arc discharge fault of the photovoltaic string is determined by combining longitudinal contrastive analysis (contrastive analysis of at least two photovoltaic string) and transverse analysis (analysis of the time sequence of the abnormal photovoltaic string), the fault detection accuracy of the photovoltaic string can be improved, and therefore the reliability of the detection result is improved.
In summary, as shown in FIG. 2, step S102 in FIG. 1 can be replaced by S1021-S1024:
and S1021, the fault detection device of the photovoltaic string determines the arc discharge characteristic deviation parameter of each photovoltaic string according to the arc discharge characteristic parameters.
S1022, the fault detection device of the photovoltaic string sets the mean value of the arc discharge characteristic parameters of at least two photovoltaic string sets according to the arc discharge characteristic parameters.
And S1023, determining arc discharge characteristic deviation parameters by the fault detection device of the photovoltaic string according to the arc discharge characteristic parameters and the average value of the arc discharge characteristic parameters.
And S1024, determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as an abnormal photovoltaic string by the fault detection device of the photovoltaic string.
Optionally, as shown in fig. 3, before step S103 in fig. 1, the method for detecting a fault of a photovoltaic string provided in the embodiment of the present application may further include S1025 to S1026:
and S1025, the fault detection device of the photovoltaic string determines the gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string according to the historical time sequence.
And S1026, determining that the gradient parameter is greater than or equal to a preset gradient threshold value by the fault detection device of the photovoltaic string.
Alternatively, as shown in fig. 4, step S103 in fig. 1 may be replaced with S1031-S1032:
and S1031, the fault detection device of the photovoltaic group string obtains a historical time sequence according to arc discharge characteristic parameters of the abnormal photovoltaic group string in i sampling periods in a preset historical time period.
S1032, determining arc discharge characteristic parameters of the abnormal photovoltaic string in j sampling periods after the current moment according to a preset time sequence algorithm and a historical time sequence by the fault detection device of the photovoltaic string to obtain a prediction time sequence.
Optionally, as shown in fig. 5, step S104 in fig. 1 may be replaced by S1041-S1042:
s1041, determining sequence deviation parameters of the time sequence and the actual time sequence by the fault detection device of the photovoltaic group string.
S1042, under the condition that the sequence deviation parameters meet the preset conditions, the fault detection device of the photovoltaic string determines that arc discharge faults occur to the abnormal photovoltaic string.
Alternatively, as shown in fig. 6, step S101 in fig. 1 may be replaced with S1011 to S1013:
s1011, the fault detection device of the photovoltaic group strings acquires a time domain variation curve and a frequency domain variation curve of alternating current components of direct current of each photovoltaic group string in the same sampling period.
And S1012, extracting arc discharge characteristic data of each photovoltaic group string by the fault detection device of the photovoltaic group string according to the time domain variation curve and the frequency domain variation curve.
And S1013, determining arc discharge characteristic parameters by the fault detection device of the photovoltaic string according to the arc discharge characteristic data and the preset weight.
As shown in fig. 7, an embodiment of the present application further provides a fault detection apparatus for a photovoltaic string, including: an acquisition module 11, a determination module 12 and a detection module 13.
The obtaining module 11 executes S101 in the above method embodiment, the determining module 12 executes S102 and S103 in the above method embodiment, and the detecting module 13 executes S104 in the above method embodiment.
Specifically, the obtaining module 11 is configured to obtain an arc discharge characteristic parameter of each of at least two photovoltaic string; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string;
the determining module 12 is configured to determine an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic parameter acquired by the acquiring module 11;
the determining module 12 is further configured to obtain a predicted time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current time according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string;
and the detection module 13 is configured to detect whether an arc discharge fault occurs in the abnormal photovoltaic string according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameter of the abnormal photovoltaic string after the current time.
Optionally, in a possible implementation manner, the determining module 12 is specifically configured to:
determining arc discharge characteristic deviation parameters of each photovoltaic group string according to the arc discharge characteristic parameters; the arc discharge characteristic deviation parameter is used for representing the deviation degree of the arc discharge characteristic parameter of the current photovoltaic string and the arc discharge characteristic parameters of other photovoltaic strings; the current photovoltaic string is any one of at least two photovoltaic strings;
and determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter.
Optionally, in another possible implementation manner, the determining module 12 is specifically configured to:
determining an arc discharge characteristic parameter mean value of at least two photovoltaic group strings according to the arc discharge characteristic parameters;
determining an arc discharge characteristic deviation parameter according to the arc discharge characteristic parameter and the average value of the arc discharge characteristic parameter;
according to the arc discharge characteristic deviation parameter, determining an abnormal photovoltaic string from at least two photovoltaic strings, comprising:
and determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as an abnormal photovoltaic string.
Optionally, in another possible implementation manner, the determining module 12 is further configured to:
determining the gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string according to the historical time sequence; the gradient parameter is used for representing the increasing degree of the arc-drawing characteristic parameter in the historical time sequence along with the change of time;
determining that the gradient parameter is greater than or equal to a preset gradient threshold.
Optionally, in another possible implementation manner, the determining module 12 is further configured to:
obtaining a historical time sequence according to arc discharge characteristic parameters of abnormal photovoltaic group strings in X sampling periods in a preset historical time period; x is a positive integer greater than or equal to 2;
determining arc discharge characteristic parameters of abnormal photovoltaic group strings in Y sampling periods after the current moment according to a preset time sequence algorithm and a historical time sequence to obtain a predicted time sequence; y is a positive integer greater than or equal to 1.
Optionally, in another possible implementation manner, the detection module 13 is specifically configured to:
determining sequence deviation parameters of the time sequence and the actual time sequence;
and under the condition that the sequence deviation parameters meet the preset conditions, determining that arc discharge faults occur in the abnormal photovoltaic string.
Optionally, in another possible implementation manner, the obtaining module 11 is specifically configured to:
acquiring a time domain variation curve and a frequency domain variation curve of alternating current components of direct current of each photovoltaic group string in the same sampling period;
extracting arc discharge characteristic data of each photovoltaic group string according to the time domain variation curve and the frequency domain variation curve; the arc discharge characteristic data comprises at least one of an average amplitude, a skewness factor, a kurtosis factor, an average frequency and a frequency root mean square;
and determining arc discharge characteristic parameters according to the arc discharge characteristic data and the preset weight.
Optionally, the fault detection device of the photovoltaic string may further include a storage module, where the storage module is configured to store a program code of the fault detection device of the photovoltaic string, and the like.
As shown in fig. 8, the embodiment of the present application further provides a fault detection apparatus for a photovoltaic string, which includes a memory 41, processors 42(42-1 and 42-2), a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the fault detection device of the photovoltaic string is operating, the processor 42 executes computer-executable instructions stored in the memory 41 to cause the fault detection device of the photovoltaic string to perform the fault detection method of the photovoltaic string as provided in the above embodiments.
In particular implementations, processor 42 may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 8, as one embodiment. And as an example, the fault detection device of the photovoltaic string may include a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 8. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. Processor 42 may perform various functions of the fault detection device of the photovoltaic string by running or executing software programs stored in memory 41, and invoking data stored in memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
As an example, in conjunction with fig. 7, the function implemented by the acquisition module in the failure detection apparatus of the photovoltaic group string is the same as the function implemented by the receiving unit in fig. 8, the function implemented by the detection module in the failure detection apparatus of the photovoltaic group string is the same as the function implemented by the processor in fig. 8, and the function implemented by the storage module in the failure detection apparatus of the photovoltaic group string is the same as the function implemented by the memory in fig. 8.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the method for detecting the fault of the photovoltaic string provided by the above embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method for detecting a fault of a photovoltaic string is characterized by comprising the following steps:
acquiring arc discharge characteristic parameters of each photovoltaic group string in at least two photovoltaic group strings; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string;
according to the arc discharge characteristic parameters, determining abnormal photovoltaic string from the at least two photovoltaic strings;
obtaining a prediction time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group string after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic group string;
and detecting whether the abnormal photovoltaic string has arc discharge faults or not according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment.
2. The method for detecting the fault of the photovoltaic string according to claim 1, wherein the determining an abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic parameter includes:
determining an arc discharge characteristic deviation parameter of each photovoltaic group string according to the arc discharge characteristic parameters; the arc discharge characteristic deviation parameter is used for representing the deviation degree of the arc discharge characteristic parameter of the current photovoltaic string and the arc discharge characteristic parameters of other photovoltaic strings; the current photovoltaic group string is any one of the at least two photovoltaic group strings;
and determining the abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter.
3. The method for detecting faults of photovoltaic string assemblies according to claim 2, wherein the step of determining the arc discharge characteristic deviation parameter of each photovoltaic string assembly according to the arc discharge characteristic parameter comprises the following steps:
determining the mean value of arc discharge characteristic parameters of the at least two photovoltaic string groups according to the arc discharge characteristic parameters;
determining the arc discharge characteristic deviation parameter according to the arc discharge characteristic parameter and the average value of the arc discharge characteristic parameter;
the determining the abnormal photovoltaic string from the at least two photovoltaic strings according to the arc discharge characteristic deviation parameter includes:
and determining the photovoltaic string with the arc discharge characteristic deviation parameter larger than or equal to a preset deviation threshold value as the abnormal photovoltaic string.
4. The method according to claim 1, wherein before obtaining the predicted time series sequence of the arc discharge characteristic parameters of the abnormal pv group string after the current time according to the historical time series sequence of the arc discharge characteristic parameters of the abnormal pv group string, the method further comprises:
determining a gradient parameter of the arc discharge characteristic parameter of the abnormal photovoltaic string according to the historical time sequence; the gradient parameter is used for representing the growth degree of the arc discharge characteristic parameter in the historical time sequence along with the change of time;
determining that the gradient parameter is greater than or equal to a preset gradient threshold.
5. The method according to claim 1, wherein obtaining the predicted time series sequence of the arc discharge characteristic parameters of the abnormal pv group string after the current time according to the historical time series sequence of the arc discharge characteristic parameters of the abnormal pv group string comprises:
obtaining the historical time sequence according to the arc discharge characteristic parameters of the abnormal photovoltaic group strings in X sampling periods in a preset historical time period; x is a positive integer greater than or equal to 2;
determining the arc discharge characteristic parameters of the abnormal photovoltaic group strings in Y sampling periods after the current moment according to a preset time sequence algorithm and the historical time sequence to obtain the predicted time sequence; y is a positive integer greater than or equal to 1.
6. The method for detecting the fault of the photovoltaic string according to claim 1, wherein the detecting whether the abnormal photovoltaic string has the arc discharge fault comprises:
determining a sequence deviation parameter for the timing sequence and the actual timing sequence;
and under the condition that the sequence deviation parameters meet preset conditions, determining that arc discharge faults occur in the abnormal photovoltaic string.
7. The method for detecting faults of photovoltaic strings according to any one of claims 1 to 6, wherein the obtaining of the arc discharge characteristic parameters of each of at least two photovoltaic strings comprises:
acquiring a time domain variation curve and a frequency domain variation curve of alternating current components of direct current of each photovoltaic group string in the same sampling period;
extracting arc discharge characteristic data of each photovoltaic group string according to the time domain variation curve and the frequency domain variation curve; the arc discharge characteristic data comprises at least one of an average amplitude, a skewness factor, a kurtosis factor, an average frequency and a frequency root mean square;
and determining the arc discharge characteristic parameters according to the arc discharge characteristic data and preset weight.
8. A fault detection device of a photovoltaic string, comprising:
the acquisition module is used for acquiring arc discharge characteristic parameters of each photovoltaic group string in at least two photovoltaic group strings; the arc discharge characteristic parameters are used for representing the arc discharge degree of each photovoltaic group string;
the determining module is used for determining an abnormal photovoltaic group string from the at least two photovoltaic group strings according to the arc discharge characteristic parameters acquired by the acquiring module;
the determining module is further configured to obtain a predicted time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment according to the historical time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string;
and the detection module is used for detecting whether the abnormal photovoltaic string has arc discharge faults or not according to the predicted time sequence and the actual time sequence of the arc discharge characteristic parameters of the abnormal photovoltaic string after the current moment.
9. The fault detection device of the photovoltaic string is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the fault detection device of the photovoltaic string is in operation, the processor executes the computer-executable instructions stored in the memory to cause the fault detection device of the photovoltaic string to perform the fault detection method of the photovoltaic string according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored therein instructions, which when executed by a computer, cause the computer to execute the method of fault detection of a string of photovoltaic groups of any one of claims 1 to 7.
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