CN111024711A - Photovoltaic module subfissure detection system based on internet of things technology - Google Patents
Photovoltaic module subfissure detection system based on internet of things technology Download PDFInfo
- Publication number
- CN111024711A CN111024711A CN201911330256.8A CN201911330256A CN111024711A CN 111024711 A CN111024711 A CN 111024711A CN 201911330256 A CN201911330256 A CN 201911330256A CN 111024711 A CN111024711 A CN 111024711A
- Authority
- CN
- China
- Prior art keywords
- data
- photovoltaic module
- subfissure
- user
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8896—Circuits specially adapted for system specific signal conditioning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a photovoltaic module subfissure detection system based on the technology of the Internet of things. The system solves the problems that the existing data acquisition system is large in scale, complex in structure, complex in communication and incapable of detecting component subfissure. And the user sends a query instruction through the web page to query the electrical information of the current component. After the background reads the component information, whether the current component is in a healthy state or not can be judged through an electric energy loss algorithm. If the current assembly is not in a healthy state, a user can send a control command, add reverse voltage, shoot a subfissure image and analyze the subfissure image to obtain whether the current assembly has subfissure or not.
Description
Technical Field
The invention belongs to the technical field of subfissure detection systems, and particularly relates to a photovoltaic module subfissure detection system based on the technology of the Internet of things.
Background
The photovoltaic module and the environmental data acquisition control system which are efficient, convenient and easy to use can help a power station and an individual user to realize effective control and management of the photovoltaic module, can easily perform fault diagnosis and analysis of electrical data on the next step, are beneficial to real-time monitoring of employees and users, are easy to maintain daily, can reduce loss of the module to a certain extent, improve economic benefits and reduce cost, and ensure smooth operation of power generation.
With the rising demand for global climate change, the shortage of energy and the growing situation of energy supply safety, new energy sources such as wind energy, solar energy, biomass energy and ocean energy are increasingly positioned in the energy strategy of each country due to the characteristics of cleanness, safety and reproducibility. Among them, solar energy is relatively low in cost, mature in technology and high in reliability, has been developed rapidly in recent years, and plays an important role in energy supply. By the end of 2014, the installed capacity of the national photovoltaic power generation reaches 2905 ten thousand kW, and the solar photovoltaic power generation system becomes the second largest world photovoltaic installed country second to Germany. However, the current photovoltaic power stations and the household photovoltaic power stations have the following problems:
(1) the data of the components are not collected, the collected data are mostly data of the series level of the inverter group, and the specific components are difficult to be positioned through the data of the series level of the inverter group.
(2) Most of the monitoring systems are desktop applications, and for development, one desktop application needs to be developed, so that the development period is long, and the development efficiency is far lower than that of a BS (base station) architecture at a web end.
(3) The existing photovoltaic data acquisition system is only limited and can not diagnose the health condition and the subfissure state of the photovoltaic module.
Disclosure of Invention
In order to solve the problems, the invention discloses a photovoltaic module subfissure detection system based on the technology of the Internet of things, which is simple in structure, good in system detection reliability, simple to operate and high in automation degree.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the photovoltaic module subfissure detection system based on the Internet of things technology is divided into three parts, namely a hardware module (data acquisition board), a software part, a hardware module (data acquisition board) and a software data interaction part.
As an improvement of the invention, the hardware module (data acquisition board) comprises a single chip microcomputer system and an environment monitoring data fusion line;
designing a single chip microcomputer system: according to actual requirements, PV positive and negative lines of the photovoltaic module are connected to two ends of a current sensor and a voltage sensor, the output of the PV positive and negative lines is connected to an AD sampling port of a single chip microcomputer, and the direct-current side voltage and current of the photovoltaic module are read by an AD sampling chip of the single chip microcomputer in the cycle of 239.5 machine cycles. Meanwhile, a data protocol is planned, a standard modbus protocol is adopted, corresponding circuit voltage sampling values can be returned aiming at the broadcast command and the point-to-point command, and the modbus protocol is also used for returning. Besides voltage and current AD detection sampling, the single chip microcomputer system is additionally provided with control switches of two relays at two GPIO ports, and the control switches can control the on-off of the corresponding relays by receiving control instructions sent by an upper computer, so that the photovoltaic module can be reversely powered up by an external power supply, and the EL detection of the subfissure is convenient to carry out. In addition, the singlechip is also provided with 1 dial switch with 8 bits, and the binary value corresponding to the switch is the physical address (namely the serial number) of the singlechip and the corresponding photovoltaic module.
The environment detection data and electrical data fusion line is as follows: the data of various environment detection sensors and all photovoltaic module data acquisition boards are connected to a 485 bus and transmitted to software through time division multiplexing.
As an improvement of the invention, the software part comprises a service flow process, a data storage management center, an electric energy loss algorithm and a subfissure detection algorithm;
and (3) service flow processing: the data management center is responsible for receiving and maintaining the connection of bottom hardware equipment, receiving and processing various information data acquired by the bottom hardware equipment, analyzing and processing the information data, and storing the information data into the data management center; on the other hand, certain operations performed by a user on a web page are received, for example, data of a single photovoltaic module is read (point-to-point) and a power loss algorithm is called to judge whether the current module is healthy or not, whether a relay is operated to be powered on by a reverse voltage and an EL (electroluminescence) graph is shot or not is determined, and the series of operations are sent to corresponding equipment and enabled to be effective. And if the EL image is shot, calling a subfissure detection algorithm by software to detect whether subfissure exists or not and informing a user.
The data storage management center: and the SQL database which is created locally stores the electrical data and the environmental data corresponding to each time stamp. Through this data storage management center. The historical data of the system can be conveniently inquired.
Electric energy loss algorithm: calculating the power to be applied of the photovoltaic module in the current environment according to the environmental data and the factory rated data of the photovoltaic module, and marking as P1; meanwhile, the current I and the current voltage U can be obtained according to the data detection board, the current actual output power P2= UI is further obtained, and the loss condition of the electric energy is obtained through P1/P2;
hidden crack detection algorithm: a two-stage deep probing scheme is used. The original EL image is first divided according to its grid lines and cut into fixed-size squares. Candidate boxes are then extracted as input for the second stage using the ROI proposal method. In the second stage, the improved convolutional neural network based on the candidate box is supervised by a binary label.
As an improvement of the invention, the software part provides interface display and user operation functions. A user can see the current electrical information and various environmental data information of each photovoltaic module; meanwhile, a user can operate each operation button on the interface to switch modes (reading all photovoltaic module electrical data and the electric energy loss condition of each module, reading specified photovoltaic module electrical data and the electric energy loss condition of the specified module, reading environment data, setting reverse voltage and carrying out reverse electrification to obtain a subfissure detection result).
As an improvement of the invention, the hardware module (data acquisition board) and the data interaction part of the background read and send instructions through a Java TXRX serial port packet. Both the sent and received command and return data frames use their own modified modbus protocol.
As an improvement of the invention, the user can have the following three modes through interface operation: automatic mode, manual mode, reverse power-on mode.
Automatic mode: in the mode, the software reads the values acquired by all the photovoltaic module data acquisition boards and the environmental sensors by taking 5s as a unit, stores the values into a data storage management center, and simultaneously sends the values to a page for displaying.
Manual mode: in this mode, the software still reads the values collected by all the photovoltaic module data collection boards and the environmental sensors in 5s units, and stores the values in the data storage management center, but does not display the values on a page. At the moment, the user can check the electric data and the electric energy loss condition of the photovoltaic module with the specified number.
Reverse power-on mode: in this mode, the background still reads the values acquired by all the photovoltaic module data acquisition boards and the environmental sensors by taking 5s as a unit, and stores the values into the data storage management center, but does not display the values on the page. At the moment, the user finds that the assembly is in an unhealthy state, can control reverse voltage to be added to the appointed photovoltaic assembly to shoot an EL picture, and performs hidden crack detection on the picture to inform the user of hidden crack conditions.
As an improvement of the invention, the modbus protocol adopts the crc16 verification, and the specific format is frame header + function + address + delay + verification code. The function code is corresponding to three types of inquiry current, voltage and current-voltage comprehensive inquiry; the addresses are point-to-point addresses and broadcast addresses; the delay is how much delay is returned after the received instruction is executed, and the actual return delay is the delay x address, so that time-sharing multiplexing is realized.
The invention relates to a detection method of a photovoltaic module subfissure detection system based on the technology of the Internet of things, which comprises the following steps:
a user sends an instruction on a web page to inquire the electrical data information of a specified photovoltaic assembly and obtain the health state of the assembly. When the user finds that the component is unhealthy, the user can send an instruction to set a reverse voltage, switch on the reverse voltage and shoot an EL picture, and then the system calls a subfissure detection algorithm to analyze the subfissure situation and informs the user.
The invention has the beneficial effects that:
the invention can conveniently and efficiently read and control the electrical data and various environmental data of the photovoltaic module, does not need to compile a corresponding client, and can access and read and control at any time through a web browser to realize the function of the Internet of things. In addition, the system can read the component level electrical data instead of the traditional inverter string level data, evaluate the data and calculate the electric energy loss so as to judge the health condition of the current component. The user can apply a reverse voltage to the photovoltaic module through the health condition and take an EL map. The system analyzes the EL image to inform a user of the subfissure condition; the photovoltaic module on-line subfissure detection method realizes the on-line subfissure detection of the photovoltaic module, can obtain the health degree and subfissure condition of the current module through an intelligent algorithm, and has the advantages of simple structure, good system detection reliability, simple operation and high automation degree.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a system diagram of a single chip microcomputer of the present invention;
FIG. 3 is a graph of the fusion of environmental test data with electrical data in accordance with the present invention;
FIG. 4 is a reverse voltage block diagram of the present invention;
FIG. 5 is a functional diagram of the business process processing and data storage management center of the present invention;
FIG. 6 is an auto mode interface of the present invention;
FIG. 7 is a manual mode interface of the present invention;
fig. 8 is a 485 bus data protocol format diagram of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The invention is based on the technology of the Internet of things, and the web interface part provides interface display and user operation functions. A user can see the current electrical information and various environmental data information of each photovoltaic module; meanwhile, a user can operate each operation button on the interface to switch modes (reading all photovoltaic module electrical data and the electric energy loss condition of each module, reading specified photovoltaic module electrical data and the electric energy loss condition of the specified module, reading environment data, setting reverse voltage and carrying out reverse electrification to obtain a subfissure detection result).
Specifically, the method comprises the following steps:
as shown in fig. 1, the hardware module (data acquisition board) includes the integration of the system design of the single chip microcomputer and the environmental monitoring data;
designing a single chip microcomputer system: as shown in fig. 2, according to the actual content of the invention, the PV positive and negative lines of the photovoltaic module are connected to two ends of the current sensor and the voltage sensor, and the output of the sensor is connected to the AD sampling port of the single chip microcomputer; the two GPIO ports are connected with the control ports of the two relays to be used as control ports for controlling reverse voltage; the other 8 GPIO ports are connected with an 8-bit dial switch as address information; the TXRX port of the singlechip is connected to a 485 communication bus through a TD321S 485H-A.
And (3) fusing environmental detection data and electrical data: as shown in fig. 3, the data of the various environmental detection sensors and all the photovoltaic module data acquisition boards are connected to a 485 bus and transmitted to the background through time division multiplexing.
As shown in fig. 4, two GPIO ports are provided at control ports of two relays, and the on-off relay can control the loop direction of current and can be connected with reverse voltage.
As shown in fig. 5, a user can obtain the electrical data and the power consumption of the current photovoltaic module through a web browser. According to the electric energy loss condition, a user can control to switch on and off the reverse voltage and shoot an EL image to perform subfissure detection to obtain a current subfissure detection result.
As shown in FIG. 6, this interface is an auto mode interface
As shown in FIG. 7, the interface is a manual mode interface, in which setting the power source is entering a reverse power-on mode
And the hardware module (data acquisition board) and the data interaction part of the background read and send instructions through a Java TXRX serial port packet. Both the sent and received command and return data frames use their own modified modbus protocol. The improved modbus protocol adopts crc16 verification, and the specific format is frame header + function + address + delay + verification code. The function code is corresponding to three types of inquiry current, voltage and current-voltage comprehensive inquiry; the addresses are point-to-point addresses and broadcast addresses; the delay is how much delay is returned after the received instruction is executed, the actual return delay is a delay x address, and thus time division multiplexing is achieved, and a specific protocol format is shown in fig. 8.
Claims (8)
1. The utility model provides a photovoltaic module subfissure detecting system based on internet of things, its characterized in that: the system is divided into three parts, namely a hardware module, a software part and a data interaction part of the hardware module and the software.
2. The photovoltaic module subfissure detection system based on the internet of things technology according to claim 1, wherein: the hardware module comprises a single chip microcomputer system and an environment monitoring data fusion line;
designing a single chip microcomputer system: connecting PV positive and negative lines of the photovoltaic module to two ends of a current sensor and a voltage sensor, connecting an output to an AD sampling port of a singlechip, and reading the direct-current side voltage and current of the photovoltaic module by an AD sampling chip of the singlechip with the cycle of 239.5 machines as a cycle; meanwhile, a data protocol is drawn up, a standard modbus protocol is adopted, corresponding circuit voltage sampling values can be returned for the broadcast command and the point-to-point command, and the modbus protocol is also used for returning; the singlechip system is provided with control switches of two relays at two GPIO ports besides voltage and current AD detection sampling, and the control switches can control the on-off of the corresponding relays by receiving a control instruction sent by an upper computer, so that the photovoltaic module can be reversely electrified by an external power supply, and the EL detection of the subfissure is convenient; in addition, the singlechip is also provided with 1 8-bit dial switch, and the binary value corresponding to the switch is the physical address of the singlechip and the corresponding photovoltaic module; the environment detection data and electrical data fusion line is as follows: the data of various environment detection sensors and all photovoltaic module data acquisition boards are connected to a 485 bus and transmitted to software through time division multiplexing.
3. The photovoltaic module subfissure detection system based on the internet of things technology according to claim 1, wherein: the software part comprises a service flow process, a data storage management center, an electric energy loss algorithm and a subfissure detection algorithm;
and (3) service flow logic: the data management center is responsible for receiving and maintaining the connection of bottom hardware equipment, receiving and processing various information data acquired by the bottom hardware equipment, analyzing and processing the information data, and storing the information data into the data management center; on the other hand, certain operations performed by a user on a web page are received, for example, data of a single photovoltaic module is read, an electric energy loss algorithm is called to judge whether the current module is healthy or not, whether a relay is operated to be powered on with reverse voltage or not is determined, an EL (electro-luminescence) picture is shot, and the series of operations are sent to corresponding equipment and are enabled to take effect; if the EL image is shot, calling a subfissure detection algorithm by software to detect whether subfissure exists or not and informing a user;
the data storage management center: storing the electrical data and the environmental data value corresponding to each timestamp in a local created SQL database; querying historical data of the system through the data storage management center;
electric energy loss algorithm: calculating the power to be applied of the photovoltaic module in the current environment according to the environmental data and the factory rated data of the photovoltaic module, and marking as P1; meanwhile, the current I and the current voltage U can be obtained according to the data detection board, the current actual output power P2= UI is further obtained, and the loss condition of the electric energy is obtained through P1/P2;
hidden crack detection algorithm: a two-stage deep detection scheme is used; firstly, segmenting an original EL image according to grid lines of the original EL image, and cutting the original EL image into squares with fixed sizes; extracting a candidate frame by using an ROI (region of interest) suggestion method as input of a second stage; in the second stage, the improved convolutional neural network based on the candidate box is supervised by a binary label.
4. The photovoltaic module subfissure detection system based on the internet of things technology as claimed in claim 3, wherein: the software part provides interface display and user operation functions, and a user can see the current electrical information and various environmental data information of each photovoltaic module; meanwhile, the user can operate each operation button on the interface to switch the modes.
5. The photovoltaic module subfissure detection system based on the internet of things technology according to claim 1, wherein: the hardware module and the data interaction part of the background read and send instructions through a Java TXRX serial port packet; the sent and received command and return data frames both use the modbus protocol.
6. The photovoltaic module subfissure detection system based on the internet of things technology as claimed in claim 4, wherein: the user has the following three modes through the interface operation: an automatic mode, a manual mode, a reverse power-on mode;
automatic mode: in the mode, the software reads values acquired by all the photovoltaic module data acquisition boards and the environmental sensors by taking 5s as a unit, stores the values into a data storage management center, and simultaneously sends the values to a page for displaying;
manual mode: in the mode, the software still reads the values acquired by all the photovoltaic module data acquisition boards and the environmental sensors by taking 5s as a unit, and stores the values into a data storage management center, but does not display the values on a page; at the moment, a user can check the electric data and the electric energy loss condition of the photovoltaic module with the specified number;
reverse power-on mode: in the mode, the background still reads the values acquired by all the photovoltaic module data acquisition boards and the environmental sensors by taking 5s as a unit, and stores the values into the data storage management center, but does not display the values on a page; at the moment, the user finds that the assembly is in an unhealthy state, can control reverse voltage to be added to the appointed photovoltaic assembly to shoot an EL picture, and performs hidden crack detection on the picture to inform the user of hidden crack conditions.
7. The photovoltaic module subfissure detection system based on the internet of things technology as claimed in claim 5, wherein: the modbus protocol adopts crc16 verification, and the specific format is frame header + function + address + delay + verification code; the function code is corresponding to three types of inquiry current, voltage and current-voltage comprehensive inquiry; the addresses are point-to-point addresses and broadcast addresses; the delay is how much delay is returned after the received instruction is executed, and the actual return delay is the delay x address, so that time-sharing multiplexing is realized.
8. The detection method of the photovoltaic module subfissure detection system based on the internet of things technology according to claim 1, which comprises the following steps: the method is characterized in that: a user sends an instruction on a web page to inquire the electrical data information of a specified photovoltaic assembly and obtains the health state of the assembly through an electric energy attenuation algorithm; when the user finds that the component is unhealthy, the user can send an instruction to set a reverse voltage, switch on the reverse voltage and shoot an EL picture, and then the system calls a subfissure detection algorithm to analyze the subfissure situation and informs the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911330256.8A CN111024711B (en) | 2019-12-20 | 2019-12-20 | Photovoltaic module subfissure detection system based on internet of things technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911330256.8A CN111024711B (en) | 2019-12-20 | 2019-12-20 | Photovoltaic module subfissure detection system based on internet of things technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111024711A true CN111024711A (en) | 2020-04-17 |
| CN111024711B CN111024711B (en) | 2022-07-12 |
Family
ID=70212422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911330256.8A Active CN111024711B (en) | 2019-12-20 | 2019-12-20 | Photovoltaic module subfissure detection system based on internet of things technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111024711B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116817808A (en) * | 2023-08-31 | 2023-09-29 | 东北电力大学 | Photovoltaic board hidden crack length detecting system based on air coupling Lamb wave |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520683A (en) * | 2011-12-02 | 2012-06-27 | 珈伟太阳能科技(上海)有限公司 | Energy source monitoring cloud platform for photovoltaic system |
| CN104734362A (en) * | 2015-04-07 | 2015-06-24 | 上海理工大学 | Photovoltaic inverter health status monitoring and alarm system |
| CN104779904A (en) * | 2015-04-20 | 2015-07-15 | 杭州电子科技大学 | Solar photovoltaic monitoring system based on Internet of Things |
| CN108169669A (en) * | 2018-01-24 | 2018-06-15 | 宁波大家小家网络科技有限公司 | A kind of method and system of the inverter digital independent analysis of photovoltaic power generation apparatus |
| CN209764748U (en) * | 2019-05-01 | 2019-12-10 | 杭州万维新能源有限公司 | Photovoltaic board dust detection device based on thing networking |
-
2019
- 2019-12-20 CN CN201911330256.8A patent/CN111024711B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520683A (en) * | 2011-12-02 | 2012-06-27 | 珈伟太阳能科技(上海)有限公司 | Energy source monitoring cloud platform for photovoltaic system |
| CN104734362A (en) * | 2015-04-07 | 2015-06-24 | 上海理工大学 | Photovoltaic inverter health status monitoring and alarm system |
| CN104779904A (en) * | 2015-04-20 | 2015-07-15 | 杭州电子科技大学 | Solar photovoltaic monitoring system based on Internet of Things |
| CN108169669A (en) * | 2018-01-24 | 2018-06-15 | 宁波大家小家网络科技有限公司 | A kind of method and system of the inverter digital independent analysis of photovoltaic power generation apparatus |
| CN209764748U (en) * | 2019-05-01 | 2019-12-10 | 杭州万维新能源有限公司 | Photovoltaic board dust detection device based on thing networking |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116817808A (en) * | 2023-08-31 | 2023-09-29 | 东北电力大学 | Photovoltaic board hidden crack length detecting system based on air coupling Lamb wave |
| CN116817808B (en) * | 2023-08-31 | 2023-11-07 | 东北电力大学 | Photovoltaic board hidden crack length detecting system based on air coupling Lamb wave |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111024711B (en) | 2022-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102116847B (en) | Single battery performance parameter acquisition system for battery pack | |
| CN201674273U (en) | Automatic transfer switch controller | |
| CN201754223U (en) | Energy saving true standby circuit | |
| CN103914097B (en) | Maximum power point of photovoltaic power generation system fast tracking method | |
| CN206370731U (en) | A kind of Feeder Terminal Unit and electrical power distribution automatization system | |
| CN105785837B (en) | A kind of energy monitoring control device and method based on embedded system | |
| CN111024711B (en) | Photovoltaic module subfissure detection system based on internet of things technology | |
| CN101873349B (en) | The multinode real-time power consumption monitoring system in the environment of wireless sensor network | |
| CN104506342A (en) | Rack-mounted server system | |
| CN110166970B (en) | Comprehensive energy monitoring system and method | |
| CN114995580A (en) | Maximum power point tracking method and system for photovoltaic system | |
| CN114781481A (en) | Household photovoltaic power generation equipment management system and method based on Internet of things | |
| CN206164602U (en) | Air source heat pump expert remote management cloud service system | |
| CN208888641U (en) | A kind of industry spot automation equipment data collection analysis system based on TCP network | |
| CN106708560A (en) | RF communication-based ammeter wireless program upgrading method and wireless upgrading ammeter | |
| CN204013610U (en) | Photovoltaic plant data processing transmission system | |
| CN216851896U (en) | Novel photovoltaic cell junction box device | |
| CN108536073A (en) | A kind of injection molding acquisition terminal, system and method | |
| CN204066378U (en) | A kind of Intelligent Information Collection device | |
| CN212875467U (en) | Electric energy monitoring system | |
| CN203465837U (en) | Data acquisition monitoring system for vacuum die-casting machine | |
| CN208061886U (en) | Reclosing switchs sport intellect control system | |
| CN203165203U (en) | Wire-communication-based electrical information acquisition device | |
| CN105223850A (en) | A kind of energy monitor based on smart bluetooth and control method | |
| CN111313560A (en) | Intelligent power distribution management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |