CN117670300B - Distributed photovoltaic equipment detection operation and maintenance system and method - Google Patents
Distributed photovoltaic equipment detection operation and maintenance system and method Download PDFInfo
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Abstract
The invention relates to the technical field of light Fu Yun dimensions, in particular to a distributed photovoltaic equipment detection operation and maintenance system and method, wherein the method comprises the following steps: collecting operation data of distributed photovoltaic equipment, wherein the operation data comprises photovoltaic module operation data, photovoltaic array operation data and photovoltaic power station operation data; performing multi-level detection on the operation data, wherein the multi-level detection comprises micro-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macro-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results; predicting equipment faults through a neural network model to obtain fault prediction results, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults; and comprehensively analyzing the multi-level detection result and the fault prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report. The invention reduces the dependence of the distributed photovoltaic equipment on the central server and improves the response speed and efficiency of the system.
Description
Technical Field
The invention relates to the technical field of light Fu Yun dimensions, in particular to a distributed photovoltaic equipment detection operation and maintenance system and method.
Background
In recent years, renewable energy sources such as solar energy, wind energy and the like are valued by various communities, and become a new choice for replacing traditional energy sources. Photovoltaic power generation is an important way of solar energy development, has the advantages of no fuel consumption, no pollutant emission, flexible application form, unlimited capacity and scale, and the like, and has very wide application prospect. However, the existing distributed photovoltaic equipment detection operation and maintenance method is seriously dependent on a central server, and the data processing pressure of the central server is high.
For example, in chinese patent publication No. CN115225032a, a distributed photovoltaic operation and maintenance system and an operation and maintenance method are disclosed, which relate to the technical field of photovoltaic systems, and the method comprises the steps of acquiring a current image on a photovoltaic panel, comparing the current image with a preset image to determine a dirty cloud point area, further acquiring current optical power data of the photovoltaic panel, and if the ratio of the sum of the current optical power data and the preset optical power data of all the photovoltaic panels is higher than a critical optical power ratio, controlling a local cleaning device to clean the dirty point area; and if the ratio of the sum of the current light power data and the preset light power data of all the photovoltaic panels is lower than or equal to the critical light power ratio, controlling the global cleaning device to clean all the photovoltaic panels.
The Chinese patent with the publication number of CN109802634B discloses an intelligent operation and maintenance method and an operation and maintenance system of a photovoltaic power station based on big data, which comprise the following steps: s1: acquiring data of each dimension of all photovoltaic power stations in a preset range; s2: dividing and reorganizing the data in each dimension in the S1 to reduce the granularity of the data and carrying out dimension lifting operation on the data; s3: performing association analysis on the data traversal after the dimension rise, and calculating the correlation; s4: extracting a data set with a correlation coefficient not smaller than 0.6 in the step S3; s5: constructing an artificial neural network model; s6: based on the data set of the step S4, predicting the operation and maintenance state of the photovoltaic power station in a preset range in a future time period by adopting the artificial neural network model of the step S5; s7: and generating operation and maintenance suggestions according to the prediction result of the S6 and the operation state of the photovoltaic power station in a preset range.
The problems proposed in the background art exist in the above patents: in the existing distributed photovoltaic equipment detection operation and maintenance method, operation data acquired by a distributed photovoltaic assembly are generally summarized to a central server, and after unified processing is carried out on the data by the central server, instructions are respectively sent to each photovoltaic equipment. Therefore, the data processing pressure of the central server is high, the distributed photovoltaic module does not have autonomous decision making capability, and the running parameters of the distributed photovoltaic module cannot be adjusted in real time according to the running data.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides the distributed photovoltaic equipment detection operation and maintenance system and the method, the photovoltaic equipment detection is divided into micro-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macro-level photovoltaic power station detection by carrying out distributed multi-level detection on the photovoltaic modules, so that the distributed photovoltaic equipment has mesoscopic-level and micro-level autonomous decision-making capability, the dependence of the distributed photovoltaic equipment on a central server is reduced, and the response speed and the response efficiency of the system are improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention provides a distributed photovoltaic equipment detection operation and maintenance method, which comprises the following steps:
collecting operation data of distributed photovoltaic equipment, wherein the operation data comprises photovoltaic module operation data, photovoltaic array operation data and photovoltaic power station operation data;
Performing multi-level detection on the operation data, wherein the multi-level detection comprises micro-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macro-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results;
Predicting equipment faults through a neural network model to obtain fault prediction results, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults;
and comprehensively analyzing the multi-level detection result and the fault prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report.
As a preferable technical scheme, the photovoltaic module operation data comprises a photovoltaic module visible light image, a photovoltaic module infrared image, a photovoltaic module operation current and a photovoltaic module operation voltage.
As the preferable technical scheme, the photovoltaic module detection obtains the photovoltaic module fault through visual imaging detection and power loss analysis of the photovoltaic module, and the photovoltaic module fault comprises module hot spots, module hidden cracks, module fragmentation, shadow shielding and module power attenuation.
As an preferable technical scheme, the photovoltaic array detection analysis photovoltaic array operation data is used for carrying out current-voltage curve diagnosis on the photovoltaic array so as to detect photovoltaic array faults, wherein the photovoltaic array faults comprise short circuit faults, open circuit faults, arc discharge faults, grounding faults and inverter faults.
As a preferred solution, the photovoltaic power plant detection detects photovoltaic power plant faults by analyzing photovoltaic power plant operation data, the photovoltaic power plant faults including transformer faults and grid fluctuations.
As a preferred technical solution, the specific steps of the verification include:
dividing the multi-level detection result into a photovoltaic module detection result, a photovoltaic array detection result and a photovoltaic power station detection result;
When the photovoltaic module is judged to have faults in the photovoltaic module detection result, the photovoltaic module detection result is checked for the first time by the photovoltaic array detection result;
if the detection result of the photovoltaic array is abnormal, checking for the first time, directly sending an operation and maintenance instruction to the photovoltaic module, and generating an operation and maintenance report from the detection result of the photovoltaic module;
if the detection result of the photovoltaic array is abnormal, the first verification is not passed, and the detection result of the photovoltaic power station is verified for the second time;
If the detection result of the photovoltaic power station is abnormal, the second verification passes, an operation and maintenance instruction is directly sent to the photovoltaic module and the photovoltaic array, and an operation and maintenance report is generated by the detection result of the photovoltaic module and the detection result of the photovoltaic array;
If the detection result of the photovoltaic power station is abnormal, the second verification is not passed, and early warning information generated by the detection result of the photovoltaic module, the detection result of the photovoltaic array and the detection result of the photovoltaic power station is sent to operation and maintenance personnel.
As an optimal technical scheme, the neural network model predicts faults of photovoltaic equipment based on a gating circulating neural network, and the gating circulating neural network is represented by the following formula:
rt＝σ(Wirxt+bir+Whrht-1+bhr)；
zt＝σ(Wizxt+biz+Whrht-1+bhz)；
nt＝tanh(Winxt+bin+rt×(Whnht-1+bhn))；
ht＝(1-zt)×nt+zt×ht-1；
yt＝σ(Wyt·ht)；
Wherein W ir、Whr、Wiz、Win、Whn、Wyt denotes a weight matrix, b ir、bhr、biz、bhz、bin、bhn denotes a bias vector, x t denotes an input at time t, h t-1 denotes a hidden state at time t-1, h t denotes a hidden state at time t, r t denotes a reset gate, z t denotes an update gate, n t denotes a candidate hidden gate, σ denotes a Sigmoid activation function, tanh (·) denotes a tanh activation function, and y t denotes a failure prediction result.
As a preferred technical solution, the specific steps of the comprehensive analysis include:
If the photovoltaic module or the photovoltaic array is judged to have faults in the prediction result and the multi-level detection result, an operation and maintenance instruction is directly sent to the photovoltaic module or the photovoltaic array, and an operation and maintenance report is generated from the detection result;
if the prediction result judges that the photovoltaic component or the photovoltaic array fails and the photovoltaic component or the photovoltaic array is not judged to fail in the multi-level detection result, an operation and maintenance report generated by the prediction result is sent to operation and maintenance personnel.
The invention also provides a distributed photovoltaic equipment detection operation and maintenance system, which comprises:
The operation data acquisition module is used for acquiring operation data of the distributed photovoltaic equipment, wherein the operation data comprises operation data of a photovoltaic module, operation data of a photovoltaic array and operation data of a photovoltaic power station;
The multi-level detection module is used for carrying out multi-level detection on the operation data, wherein the multi-level detection comprises microscopic-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macroscopic-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results;
The equipment fault prediction module is used for predicting equipment faults through the neural network model to obtain a fault prediction result, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults;
and the fault analysis module is used for comprehensively analyzing the multi-level detection result and the fault prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report.
A computer readable storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements a distributed photovoltaic device detection operation and maintenance method.
The controller comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for realizing a distributed photovoltaic equipment detection operation and maintenance method when executing the computer program.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) According to the invention, through multi-level detection of the photovoltaic modules, the detection of the photovoltaic equipment is divided into the detection of the photovoltaic modules at the microscopic level, the detection of the photovoltaic array at the mesoscopic level and the detection of the photovoltaic power station at the macroscopic level, so that the distributed photovoltaic equipment has autonomous decision making capability at the mesoscopic level and the microscopic level, the dependence of the distributed photovoltaic equipment on a central server is reduced, and the response speed and the response efficiency of the system are improved.
(2) The invention predicts equipment faults in real time by constructing the neural network model, helps operation and maintenance personnel to take operation and maintenance measures in time, reduces downtime and improves the reliability of the system.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings in which:
fig. 1 is a schematic overall flow diagram of a method for detecting operation and maintenance of a distributed photovoltaic device according to the present invention;
FIG. 2 is a schematic diagram of a flow chart of the verification in the distributed photovoltaic device detection operation and maintenance method of the present invention;
fig. 3 is a schematic structural diagram of a distributed photovoltaic device inspection operation and maintenance system according to the present invention.
Detailed Description
The following detailed description of the present invention is made with reference to the accompanying drawings and specific embodiments, and it is to be understood that the specific features of the embodiments and the embodiments of the present invention are detailed description of the technical solutions of the present invention, and not limited to the technical solutions of the present invention, and that the embodiments and the technical features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the embodiment provides a method for detecting operation and maintenance of a distributed photovoltaic device, which specifically includes the following steps:
S1: collecting operation data of distributed photovoltaic equipment, wherein the operation data comprise photovoltaic module operation data, photovoltaic array operation data and photovoltaic power station operation data;
The photovoltaic module operation data comprise a photovoltaic module visible light image, a photovoltaic module infrared image, a photovoltaic module operation current and a photovoltaic module operation voltage.
S2: performing multi-level detection on the operation data, wherein the multi-level detection comprises micro-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macro-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results;
S21: the method comprises the steps that a photovoltaic module detects and acquires a photovoltaic module fault through visual imaging detection and power loss analysis of the photovoltaic module, wherein the photovoltaic module fault comprises module hot spots, module hidden cracks, module fragmentation, shadow shielding and module power attenuation;
S22: the method comprises the steps that a photovoltaic array detects and analyzes operation data of the photovoltaic array, current-voltage curve diagnosis is conducted on the photovoltaic array, and then faults of the photovoltaic array are detected, wherein the faults of the photovoltaic array comprise short-circuit faults, open-circuit faults, arc-discharge faults, grounding faults and inverter faults;
s23: the method comprises the steps that a photovoltaic power station detects faults of the photovoltaic power station by analyzing operation data of the photovoltaic power station, wherein the faults of the photovoltaic power station comprise transformer faults and power grid fluctuation;
s24: as shown in fig. 2, the specific steps of verification include:
dividing the multi-level detection result into a photovoltaic module detection result, a photovoltaic array detection result and a photovoltaic power station detection result;
When the photovoltaic module is judged to have faults in the photovoltaic module detection result, the photovoltaic module detection result is checked for the first time by the photovoltaic array detection result;
if the detection result of the photovoltaic array is abnormal, checking for the first time, directly sending an operation and maintenance instruction to the photovoltaic module, and generating an operation and maintenance report from the detection result of the photovoltaic module;
if the detection result of the photovoltaic array is abnormal, the first verification is not passed, and the detection result of the photovoltaic power station is verified for the second time;
If the detection result of the photovoltaic power station is abnormal, the second verification passes, an operation and maintenance instruction is directly sent to the photovoltaic module and the photovoltaic array, and an operation and maintenance report is generated by the detection result of the photovoltaic module and the detection result of the photovoltaic array;
If the detection result of the photovoltaic power station is abnormal, the second verification is not passed, and early warning information generated by the detection result of the photovoltaic module, the detection result of the photovoltaic array and the detection result of the photovoltaic power station is sent to operation and maintenance personnel.
S3: predicting equipment faults through a neural network model to obtain fault prediction results, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults;
The neural network model predicts faults of the photovoltaic equipment based on a gating cyclic neural network, and the gating cyclic neural network is represented by the following formula:
rt＝σ(Wirxt+bir+Whrht-1+bhr)；
zt＝σ(Wizxt+biz+Whrht-1+bhz)；
nt＝tanh(Winxt+bin+rt×(Whnht-1+bhn))；
ht＝(1-zt)×nt+zt×ht-1；
yt＝σ(Wyt·ht)；
Wherein W ir、Whr、Wiz、Win、Whn、Wyt denotes a weight matrix, b ir、bhr、biz、bhz、bin、bhn denotes a bias vector, x t denotes an input at time t, h t-1 denotes a hidden state at time t-1, h t denotes a hidden state at time t, r t denotes a reset gate, z t denotes an update gate, n t denotes a candidate hidden gate, σ denotes a Sigmoid activation function, tanh (·) denotes a tanh activation function, and y t denotes a failure prediction result.
S4: comprehensively analyzing the multi-level detection result and the failure prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report;
The specific steps of the comprehensive analysis include:
If the photovoltaic module or the photovoltaic array is judged to have faults in the prediction result and the multi-level detection result, an operation and maintenance instruction is directly sent to the photovoltaic module or the photovoltaic array, and an operation and maintenance report is generated from the detection result;
if the prediction result judges that the photovoltaic component or the photovoltaic array fails and the photovoltaic component or the photovoltaic array is not judged to fail in the multi-level detection result, an operation and maintenance report generated by the prediction result is sent to operation and maintenance personnel.
Example 2
As shown in fig. 3, the present embodiment provides a distributed photovoltaic device detection operation and maintenance system 20, including:
An operation data acquisition module 21, configured to acquire operation data of the distributed photovoltaic device, where the operation data includes operation data of a photovoltaic module, operation data of a photovoltaic array, and operation data of a photovoltaic power station;
The multi-level detection module 22 is configured to perform multi-level detection on the operation data, where the multi-level detection includes micro-level photovoltaic module detection, mesoscopic level photovoltaic array detection, and macro-level photovoltaic power station detection, so as to obtain a multi-level detection result and verify the detection result;
the equipment fault prediction module 23 is used for predicting equipment faults through a neural network model to obtain a fault prediction result, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults;
the fault analysis module 24 is configured to perform comprehensive analysis on the multi-level detection result and the fault prediction result, send an operation and maintenance instruction to the photovoltaic device, and output an operation and maintenance report.
In this embodiment, the operation data collection module 21 is configured to collect operation data of the distributed photovoltaic device, where the operation data includes operation data of a photovoltaic module, operation data of a photovoltaic array, and operation data of a photovoltaic power station, and the operation data of the photovoltaic module includes a visible light image of the photovoltaic module, an infrared image of the photovoltaic module, an operation current of the photovoltaic module, and an operation voltage of the photovoltaic module.
In this embodiment, the multi-level detection module 22 is configured to perform multi-level detection on operation data, where the multi-level detection includes micro-level detection of a photovoltaic module, middle-level detection of a photovoltaic array, and macro-level detection of a photovoltaic power station, so as to obtain a multi-level detection result and verify the detection result, and the photovoltaic module detection obtains a photovoltaic module fault through visual imaging detection and power loss analysis of the photovoltaic module, where the photovoltaic module fault includes module hot spots, module hidden cracks, module cracking, shadow shielding, and module power attenuation;
The method comprises the steps that a photovoltaic array detects and analyzes operation data of the photovoltaic array, current-voltage curve diagnosis is conducted on the photovoltaic array, and then faults of the photovoltaic array are detected, wherein the faults of the photovoltaic array comprise short-circuit faults, open-circuit faults, arc-discharge faults, grounding faults and inverter faults;
The method comprises the steps that a photovoltaic power station detects faults of the photovoltaic power station by analyzing operation data of the photovoltaic power station, wherein the faults of the photovoltaic power station comprise transformer faults and power grid fluctuation;
The specific steps of verification include:
dividing the multi-level detection result into a photovoltaic module detection result, a photovoltaic array detection result and a photovoltaic power station detection result;
When the photovoltaic module is judged to have faults in the photovoltaic module detection result, the photovoltaic module detection result is checked for the first time by the photovoltaic array detection result;
if the detection result of the photovoltaic array is abnormal, checking for the first time, directly sending an operation and maintenance instruction to the photovoltaic module, and generating an operation and maintenance report from the detection result of the photovoltaic module;
if the detection result of the photovoltaic array is abnormal, the first verification is not passed, and the detection result of the photovoltaic power station is verified for the second time;
If the detection result of the photovoltaic power station is abnormal, the second verification passes, an operation and maintenance instruction is directly sent to the photovoltaic module and the photovoltaic array, and an operation and maintenance report is generated by the detection result of the photovoltaic module and the detection result of the photovoltaic array;
If the detection result of the photovoltaic power station is abnormal, the second verification is not passed, and early warning information generated by the detection result of the photovoltaic module, the detection result of the photovoltaic array and the detection result of the photovoltaic power station is sent to operation and maintenance personnel.
In this embodiment, the device fault prediction module 23 is configured to predict a device fault through a neural network model, so as to obtain a fault prediction result, where the device fault includes a photovoltaic module fault and a photovoltaic array fault;
The neural network model predicts faults of the photovoltaic equipment based on a gating cyclic neural network, and the gating cyclic neural network is represented by the following formula:
rt＝σ(Wirxt+bir+Whrht-1+bhr)；
zt＝σ(Wizxt+biz+Whrht-1+bhz)；
nt＝tanh(Winxt+bin+rt×(Whnht-1+bhn))；
ht＝(1-zt)×nt+zt×ht-1；
yt＝σ(Wyt·ht)；
Wherein W ir、Whr、Wiz、Win、Whn、Wyt denotes a weight matrix, b ir、bhr、biz、bhz、bin、bhn denotes a bias vector, x t denotes an input at time t, h t-1 denotes a hidden state at time t-1, h t denotes a hidden state at time t, r t denotes a reset gate, z t denotes an update gate, n t denotes a candidate hidden gate, σ denotes a Sigmoid activation function, tanh (·) denotes a tanh activation function, and y t denotes a failure prediction result.
In this embodiment, the fault analysis module 24 is configured to perform comprehensive analysis on the multi-level detection result and the fault prediction result, send an operation and maintenance instruction to the photovoltaic device, and output an operation and maintenance report;
The specific steps of the comprehensive analysis include:
If the photovoltaic module or the photovoltaic array is judged to have faults in the prediction result and the multi-level detection result, an operation and maintenance instruction is directly sent to the photovoltaic module or the photovoltaic array, and an operation and maintenance report is generated from the detection result;
if the prediction result judges that the photovoltaic component or the photovoltaic array fails and the photovoltaic component or the photovoltaic array is not judged to fail in the multi-level detection result, an operation and maintenance report generated by the prediction result is sent to operation and maintenance personnel.
The steps for implementing the corresponding functions of each parameter and each unit module in the distributed photovoltaic device detection operation and maintenance system according to the present invention may refer to each parameter and step in the embodiment of the distributed photovoltaic device detection operation and maintenance method according to the present invention, which are not described herein.
Example 3
The computer readable storage medium of the embodiment of the invention stores a computer program, and when the computer program is executed by a processor, the method for detecting and operating the distributed photovoltaic equipment is realized. It should be noted that: all computer programs of the distributed photovoltaic equipment detection operation and maintenance method are realized by using a Python language, wherein a multi-level detection module, an equipment fault prediction module and a fault analysis module are controlled by a remote server; the CPU of the remote server is Intel Xeon Gold 5118, the GPU is NVIDIA GTX 2080Ti 11GB, the operating system is Ubuntu 18.04.2, the deep learning framework is PyTorch1.7.0, CUDA version 10.2, and acceleration reasoning is carried out by using cuDNN 7.6.5; intel Xeon Gold 5118 contains a memory and a processor, wherein the memory is used to store a computer program; the processor is configured to execute the computer program to cause Intel Xeon Gold 5118 to perform a distributed photovoltaic device inspection operation and maintenance method.
Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product.
Accordingly, the present disclosure may be embodied in the following forms, namely: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. 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 Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, 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.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. The distributed photovoltaic equipment detection operation and maintenance method is characterized by comprising the following steps of:
collecting operation data of distributed photovoltaic equipment, wherein the operation data comprises photovoltaic module operation data, photovoltaic array operation data and photovoltaic power station operation data;
Performing multi-level detection on the operation data, wherein the multi-level detection comprises micro-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macro-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results; the photovoltaic array detection analysis is carried out on the photovoltaic array operation data, current-voltage curve diagnosis is carried out on the photovoltaic array so as to detect photovoltaic array faults, and the photovoltaic array faults comprise short circuit faults, open circuit faults, arc-starting faults, grounding faults and inverter faults; the specific steps of the verification include:
dividing the multi-level detection result into a photovoltaic module detection result, a photovoltaic array detection result and a photovoltaic power station detection result;
When the photovoltaic module is judged to have faults in the photovoltaic module detection result, the photovoltaic module detection result is checked for the first time by the photovoltaic array detection result;
if the detection result of the photovoltaic array is abnormal, checking for the first time, directly sending an operation and maintenance instruction to the photovoltaic module, and generating an operation and maintenance report from the detection result of the photovoltaic module;
if the detection result of the photovoltaic array is abnormal, the first verification is not passed, and the detection result of the photovoltaic power station is verified for the second time;
If the detection result of the photovoltaic power station is abnormal, the second verification passes, an operation and maintenance instruction is directly sent to the photovoltaic module and the photovoltaic array, and an operation and maintenance report is generated by the detection result of the photovoltaic module and the detection result of the photovoltaic array;
If the detection result of the photovoltaic power station is abnormal, the second verification is not passed, and early warning information generated by the detection result of the photovoltaic module, the detection result of the photovoltaic array and the detection result of the photovoltaic power station is sent to operation and maintenance personnel;
Predicting equipment faults through a neural network model to obtain fault prediction results, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults; the neural network model predicts faults of the photovoltaic equipment based on a gating cyclic neural network, and the gating cyclic neural network is represented by the following formula:
rt＝σ(Wirxt+bir+Whrht-1+bhr)；
zt＝σ(Wizxt+biz+Whrht-1+bhz)；
nt＝tanh(Winxt+bin+rt×(Whnht-1+bhn))；
ht＝(1-zt)×nt+zt×ht-1；
yt＝σ(Wyt·ht)；
Wherein W ir、Whr、Wiz、Win、Whn、Wyt denotes a weight matrix, b ir、bhr、biz、bhz、bin、bhn denotes a bias vector, x t denotes an input at time t, h t-1 denotes a hidden state at time t-1, h t denotes a hidden state at time t, r t denotes a reset gate, z t denotes an update gate, n t denotes a candidate hidden gate, σ denotes a Sigmoid activation function, tanh (·) denotes a tanh activation function, and y t denotes a failure prediction result;
Comprehensively analyzing the multi-level detection result and the failure prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report; the specific steps of the comprehensive analysis include:
If the photovoltaic module or the photovoltaic array is judged to have faults in the prediction result and the multi-level detection result, an operation and maintenance instruction is directly sent to the photovoltaic module or the photovoltaic array, and an operation and maintenance report is generated from the detection result;
if the prediction result judges that the photovoltaic component or the photovoltaic array fails and the photovoltaic component or the photovoltaic array is not judged to fail in the multi-level detection result, an operation and maintenance report generated by the prediction result is sent to operation and maintenance personnel.
2. The method for detecting and operating the distributed photovoltaic equipment according to claim 1, wherein the photovoltaic module detection obtains a photovoltaic module fault through visual imaging detection and power loss analysis of the photovoltaic module, and the photovoltaic module fault comprises module hot spots, module hidden cracks, module cracks, shadow shielding and module power attenuation.
3. A distributed photovoltaic apparatus inspection operation and maintenance method according to claim 1 wherein the photovoltaic power plant inspection detects photovoltaic power plant faults by analyzing photovoltaic power plant operational data, the photovoltaic power plant faults including transformer faults and grid fluctuations.
4. A distributed photovoltaic device inspection operation and maintenance system implemented based on the distributed photovoltaic device inspection operation and maintenance method of any of claims 1-3, the system comprising:
The operation data acquisition module is used for acquiring operation data of the distributed photovoltaic equipment, wherein the operation data comprises operation data of a photovoltaic module, operation data of a photovoltaic array and operation data of a photovoltaic power station;
The multi-level detection module is used for carrying out multi-level detection on the operation data, wherein the multi-level detection comprises microscopic-level photovoltaic module detection, mesoscopic-level photovoltaic array detection and macroscopic-level photovoltaic power station detection, so as to obtain multi-level detection results and check the detection results;
The equipment fault prediction module is used for predicting equipment faults through the neural network model to obtain a fault prediction result, wherein the equipment faults comprise photovoltaic module faults and photovoltaic array faults;
and the fault analysis module is used for comprehensively analyzing the multi-level detection result and the fault prediction result, sending an operation and maintenance instruction to the photovoltaic equipment and outputting an operation and maintenance report.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a distributed photovoltaic device detection operation and maintenance method according to any of claims 1-3.
6. A controller comprising a memory for storing a computer program and a processor for implementing a distributed photovoltaic device detection operation method according to any of claims 1-3 when the computer program is executed.
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