CN114417996A - Distributed photovoltaic string abnormity detection method and device and electronic equipment - Google Patents
Distributed photovoltaic string abnormity detection method and device and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to the technical field of photovoltaic detection, and provides an abnormity detection method and device for a distributed photovoltaic string and electronic equipment. The method comprises the following steps: acquiring a node volt-ampere characteristic curve of a photovoltaic group string, wherein the photovoltaic group string comprises a plurality of photovoltaic panels which are connected in parallel or in series; and identifying the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference. The utility model discloses a realize the real-time digital monitoring to the node volt-ampere characteristic curve of photovoltaic group cluster to can discern the abnormal state and the unusual type of photovoltaic group cluster fast according to this node volt-ampere characteristic curve, avoid in the photovoltaic application unable in time high-efficient and low-priced to carry out the problem that detects to the photovoltaic.
Description
Technical Field
The present disclosure relates to the field of photovoltaic detection technologies, and in particular, to an anomaly detection method and apparatus for a distributed photovoltaic string, and an electronic device.
Background
Different photovoltaic modules can be tested in the factory, and an IV performance curve (also called a volt-ampere characteristic curve) of the photovoltaic panel in the initial state is mainly tested to be used as a basis for judging the power generation performance of the photovoltaic panel. After the photovoltaic module is put into use, because the operation and maintenance conditions are not allowed, the test work of the IV performance curve of each module is generally ignored, and the abnormal attenuation and other conditions of the module product cannot be found in time. In addition, if a third-party company is hired to detect the product regularly, particularly a distributed photovoltaic system, the photovoltaic maintenance cost of the product is increased. Therefore, how to timely, efficiently and inexpensively detect the volt-ampere characteristic curve of the photovoltaic system to acquire the operation condition of the product is a technical problem of the operation and maintenance of the current distributed photovoltaic system.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide an anomaly detection method and apparatus for a distributed photovoltaic string, and an electronic device, so as to solve the problem in the prior art of how to timely, efficiently and inexpensively perform volt-ampere characteristic curve detection on a photovoltaic system to obtain an operating condition of a product.
In a first aspect of the embodiments of the present disclosure, a method for detecting an anomaly of a distributed photovoltaic string is provided, including: acquiring a node volt-ampere characteristic curve of a photovoltaic group string, wherein the photovoltaic group string comprises a plurality of photovoltaic panels which are connected in parallel or in series; and identifying the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
In a second aspect of the embodiments of the present disclosure, an abnormality detection apparatus for a distributed photovoltaic string is provided, including: the photovoltaic string comprises a plurality of photovoltaic panels which are connected in parallel or in series; and the detection module is configured to identify the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determine the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: through the node volt-ampere characteristic curve who obtains photovoltaic group cluster, photovoltaic group cluster includes a plurality of photovoltaic boards, a plurality of photovoltaic boards are parallelly connected or series connection, discern the difference of node volt-ampere characteristic curve and preset standard volt-ampere characteristic curve, and confirm the unusual type of the photovoltaic group cluster that node volt-ampere characteristic curve corresponds based on the difference, the real-time digital monitoring to the node volt-ampere characteristic curve of photovoltaic group cluster has been realized, and can discern the abnormal state and the unusual type of photovoltaic group cluster fast according to this node volt-ampere characteristic curve, the problem that can't in time high-efficient and detect photovoltaic module low cheaply in the photovoltaic application has been avoided.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
fig. 2 is a flowchart of an anomaly detection method for a distributed photovoltaic string provided in an embodiment of the present disclosure;
fig. 3 is a structural diagram of an abnormality detection apparatus for a distributed photovoltaic string provided in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
An anomaly detection method and an anomaly detection device for a distributed photovoltaic string according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include a photovoltaic system 1, a sensor 2, a gateway 3, a server 4, a terminal device 5, and a network 6.
The photovoltaic system 1 includes a plurality of photovoltaic panels, as shown in fig. 1, there are 12 photovoltaic panels in the photovoltaic system 1, which are respectively: photovoltaic panel 11, photovoltaic panel 12, photovoltaic panel 13, photovoltaic panel 14, photovoltaic panel 21, photovoltaic panel 22, photovoltaic panel 23, photovoltaic panel 24, photovoltaic panel 31, photovoltaic panel 32, photovoltaic panel 33, and photovoltaic panel 34. Therein, the photovoltaic system 1 may be divided into 4 photovoltaic strings, such as the photovoltaic strings 11, 12, 13 and 14 in fig. 1, wherein each photovoltaic string 11, 12, 13 and 14 comprises 3 photovoltaic panels connected in series. It should be noted that, in practical applications, the photovoltaic string may also be a plurality of photovoltaic panels connected in parallel.
The sensors 2 are configured to be installed at nodes of each photovoltaic string to detect node voltage data and node current data at the nodes, for example, one sensor 2 may be provided at each of the nodes of each of the photovoltaic strings 11, 12, 13 and 14 in fig. 1. In particular, the sensor 2 may be any type of current-voltage sensor.
The gateway 3 is configured to be connected to the sensors 2 at the nodes of the photovoltaic strings 11, 12, 13, and 14, respectively, and configured to receive the node voltage data and the node current data detected by the sensors 2, and send the node voltage data and the node current data out through the network 6, for example, as shown in fig. 1, the gateway 3 sends the node voltage data and the node current data to the server 4 or/and the terminal device 5 through the network 6. Specifically, the gateway may include a router, a switch, or other devices.
The server 4 may be a server providing various services, for example, a backend server receiving a request sent by a terminal device establishing a communication connection with the server, and the backend server may receive and analyze the request sent by the terminal device and generate a processing result. The server 4 may be one server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.
The server 4 may be hardware or software. When the server 4 is hardware, it may be various electronic devices that provide various services to the terminal device 5. When the server 4 is software, it may be a plurality of software or software modules for providing various services for the terminal device 5, or may be a single software or software module for providing various services for the terminal device 5, which is not limited in the embodiment of the present disclosure.
The terminal device 5 may be hardware or software. When the terminal device 5 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 4, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the terminal device 5 is software, it may be installed in the electronic device as above. The terminal device 5 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiment of the present disclosure. Further, various applications, such as a data processing application, an instant messaging tool, social platform software, a search-type application, a shopping-type application, and the like, may be installed on the terminal device 5.
The network 6 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.
In practical application, the server 4 may obtain node voltage data and node current data of each node where the photovoltaic system 1 is located through the network 6, obtain a real-time node volt-ampere characteristic curve of each node according to the node voltage data and the node current data, calculate the node volt-ampere characteristic curve to determine a difference between the node volt-ampere characteristic curve and a standard volt-ampere characteristic curve to detect whether an abnormality or/and a corresponding abnormality type exists in the corresponding node, and then send an alarm message according to the abnormality type, or notify a maintenance worker to overhaul a corresponding photovoltaic group string in the photovoltaic system 1, thereby realizing real-time detection of the operating condition of the photovoltaic system 1. In addition, the user may also access the detection results in the server 4 and the node voltage data and the node current data of the photovoltaic system 1, etc. through the network 6 on the terminal device 5.
It should be noted that specific types, numbers, and combinations of the photovoltaic system 1, the sensor 2, the gateway 3, the server 4, the terminal device 5, and the network 6 may be adjusted according to actual requirements of an application scenario, which is not limited in the embodiment of the present disclosure.
Fig. 2 is a flowchart of an abnormality detection method for a distributed photovoltaic string according to an embodiment of the present disclosure. The anomaly detection method for the distributed photovoltaic string of fig. 2 may be executed by the terminal device or the server of fig. 1. As shown in fig. 2, the method for detecting an anomaly of a distributed photovoltaic string includes:
s201, acquiring a node volt-ampere characteristic curve of a photovoltaic group string, wherein the photovoltaic group string comprises a plurality of photovoltaic panels which are connected in parallel or in series;
s202, identifying the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining the abnormal type of the photovoltaic group string corresponding to the node volt-ampere characteristic curve based on the difference.
The working principle of the method for detecting the abnormality of the distributed photovoltaic string provided by the embodiment of the disclosure is as follows: since the initial volt-ampere characteristic curve of the photovoltaic panel is known, a standard volt-ampere characteristic curve of a photovoltaic module formed by connecting a plurality of photovoltaic panels in series or in parallel can be determined, and in the photovoltaic use process, the node volt-ampere characteristic curve of the photovoltaic string is compared with the standard volt-ampere characteristic curve, so that the difference between the node volt-ampere characteristic curve and the standard volt-ampere characteristic curve can be obtained, and the abnormality or/and the abnormal type of the photovoltaic string can be judged by utilizing the difference.
Specifically, the difference between the node volt-ampere characteristic curve and the standard volt-ampere characteristic curve may include a curve slope difference and a curve deformation difference, a threshold range of the difference may be preset, and if the difference is within the threshold range, the photovoltaic string may be considered to be normally operated, and conversely, if the difference is outside the threshold range, the photovoltaic module may be considered to be abnormally operated. In addition, different differences can be classified in advance, and the abnormal type or abnormal reason corresponding to each difference is marked, so that the abnormal type or abnormal reason of the photovoltaic string can be determined according to the differences.
According to the technical scheme provided by the embodiment of the disclosure, through obtaining the node volt-ampere characteristic curve of photovoltaic group cluster, the photovoltaic group cluster includes a plurality of photovoltaic boards, a plurality of photovoltaic boards are parallelly connected or series connection, the difference of node volt-ampere characteristic curve and preset standard volt-ampere characteristic curve is discerned, and the abnormal type of the photovoltaic group cluster corresponding to the node volt-ampere characteristic curve is determined based on the difference, the real-time digital monitoring of the node volt-ampere characteristic curve of the photovoltaic group cluster is realized, the abnormal state and the abnormal type of the photovoltaic group cluster can be rapidly discerned according to the node volt-ampere characteristic curve, and the problem that photovoltaic components cannot be timely, efficiently and inexpensively detected in photovoltaic application is avoided.
In some embodiments, obtaining the node volt-ampere characteristic of the photovoltaic string comprises: acquiring node voltage data and node current data of the photovoltaic string; and generating a corresponding volt-ampere characteristic curve based on the node voltage data and the node current data.
In particular, in the application scenario of fig. 1, node voltage data and node current data may be automatically acquired by providing a power supply and a power supply sensor at a node of a photovoltaic string, so as to generate a corresponding volt-ampere characteristic curve, i.e., a node volt-ampere characteristic curve, according to the node voltage data and the node current data.
In practical application, the technical means for detecting whether the photovoltaic string is abnormal or not and the type and the cause of the abnormality by using the node volt-ampere characteristic curve are not unique, and the following specific implementation modes will be provided in the embodiment of the disclosure.
As an optional implementation manner, in some embodiments, identifying a difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining an abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference includes: inputting the node volt-ampere characteristic curve into a pre-trained characteristic curve abnormity identification model, and obtaining the abnormity type of the photovoltaic string corresponding to the node volt-ampere characteristic curve at the output of the characteristic curve abnormity identification model, wherein the characteristic curve abnormity identification model is a machine learning model obtained by adopting a volt-ampere characteristic deformation curve sample marked with the abnormity type for training in advance.
Specifically, node volt-ampere characteristic curves of different photovoltaic module nodes can be collected, and the node volt-ampere characteristic curves are labeled by comparing abnormal types corresponding to differences between the node volt-ampere characteristic curves and standard volt-ampere characteristic curves of corresponding nodes, so that sample data is formed. The abnormal type corresponding to the abnormal node volt-ampere characteristic curve can be obtained through maintenance experience or existing empirical data and the like. Furthermore, the machine learning model may be embodied as a regression-type algorithm model or a neural network, and the embodiments of the present disclosure are not limited to the specific type of the machine learning model.
With reference to the application scenario of fig. 1, the trained characteristic curve abnormality recognition model may be deployed on the server 4 or the terminal device 5, the server 4 or the terminal device 5 acquires a node volt-ampere characteristic curve of each photovoltaic module node in the photovoltaic system 1, inputs the node volt-ampere characteristic curve into the characteristic curve abnormality recognition model, and then automatically obtains the abnormality type of the photovoltaic module on the output of the characteristic curve abnormality recognition model.
On the basis of the above embodiments, in some embodiments, the anomaly detection method for the distributed photovoltaic string in fig. 2 may further include: acquiring maintenance record information fed back aiming at the abnormal type, wherein the maintenance record information comprises the abnormal type detected by the photovoltaic string and the actual abnormal type of the photovoltaic string; and responding to the maintenance record information, and correcting the characteristic curve abnormity identification model.
Specifically, when detecting that photovoltaic module exists unusually, maintainer can overhaul the photovoltaic module who corresponds, then feeds back maintenance record to the execution main part. In this way, for the actual abnormal situation of the detected photovoltaic module of the abnormal type, if the actual abnormal type of the photovoltaic module is found to be consistent with the detected abnormal type through maintenance, the detection accuracy is very high, and conversely, the node volt-ampere characteristic curve corresponding to the detected abnormal type can be labeled according to the actual abnormal type of the photovoltaic module, so as to determine the abnormal type label of the node volt-ampere characteristic curve.
Thus, in some embodiments, modifying the characteristic curve anomaly identification model in response to service record information includes: comparing the detected abnormal type of the photovoltaic string with the actual abnormal type of the photovoltaic string based on the maintenance record information: if the detected abnormal type is inconsistent with the actual abnormal type, marking a volt-ampere characteristic curve corresponding to the detected abnormal type based on the actual abnormal type to obtain sample data; and training the characteristic curve abnormality recognition model by using the sample data to obtain a corrected characteristic curve abnormality recognition model.
Specifically, whether the detection result of the photovoltaic module is accurate or not is further verified through maintenance record information, and when the actual abnormal type is inconsistent with the detected abnormal type, the actual abnormal type is used for labeling the corresponding node volt-ampere characteristic curve to form sample data, so that the characteristic curve abnormal recognition model is corrected by using the sample data.
As another optional implementation manner, in some embodiments, identifying a difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining an abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference includes: determining a standard volt-ampere characteristic curve of a node where the photovoltaic string is located based on the initial volt-ampere characteristic curve of each photovoltaic panel in the photovoltaic string; based on an image recognition algorithm, recognizing the difference between a node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, wherein the difference comprises a curve slope difference and a curve deformation difference; and determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
Specifically, in addition to the above manner of intelligently identifying the abnormal type of the photovoltaic module by using the machine learning manner, in the embodiment of the present disclosure, the difference between the real-time node volt-ampere characteristic curve of the photovoltaic module node and the standard volt-ampere characteristic curve may be obtained by using an image identification algorithm, and the abnormal type of the photovoltaic module may be quickly identified by using the abnormal types corresponding to the different differences in advance.
In particular, the standard voltage-current characteristic is a combination of the initial voltage-current characteristics of all the photovoltaic panels in the photovoltaic module in which it is located. For example, for a photovoltaic module consisting of a plurality of photovoltaic panels connected in series, the voltage amplitudes of the initial voltage-current characteristic curves of each photovoltaic panel are added; for a photovoltaic module comprising a plurality of photovoltaic panels connected in parallel, the current amplitudes of the initial current-voltage characteristic curves of each photovoltaic panel are added.
In some embodiments, the anomaly detection method for the distributed photovoltaic string in fig. 2 may further include: and responding to the abnormal type, and sending preset alarm information corresponding to the abnormal type.
Specifically, in combination with the application scenario of fig. 1, the preset warning information may be that the warning information is displayed on the server or the terminal device, or the server sends the warning information to the terminal device.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of an abnormality detection apparatus for a distributed photovoltaic string according to an embodiment of the present disclosure. As shown in fig. 3, the abnormality detection apparatus for a distributed photovoltaic string includes:
an obtaining module 301 configured to obtain a node volt-ampere characteristic curve of a photovoltaic string, where the photovoltaic string includes a plurality of photovoltaic panels, and the photovoltaic panels are connected in parallel or in series;
and the detection module 302 is configured to identify a difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determine an abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
According to the technical scheme provided by the embodiment of the disclosure, through obtaining the node volt-ampere characteristic curve of photovoltaic group cluster, the photovoltaic group cluster includes a plurality of photovoltaic boards, a plurality of photovoltaic boards are parallelly connected or series connection, the difference of node volt-ampere characteristic curve and preset standard volt-ampere characteristic curve is identified, and the abnormal type of the photovoltaic group cluster corresponding to the node volt-ampere characteristic curve is determined based on the difference, the real-time digital monitoring of the node volt-ampere characteristic curve of the photovoltaic group cluster is realized, the abnormal state and the abnormal type of the photovoltaic group cluster can be rapidly identified according to the node volt-ampere characteristic curve, and the problem that the photovoltaic can not be timely, efficiently and inexpensively detected in photovoltaic application is avoided.
In some embodiments, the acquisition module 301 in fig. 3 acquires node voltage data and node current data for a string of photovoltaic strings; and generating a corresponding volt-ampere characteristic curve based on the node voltage data and the node current data.
In some embodiments, the detection module 302 in fig. 3 inputs the node volt-ampere characteristic curve into a pre-trained characteristic curve abnormality recognition model, and obtains the abnormality type of the photovoltaic string corresponding to the node volt-ampere characteristic curve from the output of the characteristic curve abnormality recognition model, where the characteristic curve abnormality recognition model is a machine learning model obtained by training in advance by using a volt-ampere characteristic deformation curve sample labeled with the abnormality type.
In some embodiments, the apparatus for detecting an anomaly of a distributed photovoltaic string further includes: a correction module 303 configured to obtain maintenance record information fed back for the anomaly type, where the maintenance record information includes the anomaly type detected for the photovoltaic string and an actual anomaly type of the photovoltaic string; and responding to the maintenance record information, and correcting the characteristic curve abnormity identification model.
In some embodiments, the correction module 303 of fig. 3 compares the detected anomaly type of the photovoltaic string to the actual anomaly type of the photovoltaic string based on the service record information: if the detected abnormal type is inconsistent with the actual abnormal type, marking a volt-ampere characteristic curve corresponding to the detected abnormal type based on the actual abnormal type to obtain sample data; and training the characteristic curve abnormality recognition model by using the sample data to obtain a corrected characteristic curve abnormality recognition model.
In some embodiments, the detection module 302 in fig. 3 determines a standard voltage-current characteristic of a node where the pv string is located based on the initial voltage-current characteristic of each pv panel in the pv string; based on an image recognition algorithm, recognizing the difference between a node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, wherein the difference comprises a curve slope difference and a curve deformation difference; and determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
In some embodiments, the apparatus for detecting an anomaly of a distributed photovoltaic string further includes: and the alarm module 304 is configured to respond to the abnormal type and send out preset alarm information corresponding to the abnormal type.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
Fig. 4 is a schematic diagram of an electronic device 400 provided by an embodiment of the disclosure. The electronic device 400 in fig. 4 may be the server or the terminal device in fig. 1. As shown in fig. 4, the electronic apparatus 400 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps in the various method embodiments described above are implemented when the processor 401 executes the computer program 403. Alternatively, the processor 401 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 403.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 403 in the electronic device 400.
The electronic device 400 may be a desktop computer, a notebook, a palm top computer, a cloud server, or other electronic devices. The electronic device 400 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of an electronic device 400 and does not constitute a limitation of electronic device 400 and may include more or fewer components than shown, or combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the electronic device 400, for example, a hard disk or a memory of the electronic device 400. The memory 402 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 400. Further, the memory 402 may also include both internal storage units and external storage devices of the electronic device 400. The memory 402 is used for storing computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, and multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. An abnormality detection method for a distributed photovoltaic string, comprising:
acquiring a node volt-ampere characteristic curve of a photovoltaic group string, wherein the photovoltaic group string comprises a plurality of photovoltaic panels which are connected in parallel or in series;
and identifying the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determining the abnormal type of the photovoltaic group string corresponding to the node volt-ampere characteristic curve based on the difference.
2. The method of claim 1, wherein obtaining the node volt-ampere characteristic of the photovoltaic string comprises:
acquiring node voltage data and node current data of the photovoltaic string;
and generating a corresponding volt-ampere characteristic curve based on the node voltage data and the node current data.
3. The method of claim 1, wherein the identifying the difference between the node volt-ampere characteristic and a preset standard volt-ampere characteristic, and the determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic based on the difference comprises:
and inputting the node volt-ampere characteristic curve into a pre-trained characteristic curve abnormity identification model, and obtaining the abnormity type of a photovoltaic string corresponding to the node volt-ampere characteristic curve at the output of the characteristic curve abnormity identification model, wherein the characteristic curve abnormity identification model is a machine learning model obtained by adopting a volt-ampere characteristic deformation curve sample marked with the abnormity type for training in advance.
4. The method of claim 3, further comprising:
acquiring maintenance record information fed back aiming at the abnormal type, wherein the maintenance record information comprises the abnormal type detected for the photovoltaic string and the actual abnormal type of the photovoltaic string;
and responding to the maintenance record information, and correcting the characteristic curve abnormity identification model.
5. The method of claim 4, further comprising: and the responding to the maintenance record information and correcting the characteristic curve abnormity identification model comprises the following steps:
based on the maintenance record information, comparing the detected abnormal type of the photovoltaic string with the actual abnormal type of the photovoltaic string:
if the detected abnormal type is inconsistent with the actual abnormal type, marking a volt-ampere characteristic curve corresponding to the detected abnormal type based on the actual abnormal type to obtain sample data;
and training the characteristic curve abnormality recognition model by using the sample data to obtain a corrected characteristic curve abnormality recognition model.
6. The method of claim 1, wherein the identifying the difference between the node volt-ampere characteristic and a preset standard volt-ampere characteristic, and the determining the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic based on the difference comprises:
determining a standard volt-ampere characteristic curve of a node where the photovoltaic group string is located based on the initial volt-ampere characteristic curve of each photovoltaic panel in the photovoltaic group string;
based on an image recognition algorithm, recognizing the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, wherein the difference comprises a curve slope difference and a curve deformation difference;
and determining the abnormal type of the photovoltaic group string corresponding to the node volt-ampere characteristic curve based on the difference.
7. The method according to any one of claims 1 to 6, characterized in that in response to the type of anomaly, preset warning information corresponding to the type of anomaly is issued.
8. An anomaly detection device for a distributed photovoltaic string, comprising:
the photovoltaic array comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire a node volt-ampere characteristic curve of a photovoltaic array string, the photovoltaic array string comprises a plurality of photovoltaic panels, and the photovoltaic panels are connected in parallel or in series;
and the detection module is configured to identify the difference between the node volt-ampere characteristic curve and a preset standard volt-ampere characteristic curve, and determine the abnormal type of the photovoltaic string corresponding to the node volt-ampere characteristic curve based on the difference.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115840920A (en) * | 2022-12-30 | 2023-03-24 | 北京志翔科技股份有限公司 | Photovoltaic group string single-day separable anomaly classification method, device, equipment and storage medium |
| CN116487285A (en) * | 2023-06-14 | 2023-07-25 | 英利能源发展(天津)有限公司 | Photovoltaic module electrical parameter test result correction method and related device |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115840920A (en) * | 2022-12-30 | 2023-03-24 | 北京志翔科技股份有限公司 | Photovoltaic group string single-day separable anomaly classification method, device, equipment and storage medium |
| CN116487285A (en) * | 2023-06-14 | 2023-07-25 | 英利能源发展(天津)有限公司 | Photovoltaic module electrical parameter test result correction method and related device |
| CN116487285B (en) * | 2023-06-14 | 2023-09-05 | 英利能源发展(天津)有限公司 | Photovoltaic module electrical parameter test result correction method and related device |
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