CN116046796A - Photovoltaic module hot spot detection method and system based on unmanned aerial vehicle - Google Patents
Photovoltaic module hot spot detection method and system based on unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a photovoltaic module hot spot detection method based on an unmanned aerial vehicle, which comprises the following steps: collecting multispectral images of a photovoltaic power station; performing defect identification on the acquired multispectral image and obtaining defect positioning information; the recorded defects are classified and tracked according to the severity of the defect. The unmanned aerial vehicle-based photovoltaic module hot spot detection method provided by the invention effectively solves the problems of low detection efficiency and complex algorithm of the photovoltaic module hot spot detection, and simultaneously selects to adopt an unmanned aerial vehicle-mounted infrared thermal imager to collect infrared images of a photovoltaic power station in order to save the hot spot detection cost and improve the accuracy and the real-time performance of the hot spot detection, and provides a high-precision segmentation algorithm based on local gray scale characteristics of a photovoltaic array region and a hot spot detection method based on a support vector machine, so that a better detection effect is obtained.
Description
Technical Field
The invention relates to the technical field of operation of power grids and power systems, in particular to a photovoltaic module hot spot detection method and system based on an unmanned aerial vehicle.
Background
The method for detecting the hot spots of the photovoltaic module is generally adopted at present in the modes of electrical characteristic monitoring and infrared image analysis.
The detection method based on electrical characteristic monitoring mainly comprises the steps of building a physical circuit on the periphery of a photovoltaic module or a photovoltaic array, collecting output parameters (output voltage, output current and the like) of the photovoltaic module or the photovoltaic array in real time, and analyzing the circuit output characteristics by utilizing a mathematical statistical model or machine learning (a neural network, a fuzzy clustering algorithm and the like), so that the photovoltaic module or the photovoltaic array with the hot spot fault is detected. The method is simple in implementation principle, but for a large photovoltaic power station, thousands or even tens of thousands of sensors are needed to complete data acquisition, so that the cost is high, and the detection efficiency is reduced along with the increase of the scale of the photovoltaic power station.
The detection method based on image analysis is mainly based on the fact that the infrared image can reflect the temperature distribution condition of the solar cell in different working states, and hot spot detection is achieved through machine learning or an image processing method. In the detection method based on image analysis, the detection methods generally adopted are divided into the following modes:
(1) The method for detecting the photovoltaic hot spot faults based on the fuzzy clustering algorithm combines the fuzzy clustering and two-dimensional histogram segmentation modes, so that the photovoltaic array area with faults is preferentially segmented, then the temperature characteristics of all areas are extracted, and meanwhile, the detection operation is completed by means of membership functions.
(2) The maximum divergence threshold difference method is used as a basis to provide a photovoltaic hot spot fault detection method, and the self-adaptive threshold segmentation operation is carried out on the reinforced photovoltaic array infrared image, so that hot spot detection is realized.
(3) According to the photovoltaic array hot spot detection method based on Canny edge detection, a photovoltaic array with possible hot spots is extracted in advance by using Canny edge detection, and then the distribution condition of a temperature histogram of the photovoltaic array area is counted to realize hot spot detection.
(4) The hot spot detection method based on an SLIC (simple iterative clustering algorithm) algorithm is used for improving the accuracy of hot spot detection, a photovoltaic array area is divided into a plurality of sub-areas according to temperature characteristics by using the SLIC algorithm, and the temperature and area characteristics of each sub-area are extracted and counted, so that the hot spot detection is realized.
The detection methods (1) and (2) have obvious effects at present, but in the process of processing the infrared image of the photovoltaic array, the method can only process the condition without any background environment interference, and if the background of the infrared image of the photovoltaic power station is quite complex, the method cannot be directly used. (3) In contrast to the method of the present invention, the detection efficiency is significantly higher, but the background in the infrared image of the photovoltaic power station still needs to be manually separated in advance, and the photovoltaic array area is reserved, so that the degree of automation is not high. The detection method of (4) can process the infrared image at any time, but is still in a simulation stage at present, and has low practicality and high cost.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing photovoltaic module hot spot detection method has the problems of low detection efficiency, complex algorithm, low practicality and higher cost, and the optimization problem of how to accurately detect under a complex background environment.
In order to solve the technical problems, the invention provides the following technical scheme: a photovoltaic module hot spot detection method based on an unmanned aerial vehicle comprises the following steps:
collecting multispectral images of a photovoltaic power station;
performing defect identification on the acquired multispectral image and obtaining defect positioning information;
the recorded defects are classified and tracked according to the severity of the defect.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection method, the invention comprises the following steps: the method for collecting the multispectral image of the photovoltaic power station comprises the following steps: and carrying out image shooting by using the multi-rotor unmanned aerial vehicle to carry a cradle head camera.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection method, the invention comprises the following steps: the pan-tilt camera includes: visible light pan-tilt camera, infrared thermal imaging pan-tilt camera, near infrared imaging pan-tilt camera.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection method, the invention comprises the following steps: the multispectral image comprises:
the visible light image contains visible defect information of the component, and is acquired by a visible light cradle head camera, wherein the acquisition is carried out in cloudy or cloudy days;
the thermal infrared image comprises thermal infrared information of the component, is used for identifying whether the component has a problem of hot spots affecting safe production, is acquired by a thermal infrared cradle head camera, is acquired when irradiance is more than or equal to 600W/m, and is considered to generate hot spots when the temperature right above a battery on the outer surface of the same component exceeds 20 ℃;
the EL image is used for judging the hidden crack defect of the battery piece of the component, and when direct current voltage approximately equal to the open circuit voltage of the group string is applied to the two ends of the group string under the conditions of low illumination and darkness, the near infrared cradle head camera can be used for completing shooting of the hidden crack image of the group string.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection method, the invention comprises the following steps: the defect identification step comprises the following steps:
curve evolution using active contour model based on global gray statistics and contour representation using level set by matching
Is minimized;
performing manual identification and algorithm automatic identification, wherein the algorithm automatic identification is performed based on image morphological characteristics, an countermeasure generation network and a deep learning image identification algorithm;
and quantifying the defect severity of each part of the power transmission line.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection method, the invention comprises the following steps: the classifying and tracking the recorded defects according to the severity of the defects comprises:
classifying the recorded defects according to the severity of the defects;
for major defects, immediately alarming and arranging operation and maintenance personnel to go to field for treatment;
for non-major defects, tracking processing is adopted, manual inspection data in the past of a photovoltaic power station is combined with the occurrence times of non-major defects and the response time of operation and maintenance personnel, inspection times are set to be 5 times in an unmanned plane control background, defect development trends are recorded, the nature of the defects is judged, and component defect processing instruction suggestions are formed in long-term tracking, such as processing instructions are sent for defect severity, and the operation and maintenance personnel are guided to overhaul quickly.
An optimization system for a voice feedback menu of an electric power customer service system, comprising: an information acquisition module, a wireless transmission module and an upper computer server,
the information acquisition module is used for acquiring the operation voltage and current of the photovoltaic panel, the temperature of the photovoltaic panel and the illumination intensity of the environment;
the wireless transmission module is used for gathering data and transmitting the data to the upper computer server;
the upper computer server is used for data analysis and processing, and displaying and background storing on the interface.
As a preferable scheme of the unmanned aerial vehicle-based photovoltaic module hot spot detection system, the invention comprises the following steps: the upper computer server further comprises: the system comprises a server, a background processing module, a display module and a database module;
the information acquisition module also comprises a plurality of acquisition nodes, a router and a coordinator,
the multi-acquisition node is used for data acquisition and comprises a temperature sensor, a photosensitive sensor and a power supply circuit;
the router is used for transmitting data acquired by the multiple acquisition nodes;
the coordinator is used for collecting and summarizing the acquired data transmitted by the router, and transmitting the data to the upper computer server through the RS232 serial port.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method as described above.
The invention has the beneficial effects that: the unmanned aerial vehicle-based photovoltaic module hot spot detection method provided by the invention effectively solves the problems of low detection efficiency and complex algorithm of the photovoltaic module hot spot detection, and simultaneously selects to adopt an unmanned aerial vehicle-mounted infrared thermal imager to collect infrared images of a photovoltaic power station in order to save the hot spot detection cost and improve the accuracy and the real-time performance of the hot spot detection, and provides a high-precision segmentation algorithm based on local gray scale characteristics of a photovoltaic array region and a hot spot detection method based on a support vector machine, so that a better detection effect is obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a method for detecting hot spots of a photovoltaic module based on an unmanned aerial vehicle according to a first embodiment of the present invention;
fig. 2 is an energy function diagram of a photovoltaic module hot spot detection method based on an unmanned aerial vehicle according to a first embodiment of the present invention; fig. 3 is a schematic diagram of a system result of a photovoltaic module hot spot detection system based on an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 4 is a system overall block diagram of a photovoltaic module hot spot detection system based on an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 5 is a functional architecture diagram of a photovoltaic module hot spot detection system based on an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 6 is a software system architecture diagram of a photovoltaic module hot spot detection system based on an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 7 is a graph of solar irradiance, ambient temperature and photovoltaic module back panel temperature of a photovoltaic module thermal spot detection system based on an unmanned aerial vehicle according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a method for detecting hot spots of a photovoltaic module based on an unmanned aerial vehicle, including:
s1: collecting multispectral images of a photovoltaic power station;
furthermore, the multi-rotor unmanned aerial vehicle is used for carrying a tripod head camera for image shooting.
It should be noted that, many rotor unmanned aerial vehicle is a special unmanned helicopter with three or more rotor shafts, and the operability is strong, but vertical take-off and land and hover, mainly is applicable to low altitude, low-speed, have the task type that vertical take-off and land and hover required, is the first choice of present civilian unmanned aerial vehicle, can be applied to photovoltaic power plant's daily inspection and defect investigation well.
Furthermore, in order to reduce the influence on the imaging device in the unmanned aerial vehicle movement process, the imaging device is generally required to be installed on a cradle head, and the cradle head has the rotation performance of horizontal and pitching. The cradle head camera comprises: visible light pan-tilt camera, infrared thermal imaging pan-tilt camera, near infrared imaging pan-tilt camera. The visible light camera is used for shooting visible light images of the photovoltaic module, and can identify the conditions of cracking, fouling and the like of the module; a thermal infrared camera may be used to identify hot spots of the component; the near infrared camera can be used for shooting an EL image of the component and identifying the hidden crack condition of the battery piece of the photovoltaic component.
Furthermore, in the process of the unmanned aerial vehicle for inspection flight operation, when the conventional method is adopted for inspection, the shooting is performed in an equal-time and equal-distance mode, and when the optimized planning method is adopted, the shooting is performed at each navigation point. The photographed image includes multispectral images such as visible light images, thermal infrared images, EL images, and the like, specifically:
the visible light image contains visible defect information of the component, such as appearance defects including broken grid lines of bent and deformed photovoltaic cells, color changes, snail lines and the like, and is acquired by a visible light tripod head camera, and the acquisition is carried out on cloudy or overcast days, so that the effect of shooting is prevented from being influenced by reflected light intensity;
the thermal infrared image comprises thermal infrared information of the component, is used for identifying whether the component has a problem of hot spots affecting safe production, can judge the running conditions of a battery, a bypass diode, a junction box, a welding strip, a connector and the like, is acquired by a thermal infrared cradle head camera, is carried out when irradiance is more than or equal to 600W/m, and is regarded as hot spots when the temperature right above the battery on the outer surface of the same component exceeds 20 ℃;
the EL image is used for judging the hidden crack defect of the battery piece of the component, and when direct current voltage approximately equal to the open circuit voltage of the group string is applied to the two ends of the group string under the conditions of low illumination and darkness, the near infrared cradle head camera can be used for completing shooting of the hidden crack image of the group string.
S2: performing defect identification on the acquired multispectral image and obtaining defect positioning information;
and performing curve evolution by using an active contour model based on global gray statistical information and representing the contour by using a level set. The segmentation process is divided into multiple stages by a neighborhood substitution algorithm through a strategy of continuously changing and shrinking the segmentation area. The advantage of this algorithm is that the work can be done automatically without manual intervention. The whole segmentation process consists of two phases t1 and t2, which respectively represent the iteration times of the corresponding phases.
By aligning
As shown in fig. 2. It can be seen that in phase 1, when the number of iterations t1=41, the energy reaches steady state, the curve converges to the outer boundary of the object; in stage 2, the segmented target is limited to the inner area of the final curve in the previous stage, so that the number of iterations is smaller; when t2=29, the energy reaches steady state, and the whole segmentation process ends for a total of 70 iterations, taking only 7.64s.
Further, the defect recognition method comprises manual recognition and automatic algorithm recognition, wherein the automatic algorithm recognition can be performed based on image recognition algorithms such as image morphological characteristics, countermeasure generation network, deep learning and the like. And respectively recording the identification results of the spectral images, respectively recording defects including hot spots, breakage, hidden cracks and the like, simultaneously recording longitude and latitude information of the shot images, and comprehensively obtaining centimeter-level component defect positioning information.
Further, the defect severity of each part of the power transmission line is quantified, specifically:
the transmission line is composed of various parts, the transmission line image data collected by the unmanned aerial vehicle is divided into a plurality of parts according to different equipment types, wherein the parts comprise a wire, an insulator, a hardware fitting, a pole tower, a ground wire, a lightning arrester and a damper. And recording specific information of the defects which are already generated on the line, and dividing and recording the severity level of the defects (such as the inclination of a pole tower, sag of a wire and the like) according to the state quantity conditions of various types of defects. According to the corresponding regulations, the defects occurring in the equipment are managed in three levels, and are classified into critical defects, serious defects and general defects according to the severity of the defects from high to low.
S3: the recorded defects are classified and tracked according to the severity of the defect.
Further, the recorded defects are classified according to the severity of the defects;
for major defects, immediately alarming and arranging operation and maintenance personnel to go to field for treatment;
for non-major defects, tracking processing is adopted, manual inspection data in the past of a photovoltaic power station is combined with the occurrence times of non-major defects and the response time of operation and maintenance personnel, inspection times are set to be 5 times in an unmanned plane control background, defect development trends are recorded, the nature of the defects is judged, and component defect processing instruction suggestions are formed in long-term tracking, such as processing instructions are sent for defect severity, and the operation and maintenance personnel are guided to overhaul quickly.
Example 2
Referring to fig. 3-6, for one embodiment of the present invention, a photovoltaic module hot spot detection system based on an unmanned aerial vehicle is provided, and the image is segmented by using photovoltaic panel image data collected by an infrared thermal imager of the unmanned aerial vehicle, where the travel defect data includes: an information acquisition module 100, a wireless transmission module 200 and an upper computer server 300,
the information acquisition module 100 is used for acquiring the operation voltage and current of the photovoltaic panel, the temperature of the photovoltaic panel and the illumination intensity of the environment;
the wireless transmission module 200 is used for gathering data and transmitting the data to the upper computer server 300;
the upper computer server 300 is used for data analysis and processing, and displaying and storing in the background on the interface.
Still further, the information acquisition module 100 further includes a multi-acquisition node 101, a router 102 and a coordinator 103,
the multi-acquisition node 101 is used for data acquisition and comprises a temperature sensor, a photosensitive sensor and a power supply circuit;
the router 102 is used for data transmission acquired by the multi-acquisition node 101;
the coordinator 103 is used for collecting and summarizing the collected data transmitted by the router 102, and transmitting the data to the upper computer server 300 through an RS232 serial port;
the multiple acquisition nodes 101 are ZigBee nodes, and the connection between the nodes is net type.
Further, the upper computer server 300 further includes: server 301, background processing module 302, display module 303, and database module 304.
As shown in fig. 4, the system frame design is performed based on the method of the above embodiment. The bottom layer acquisition part can be realized by means of a sensor module, the requirement of the data transmission part can be realized by means of a wireless sensor network communication function, and the design requirement of the upper computer software part is realized by means of program design. Comprehensively considers the differences of all functions and implementation modes.
As shown in fig. 4, a certain number of nodes are firstly placed at the corresponding photovoltaic cell assemblies, and the nodes are carried with temperature sensors and photosensitive sensors, so that stable and continuous electric energy is required to be provided for the nodes. And then the wireless sensor network technology is used for collecting and transmitting the data, then the TCP/IP communication is used, the collected data is received through a server of the upper computer, the data is processed through a background of the server, the data is stored in a local database after being analyzed, and the data is transmitted to a display interface for display, so that a worker can intuitively know the running state of the photovoltaic module, and the photovoltaic module is convenient to maintain in time when faults occur.
The fault monitoring system of the embodiment is composed of ZigBee nodes and an upper computer. The ZigBee node comprises an acquisition node (comprising a terminal node and a router) arranged on the photovoltaic module and a coordinator connected with the upper computer through a serial port. In order to improve the reliability of data transmission and reduce the power consumption of the system, the ZigBee wireless network is configured as a network in the embodiment. The acquisition node is powered by the photovoltaic module through the power supply circuit module, the node transmits the acquired data such as module voltage and current, backboard temperature and the like to the coordinator in a multi-hop mode through the network, and the coordinator transmits the data to the upper computer through the RS232 serial port. The upper computer adopts LabVIEW development and has the functions of analyzing data, displaying data, storing data, managing users, communicating through serial ports, diagnosing faults and the like. Various data are stored in an Access database.
As shown in fig. 5, according to the operation requirement of the photovoltaic power station, the software is subjected to data transmission, storage, calculation, monitoring and fault diagnosis, and the basic functional structure comprises:
(1) Data processing
And through data acquisition, receiving real-time operation information of the photovoltaic power station acquired by the inverter and information transmitted by the sensor, and sending a control signal to the system through downlink communication. The acquired information is visually processed, so that an operator can quickly master the real-time dynamic state of the system.
(2) On-line monitoring
And under the condition that the normal operation of the photovoltaic power station is not affected, monitoring all technical parameters acquired by the system in real time.
(3) Fault diagnosis
And after the acquired information is calculated, the acquired information is interactively compared with the standard data of the index, and the working state of the index is analyzed. In the analysis, the normal indexes are compared, and after the fault state is determined, the fault information is classified and arranged.
(4) Recording and recall of events
And (5) recording index parameters of the photovoltaic power station in the normal operation process, equipment operation states and setting fault levels. Historical events can be invoked by way of extraction. And predicting the expected event according to the running characteristics of the record induction of the historical data.
(5) Alarm function
When the operation of the photovoltaic power station fails, the type of the failure is prompted according to a failure diagnosis program, and failure processing response is stimulated.
In the function realization process, data acquisition and transmission, interface switching, data integration and data processing are required. In particular to an intelligent recognition and processing process for data in a complex application environment.
(1) Due to the instability of the input data, the necessary technical processing of the input data is required.
(2) Mutation of short-term parameters. The input parameters are collected under the influence of short-time weather, such as clouds, short-time thunderstorms and the like, are limited by the frequency of the collected signals, and the output data is distorted due to the hysteresis of the collection. Therefore, the abrupt change signals are analyzed and eliminated in the acquired signals, and the output accuracy is ensured.
(3) Interference signals in an electromagnetic environment. Besides adding anti-interference measures of hardware equipment, the data terminal needs to be identified and eliminated due to nonlinear errors introduced by a remote control sensing device. The intelligent recognition function at the data processing end can eliminate error signals in time for the trend of the prejudged data.
(4) And (5) storing and extracting the quantity. The real-time data of the photovoltaic power station relate to twenty or more parameters, the quantity is large, and the data should be collected and stored in time. And extracting corresponding data according to different output port requirements.
(5) And (5) calculating collected data. By diagnostic classification of the acquired signals. And (5) completing feedback of various output parameters by calculation.
(6) And (5) fault diagnosis. By integrating the data content, the preset index of the power station is combined. And calculating all the output indexes and historical data, and analyzing the development trend and development track of the power station. The fault phenomenon is identified through an algorithm, and the fault is diagnosed.
(7) And (5) data processing. The data processing comprises the steps of acquisition, identification and calculation, and meets the requirements of inquiry and statistics. And outputting parameters representing the operation indexes of the power station after calculating the necessary parameter items.
(8) And a human-computer interaction interface. The system is set to be used by persons having ordinary operation and maintenance capabilities.
As shown in fig. 6, the software completes the interaction between external data and internal data through the efficient internal communication protocol under the centralized configuration of the data interaction center, and processes the output of the data. The data acquisition is used for completing the communication tasks with the inverter and the remote control sensor, and is an external input port of the whole system. And the data acquisition adopts an active scanning mode, and is uploaded to a data interaction center through received data decoding and verification. The data interaction center calculates the collected data, analyzes the data and stores the data into a system database, and correspondingly outputs the data to the interface after the data is classified and processed. The operator can call the past data recorded in the history database through the interaction center, and send out an operation instruction to be transmitted to the system configuration, so as to finish the remote control operation of the equipment.
All or part of each module in the photovoltaic module hot spot monitoring system based on the unmanned aerial vehicle can be realized through software, hardware and combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The computer device may be a server, the computer device comprising a processor, a memory, input/Output interfaces (I/O for short) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data cluster data of the power monitoring system. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, is used for realizing the photovoltaic module hot spot monitoring method based on the unmanned aerial vehicle.
Example 3
Referring to fig. 7, for one embodiment of the present invention, a method for detecting hot spots of a photovoltaic module based on an unmanned aerial vehicle is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation/comparison experiments.
Firstly, aiming at the method in the embodiment, the method is applied to a Guangdong photovoltaic power station to carry out an unmanned aerial vehicle photovoltaic panel hot spot detection experiment, and the detection is carried out when the solar irradiance is more than 700W/m 2 The detection in the power station is performed at this time, covering all photovoltaic modules. Average value of difference value between hot spot temperature of all hot spot photovoltaic modules and central temperature of corresponding photovoltaic modules; the dividing standard of the hot spot photovoltaic module is defined by CNCA/CTS 0016-2015 technical specification of grid-connected photovoltaic power station performance detection and quality evaluation, and the hot spot photovoltaic module is regarded as the hot spot photovoltaic module when the temperature difference in the same photovoltaic module is more than 20 ℃.
The test selects a photovoltaic power station as a distributed photovoltaic power station, selects a roof and a roof as experimental objects, has a loading capacity of 10MWp, installs polycrystalline silicon single-sided photovoltaic modules with nominal power of 270Wp, and has installation inclination angles of 15 degrees (tiling along the roof); the height of the lowest point of the photovoltaic module in the roof is about 25m from the ground, the height of the lowest point of the photovoltaic module in the roof is about 3.5m from the ground, and the detection time is 11 months. The solar irradiance on the day of detection is monitored to ensure that the detection is performed after the solar irradiance is greater than 700W/m 2. The solar irradiance, the ambient temperature and the photovoltaic module backboard temperature curve of the solar photovoltaic power station are shown in fig. 7, and specific data are shown in the following table:
from the table above, it can be seen that: although the average temperature difference of the hot spots in the 2 areas is relatively close and is slightly higher than 20 ℃, the average temperature of the hot spots in the roof area is obviously higher and reaches 75.5 ℃, which is caused by the fact that the temperature of the back plate of the photovoltaic module in the photovoltaic power station is generally higher; the duty ratio of the hot spot photovoltaic module in the roof is 0.16%, which is obviously higher than 0.09% of the roof of the car shed. It can be seen from the above that: the hot spot effect of the roof is more remarkable than that of a shed, and the hot spot is more likely to occur in the photovoltaic power station and the temperature of the hot spot is higher.
Compared with the traditional mode of manually inspecting the high-altitude camera, the detection accuracy of the hot spots of the photovoltaic module based on the unmanned aerial vehicle is improved by 50%, the labor cost is saved by about 50 ten thousand yuan/year, the power generation efficiency is improved by more than 10%, and the new benefit of the average year is about 100 ten thousand yuan.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A photovoltaic module hot spot detection method based on an unmanned aerial vehicle is characterized by comprising the following steps:
collecting multispectral images of a photovoltaic power station;
performing defect identification on the acquired multispectral image and obtaining defect positioning information;
the recorded defects are classified and tracked according to the severity of the defect.
2. The unmanned aerial vehicle-based photovoltaic module hot spot detection method according to claim 1, wherein the method comprises the following steps: the method for collecting the multispectral image of the photovoltaic power station comprises the following steps: and carrying out image shooting by using the multi-rotor unmanned aerial vehicle to carry a cradle head camera.
3. The unmanned aerial vehicle-based photovoltaic module hot spot detection method according to claim 1 or 2, wherein: the pan-tilt camera includes: visible light pan-tilt camera, infrared thermal imaging pan-tilt camera, near infrared imaging pan-tilt camera.
4. The unmanned aerial vehicle-based photovoltaic module hot spot detection method according to any one of claims 1 to 3, wherein: the multispectral image comprises:
the visible light image contains visible defect information of the component, and is acquired by a visible light cradle head camera, wherein the acquisition is carried out in cloudy or cloudy days;
the thermal infrared image comprises thermal infrared information of the component, is used for identifying whether the component has a problem of hot spots affecting safe production, is acquired by a thermal infrared cradle head camera, is acquired when irradiance is more than or equal to 600W/m, and is considered to generate hot spots when the temperature right above a battery on the outer surface of the same component exceeds 20 ℃;
the EL image is used for judging the hidden crack defect of the battery piece of the component, and when direct current voltage approximately equal to the open circuit voltage of the group string is applied to the two ends of the group string under the conditions of low illumination and darkness, the near infrared cradle head camera can be used for completing shooting of the hidden crack image of the group string.
5. The unmanned aerial vehicle-based photovoltaic module hot spot detection method according to claim 4, wherein the method comprises the following steps: the defect identification step comprises the following steps:
curve evolution using active contour model based on global gray statistics and contour representation using level set by matching
Iteration is carried out on the gradient descent flow of the profile curve to enable the profile curve to continuously evolve, so that
Is minimized;
performing manual identification and algorithm automatic identification, wherein the algorithm automatic identification is performed based on image morphological characteristics, an countermeasure generation network and a deep learning image identification algorithm;
and quantifying the defect severity of each part of the power transmission line.
6. The unmanned aerial vehicle-based photovoltaic module hot spot detection method according to claim 5, wherein the method comprises the following steps: the classifying and tracking the recorded defects according to the severity of the defects comprises:
classifying the recorded defects according to the severity of the defects;
for major defects, immediately alarming and arranging operation and maintenance personnel to go to field for treatment;
for non-major defects, tracking processing is adopted, manual inspection data in the past of a photovoltaic power station is combined with the occurrence times of non-major defects and the response time of operation and maintenance personnel, inspection times are set to be 5 times in an unmanned plane control background, defect development trends are recorded, the nature of the defects is judged, and component defect processing instruction suggestions are formed in long-term tracking, such as processing instructions are sent for defect severity, and the operation and maintenance personnel are guided to overhaul quickly.
7. Photovoltaic module hot spot detecting system based on unmanned aerial vehicle, its characterized in that includes: an information acquisition module (100), a wireless transmission module (200) and an upper computer server (300),
the information acquisition module (100) is used for acquiring the operating voltage and current of the photovoltaic panel, the temperature of the photovoltaic panel and the illumination intensity of the environment;
the wireless transmission module (200) is used for gathering data and transmitting the data to the upper computer server (300);
the upper computer server (300) is used for data analysis and processing, and displaying and storing in the background on the interface.
8. The unmanned aerial vehicle-based photovoltaic module hot spot detection system of claim 7, wherein:
the upper computer server (300) further includes: a server (301), a background processing module (302), a display module (303) and a database module (304);
the information acquisition module (100) also comprises a plurality of acquisition nodes (101), a router (102) and a coordinator (103),
the multi-acquisition node (101) is used for data acquisition and comprises a temperature sensor, a photosensitive sensor and a power supply circuit;
the router (102) is used for data transmission collected by the multiple collection nodes (101);
the coordinator (103) is used for collecting and summarizing the acquired data transmitted by the router (102), and transmitting the data to the upper computer server (300) through the RS232 serial port.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN117315350B (en) * | 2023-09-26 | 2024-05-07 | 金开智维(宁夏)科技有限公司 | Hot spot detection method and device for photovoltaic solar panel based on unmanned aerial vehicle |
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