CN115589187A - Photovoltaic power generation system and method for improving power generation efficiency of solar cell - Google Patents
Photovoltaic power generation system and method for improving power generation efficiency of solar cell Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a photovoltaic power generation system and method for improving the power generation efficiency of a solar cell, and particularly relates to the field of photovoltaic power generation, and the photovoltaic power generation system comprises a monitoring module, a power generation amount prediction module, a central control module, a control module and a display module, wherein the monitoring module is mainly used for collecting relevant information of photovoltaic power generation and transmitting the collected information to the central control module and the power generation prediction module, the power generation amount prediction module is used for predicting a power generation amount value and providing a basis for comparison and judgment of the central control module, the central control module is used for judging the transmitted information of the monitoring module, the power generation efficiency of the solar cell panel and the optimal inclination angle of a bracket are obtained through operation of the central control module, the information is transmitted to the control module and the display module, the control module is used for adjusting the angle of the photovoltaic cell panel and controlling the replacement of a power generation component, and the display module is used for receiving the information of the control module and displaying the power generation information in a display screen.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation system and method for improving the power generation efficiency of a solar cell.
Background
The photovoltaic power generation is a power generation mode of directly converting solar radiation into electric energy, is the mainstream of the current solar power generation, is a photovoltaic power generation facility which is particularly constructed near a user site, has an operation mode of self-use by the user side, surfing the internet with multiple electric quantities and is characterized by balance adjustment of a power distribution system, is a novel power generation and energy comprehensive utilization mode with wide development prospect, advocates the principles of near power generation, near grid connection, near conversion and near use, can effectively improve the generated energy of a photovoltaic power station with the same scale, and also effectively solves the loss problem of the electric power in boosting and long-distance transportation.
The development of distributed photovoltaic power generation has important significance for optimizing an energy structure, realizing a double-carbon target, promoting energy conservation and emission reduction and realizing economic sustainable development. A1 kilowatt photovoltaic power generation system is installed under the average sunshine condition in China, 1200 electricity can be generated in 1 year, the using amount of coal can be reduced by about 400 g, and the emission of carbon dioxide is reduced by about 1 ton.
The distributed photovoltaic power generation apparatus is basically configured to: solar cell panel, dc-to-ac converter, support cable, installation accessory, monitored control system etc. other auxiliary assembly such as transformer, switch board still need in large-scale power station, and distributed photovoltaic power generation system divide into two kinds of cluster formula and concentrated formula.
The efficiency of solar cell generation affects the effectiveness of the distributed photovoltaic power generation system, and solar panels naturally produce less power over time, and this reduced power output is known as the degradation rate. The median rate of solar panel degradation is about 0.5%, which means that the power production of the solar panel will decline at a rate of 0.5% per year. At present, the factors influencing the generating efficiency of the solar cell mainly comprise sunlight intensity, cell quality and solar panel materials. Therefore, the power generation efficiency of the solar cell panel can be improved and the service life of the solar cell panel can be prolonged by the following method: 1. the surface of the solar panel is kept clean and tidy, and hot spots on the surface of the solar panel are reduced; 2. solar panels are monitored and maintained periodically to maintain the performance of the panels, meters, inverters and other components of the solar system operating at maximum efficiency.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a photovoltaic power generation system and a method for improving the power generation efficiency of a solar cell.
Technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: the system comprises a monitoring module, a power generation amount prediction module, a central control module, a control module and a display module, wherein the monitoring module is mainly used for collecting temperature, average illumination amplitude, illumination angle, illumination duration, solar panel hot spot, solar panel information, power generation voltage, power generation current and daily power generation amount, transmitting the collected information to the central control module and the power generation prediction module, the power generation amount prediction module is used for predicting power generation amount values and providing basis for comparison and judgment of the central control module, the central control module is used for judging the transmitted information of the monitoring module, the solar panel power generation efficiency and the optimal support inclination angle are obtained through calculation of the central control module, the information is transmitted to the control module and the display module, the control module is used for adjusting the angle of the photovoltaic panel and controlling the replacement of a power generation component, the control module is connected with a direct current junction box, a direct current power distribution cabinet, a grid-connected inverter, a storage battery and a solar panel support, and the display module is used for receiving the control module information and displaying the power generation information in a display screen.
In a preferred embodiment, the generating capacity prediction module builds a photovoltaic generating capacity prediction model by combining a drosophila algorithm with a neural network hybrid algorithm, the prediction model is divided into an input layer, a hidden layer and an output layer, and input variables of the input layer are the average temperature and the average illumination of each time period of the photovoltaic generating system; the neural network excitation function adopts a unipolar sigmods excitation function, the neural network structure is a double hidden layer, the number of neurons in the hidden layer is 25, the hidden layer consists of neurons, the neurons determine the weight values of all input variables and all output variables, and the average temperature and the average illumination of the sample in all time periods are predicted through the hidden layer; the output layer outputs the photovoltaic power generation amount of each time period on the day, the photovoltaic power generation prediction model is divided into 24 hours/day, each hour is a calculation unit, each node in the input layer serves as an excitation signal to form an input signal of the next layer, the output signal of the layer serves as an input signal of the next layer, and the like.
In a preferred embodiment, the drosophila algorithm incorporates a hybrid algorithm flow of neural networks as follows:
a1, initializing a population scale S, maximizing iteration times iter, and randomly generating the position, the moving direction, the moving step length and the neuron weight of each drosophila;
a2, reading data, reading training sample data of the photovoltaic power generation system, wherein the training sample data comprises average temperature, average illumination intensity and photovoltaic power generation amount of each time period, and performing normalization processing on the sample data;
step A3, training through a neural network sample to obtain a corresponding weight, correcting and optimizing the weight by using a drosophila algorithm, moving the drosophila individual to a preset direction for a certain step length, calculating concentration, namely a predicted value, and if the predicted value best is better, retaining and continuously iterating until the prediction precision is reached;
and A4, outputting the position with the highest concentration of the fruit flies in the population, namely the optimal weight of the neural network, and outputting a prediction result.
In a preferred embodiment, the monitoring module comprises a hot spot detection unit, the hot spot detection unit detects by an unmanned aerial vehicle, a high definition camera, an infrared camera and a communication device are mounted on the unmanned aerial vehicle, and the hot spot positioning is used to shoot and patrol the photovoltaic panel from multiple angles to obtain hot spot information, and the specific flow of the infrared hot spot detection of the unmanned aerial vehicle is as follows: planning a detection range and a detection path, then carrying out flight shooting, carrying out monitoring operation on hot spots, and finally carrying out comprehensive analysis on the image and marking an abnormal area.
In a preferred embodiment, the method includes the steps of firstly collecting photovoltaic power generation related information through a monitoring module, then predicting photovoltaic power generation amount according to the collected information, then comparing and judging the collected data by a central control module to form an operation instruction, finally executing the operation instruction, and monitoring an execution result in time to guarantee an execution effect, and specifically includes the following steps:
step 101, data acquisition, namely acquiring photovoltaic power generation weather information, solar panel surface information and power generation component performance information according to a certain frequency through a monitoring module, and acquiring average temperature, average illumination amplitude, illumination angle, illumination duration, solar panel hot spot condition, loss rate of a power generation component and daily generated energy in each time period;
102, building a photovoltaic power generation amount prediction model, predicting the photovoltaic power generation amount of each time period of the current day by inputting the average temperature and the average illumination of each time period of the photovoltaic power generation system, and setting the predicted photovoltaic power generation amount as a reference value;
103, generating an operation instruction and early warning, calculating the optimal inclination angle of the solar panel through an operation unit in the central control module, calculating the generation efficiency of the solar panel and the power consumed by a power generation component every day, comparing the predicted photovoltaic power generation with the actual power generation through a comparison judgment unit in the central control module, forming a self-checking instruction if the actual power generation is abnormally lower than the predicted power generation, checking the arrangement angle of the distributed photovoltaic power generation panels and the power generation component one by one, monitoring the power generation component, including hot spot monitoring of the solar panel, comparing an actual hot image obtained by a hot spot detection unit with a standard hot image, sending an instruction for replacing and cleaning the panels if the color difference of the actual hot image is large, starting an early warning unit in the central control module if the power consumption of the power generation component exceeds an early warning value, and forming an operation instruction to be transmitted to the control module;
104, executing a central control instruction, and executing the generated operation instruction through a control module, wherein the operation instruction comprises an automatic control execution module and a manual execution module, the automatic control execution module is used for automatically controlling the inclination angle of the solar panel, the execution mode is that the control module controls the inclination angle of the solar panel through a motor on a control support and controls the motor to rotate so as to adjust the angle of the photovoltaic solar panel, and the manual execution module is used for executing instructions for replacing the solar panel and replacing a power generation component;
and 105, feeding back an execution result, after the execution is finished, manually starting the monitoring module through a human-computer interaction unit of the central control module, verifying the execution effect, and after the verification is passed, canceling the early warning.
In a preferred embodiment, the data acquisition is obtained using a large data analysis IV curve, including evaluation-step or notch, low current, low voltage.
The invention has the technical effects and advantages that: according to the invention, the surface of the solar panel is monitored, measures are taken in time to remove foreign matters on the surface of the solar panel, solar cells with serious hot spots are replaced in time, the solar panel is ensured to be directly irradiated by the sun by adjusting the angle of the solar panel, the power generation efficiency of the solar cell is improved, and the operation efficiency of important parts is ensured by monitoring and maintaining the important parts for photovoltaic power generation.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 is a schematic diagram of the system method of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be discussed further in subsequent figures.
Embodiments of the application are applicable to computer systems/servers that are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the computer system/server include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
The computer system/server may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
The present embodiment is based on a distributed photovoltaic power generation apparatus, which is basically configured to: the photovoltaic cell module, the direct current collection flow box, the direct current switch board, the inverter that is incorporated into the power networks, installation accessory, charge-discharge controller, battery. The photovoltaic cell assemblies are arranged in parallel or in series according to an array mode, solar energy is converted into electric energy, the electric energy is concentrated and sent into a direct current power distribution cabinet through a direct current header box, or the electric energy is converted into alternating current through a grid-connected inverter and sent into an alternating current power distribution cabinet, the photovoltaic cell assemblies are electrically connected with the direct current power distribution cabinet through a positive direct current bus and a negative direct current bus, the photovoltaic cell assemblies are fixedly connected onto a support, and the support is controlled by a motor and can change the inclination angle.
The invention provides a photovoltaic power generation system and a method for improving the power generation efficiency of a solar cell as shown in figure 1, and the system comprises a monitoring module, a power generation amount prediction module, a central control module, a control module and a display module, wherein the monitoring module is mainly used for collecting temperature, average illumination amplitude, illumination angle, illumination duration, solar panel heat spot, panel information, power generation voltage, power generation current and daily power generation amount, and transmitting the collected information to the central control module and the power generation prediction module, the power generation amount prediction module is used for predicting the power generation amount value and providing basis for comparison and judgment of the central control module, the central control module is used for judging the transmitted information of the monitoring module, the power generation efficiency of the solar panel and the optimal inclination angle of a support are obtained through operation of the central control module, the information is transmitted to the control module and the display module, the control module is used for adjusting the angle of the photovoltaic panel and controlling the replacement of a power generation component, the control module is connected with a direct current junction box, a direct current power distribution cabinet, a grid-connected inverter, a storage battery and a panel support, and the display module is used for receiving the control module information and displaying the power generation information in a display screen.
The photovoltaic power generation prediction model is built by combining a drosophila algorithm with a neural network hybrid algorithm through the power generation prediction module, the prediction model is divided into an input layer, a hidden layer and an output layer, and input variables of the input layer are the average temperature and the average illumination of the photovoltaic power generation system in each time period; in the hidden layer, a unipolar sigmods excitation function is adopted as a neural network excitation function: the neural network structure is a double hidden layer, the number of neurons of the hidden layer is 25, the hidden layer consists of neurons, the neurons determine the weight values of all input variables and all output variables, and the average temperature and the average illumination of the sample in all time periods are predicted through the hidden layer; the output layer outputs the photovoltaic power generation amount of each time period on the current day, the photovoltaic power generation prediction model is divided into 24 hours/day, each hour is a calculation unit, each node in the input layer serves as an excitation signal to form an input signal of the next layer, the output signal of the layer serves as an input signal of the lower layer, and so on, the fruit fly algorithm is combined with the mixed algorithm flow of the neural network as follows:
a1, initializing population scale S, maximum iteration number iter, and randomly generating the position, moving direction, moving step length and neuron weight of each drosophila;
a2, reading data, reading training sample data of the photovoltaic power generation system, wherein the training sample data comprises average temperature, average illumination intensity and photovoltaic power generation amount of each time period, and performing normalization processing on the sample data;
step A3, training through a neural network sample to obtain a corresponding weight, correcting and optimizing the weight by using a drosophila algorithm, moving the drosophila individuals to a preset direction for a certain step length, calculating concentration, wherein the concentration is a predicted value, if the predicted value best is better, retaining, and continuing iteration until the prediction precision is reached;
and A4, outputting the position with the highest concentration of the fruit flies in the population, namely the optimal weight of the neural network. And outputting a prediction result.
The output side of the grid-connected inverter is connected with a public power distribution network in parallel, a synchronization point is arranged at the alternating current output end of the grid-connected inverter, the grid-connected inverter automatically detects the voltage, the phase and the frequency of the power grid, when the voltage, the phase and the frequency of the synchronization point are consistent with the voltage, the phase and the frequency of the power grid, the grid-connected inverter transmits alternating current to the low-voltage end of a transformer, the alternating current is boosted by a three-phase transformer and then is transmitted to the public power distribution network by a power transmission line for power supply, and therefore the fact that the public power distribution network is not impacted and disturbed when the inverter is in grid-connected operation is guaranteed.
The invention provides a photovoltaic power generation system and a method for improving the power generation efficiency of a solar cell, as shown in figure 2, wherein the method comprises the following steps of firstly collecting relevant photovoltaic power generation information through a monitoring module, then predicting the photovoltaic power generation amount according to the collected information, then carrying out comparison and judgment on a central control module according to the collected data to form an operation instruction, finally executing the operation instruction, monitoring an execution result in time and ensuring the execution effect, and specifically comprises the following steps:
step 101, data acquisition, namely acquiring photovoltaic power generation weather information, solar panel surface information and power generation component performance information according to a certain frequency through a monitoring module, and acquiring average temperature, average illumination amplitude, illumination angle, illumination duration, solar panel hot spot condition, loss rate of a power generation component and daily generated energy in each period;
102, building a photovoltaic power generation amount prediction model, predicting the photovoltaic power generation amount of each time period of the current day by inputting the average temperature and the average illumination of each time period of the photovoltaic power generation system, and setting the predicted photovoltaic power generation amount as a reference value;
103, generating an operation instruction and early warning, calculating the optimal inclination angle of the solar panel through an operation unit in the central control module, calculating the generation efficiency of the solar panel and the power consumed by a power generation component every day, comparing the predicted photovoltaic power generation with the actual power generation through a comparison judgment unit in the central control module, forming a self-checking instruction if the actual power generation is abnormally lower than the predicted power generation, checking the arrangement angle of the distributed photovoltaic power generation panels and the power generation component one by one, monitoring the power generation component, including hot spot monitoring of the solar panel, comparing an actual hot image obtained by a hot spot detection unit with a standard hot image, sending an instruction for replacing and cleaning the panels if the color difference of the actual hot image is large, starting an early warning unit in the central control module if the power consumption of the power generation component exceeds an early warning value, and forming an operation instruction to be transmitted to the control module;
104, executing a central control instruction, and executing the generated operation instruction through a control module, wherein the operation instruction comprises an automatic control execution module and a manual execution module, the automatic control execution module is used for automatically controlling the inclination angle of the solar panel, the execution mode is that the control module controls the inclination angle of the solar panel through a motor on a control support and controls the motor to rotate so as to adjust the angle of the photovoltaic solar panel, and the manual execution module is used for executing instructions for replacing the solar panel and replacing a power generation component;
and 105, feeding back an execution result, after the execution is finished, manually starting a monitoring module through a human-computer interaction unit of the central control module, verifying the execution effect, and after the verification is passed, canceling the early warning.
In this embodiment, it should be specifically explained that the monitoring module includes hot spot detecting unit, and hot spot detecting unit detects through unmanned aerial vehicle, through carrying on high definition camera, infrared camera, communication device on unmanned aerial vehicle to use the hot spot location, shoot and patrol and examine the photovoltaic board from a plurality of angles, obtain hot spot information, the infrared hot spot of unmanned aerial vehicle detects concrete flow as follows: planning a detection range and a detection path, then carrying out flight shooting, carrying out monitoring operation on hot spots, and finally carrying out comprehensive analysis on the image and marking an abnormal area.
In this embodiment, it is specifically noted that: the illumination angle, the illumination duration and the temperature can be obtained through meteorological data in weather forecast.
In this embodiment, it is specifically noted that: the data acquisition is obtained by analyzing an IV curve by using big data, and comprises evaluation, step or recess, low current and low voltage.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. Improve solar cell generating efficiency's photovoltaic power generation system, its characterized in that: the solar photovoltaic power generation system comprises a monitoring module, a power generation predicting module, a central control module, a control module and a display module, wherein the monitoring module is mainly used for collecting temperature, average illumination amplitude, illumination angle, illumination duration, solar panel hot spots, panel information, power generation voltage, power generation current and daily power generation amount, transmitting the collected information to the central control module and the power generation predicting module, the power generation predicting module is used for predicting power generation amount values and providing basis for comparison and judgment of the central control module, the central control module is used for judging the transmitted information of the monitoring module, the solar panel power generation efficiency and the optimal support inclination angle are obtained through calculation of the central control module, the information is transmitted to the control module and the display module, the control module is used for adjusting the angle of a photovoltaic panel and controlling the replacement of a power generation component, the control module is connected with a direct current combiner box, a direct current power distribution cabinet, a grid-connected inverter, a storage battery and a panel support, and the display module is used for receiving the information of the control module and displaying the power generation information in a display screen.
2. The photovoltaic power generation system for improving the power generation efficiency of a solar cell according to claim 1, characterized in that: the photovoltaic power generation prediction model is built by combining a drosophila algorithm with a neural network hybrid algorithm and is divided into an input layer, a hidden layer and an output layer, and input variables of the input layer are the average temperature and the average illumination of the photovoltaic power generation system in each time period; the neural network excitation function adopts a unipolar sigmods excitation function, the neural network structure is a double hidden layer, the number of neurons in the hidden layer is 25, the hidden layer consists of neurons, the neurons determine the weight values of all input variables and all output variables, and the average temperature and the average illumination of the sample in all time periods are predicted through the hidden layer; the output layer outputs the photovoltaic power generation amount of each time period on the day, the photovoltaic power generation prediction model is divided into 24 hours/day, each hour is a calculation unit, each node in the input layer serves as an excitation signal to form an input signal of the next layer, the output signal of the layer serves as an input signal of the next layer, and the like.
3. The photovoltaic power generation system for improving the power generation efficiency of a solar cell according to claim 2, characterized in that: the mixed algorithm flow of the drosophila algorithm combined with the neural network is as follows:
a1, initializing population scale S, maximum iteration number iter, and randomly generating the position, moving direction, moving step length and neuron weight of each drosophila;
a2, reading data, reading training sample data of the photovoltaic power generation system, wherein the training sample data comprises average temperature, average illumination intensity and photovoltaic power generation amount of each time period, and performing normalization processing on the sample data;
step A3, training through a neural network sample to obtain a corresponding weight, correcting and optimizing the weight by using a drosophila algorithm, moving the drosophila individual to a preset direction for a certain step length, calculating concentration, namely a predicted value, and if the predicted value best is better, retaining and continuously iterating until the prediction precision is reached;
and A4, outputting the position with the highest concentration of the fruit flies in the population, namely the optimal weight of the neural network, and outputting a prediction result.
4. The photovoltaic power generation system for improving the power generation efficiency of a solar cell according to claim 1, characterized in that: the monitoring module includes hot spot detecting element, and hot spot detecting element detects through unmanned aerial vehicle, through carrying on high definition camera, infrared camera, communication device on unmanned aerial vehicle to use the hot spot location, shoot and patrol and examine the photovoltaic board from many angles, obtain hot spot information, the infrared hot spot of unmanned aerial vehicle detects concrete flow as follows: planning a detection range and a detection path, then carrying out flight shooting, carrying out monitoring operation on the hot spots, and finally carrying out comprehensive analysis on the image and marking an abnormal area.
5. The method of photovoltaic power generation system for improving power generation efficiency of solar cells according to any one of claims 1 to 4, wherein: the method comprises the steps of firstly collecting relevant information of photovoltaic power generation through a monitoring module, then predicting photovoltaic power generation capacity according to the collected information, then comparing and judging the collected data through a central control module to form an operation instruction, finally executing the operation instruction, monitoring an execution result in time and ensuring an execution effect, and specifically comprises the following steps:
step 101, data acquisition, namely acquiring photovoltaic power generation weather information, solar panel surface information and power generation component performance information according to a certain frequency through a monitoring module, and acquiring average temperature, average illumination amplitude, illumination angle, illumination duration, solar panel hot spot condition, loss rate of a power generation component and daily generated energy in each period;
102, building a photovoltaic power generation amount prediction model, predicting the photovoltaic power generation amount of each time period of the current day by inputting the average temperature and the average illumination of each time period of the photovoltaic power generation system, and setting the predicted photovoltaic power generation amount as a reference value;
103, generating an operation instruction and early warning, calculating the optimal inclination angle of the solar panel through an operation unit in the central control module, calculating the generation efficiency of the solar panel and the power consumed by a power generation component every day, comparing the predicted photovoltaic power generation with the actual power generation through a comparison and judgment unit in the central control module, forming a self-checking instruction if the actual power generation is abnormally lower than the predicted power generation, checking the arrangement angle of the distributed photovoltaic power generation panels and the power generation component one by one, monitoring the power generation component, including hot spot monitoring of the solar panel, comparing an actual hot image obtained by a hot spot detection unit with a standard hot image, sending an instruction for replacing and cleaning the panels if the color difference of the actual hot image is large, starting an early warning unit in the central control module if the power consumption of the power generation component exceeds the early warning value, and forming an operation instruction to be transmitted to the control module;
104, executing a central control instruction, and executing the generated operation instruction through a control module, wherein the operation instruction comprises an automatic control execution module and a manual execution module, the automatic control execution module is used for automatically controlling the inclination angle of the solar panel, the execution mode is that the control module controls the inclination angle of the solar panel through a motor on a control support and controls the motor to rotate so as to adjust the angle of the photovoltaic solar panel, and the manual execution module is used for executing instructions for replacing the solar panel and replacing a power generation component;
and 105, feeding back an execution result, after the execution is finished, manually starting the monitoring module through a human-computer interaction unit of the central control module, verifying the execution effect, and after the verification is passed, canceling the early warning.
6. The method of photovoltaic power generation system for improving the power generation efficiency of solar cells according to claim 5, wherein: the data acquisition is obtained by analyzing an IV curve by using big data, and comprises evaluation, step or recess, low current and low voltage.
7. The method of photovoltaic power generation system for improving the power generation efficiency of solar cells according to claim 5, wherein: the illumination angle, the illumination duration and the temperature can be obtained through meteorological data in weather forecast.
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| CN116883406A (en) * | 2023-09-08 | 2023-10-13 | 中交第一航务工程勘察设计院有限公司 | Photovoltaic power station hot spot detection device and method based on cleaning robot |
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| CN116883406A (en) * | 2023-09-08 | 2023-10-13 | 中交第一航务工程勘察设计院有限公司 | Photovoltaic power station hot spot detection device and method based on cleaning robot |
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| CN117151696A (en) * | 2023-10-27 | 2023-12-01 | 中科华辰(山东)实业股份有限公司 | Photovoltaic operation and maintenance management system |
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| CN117590873B (en) * | 2024-01-18 | 2024-04-19 | 广东永浩信息技术有限公司 | Intelligent monitoring system based on artificial intelligence and photovoltaic energy supply |
| CN117999994A (en) * | 2024-04-10 | 2024-05-10 | 四川永坚新能源科技有限公司 | Agricultural seed cultivation temperature control storage device, system and method |
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