A solar photovoltaic intelligent detection system and method based on the Internet of Things

Abstract

The application discloses a solar photovoltaic intelligent detection system and method based on Internet of Things, and belongs to the technical field of photovoltaic intelligent detection. The solar photovoltaic intelligent detection system based on Internet of Things comprises a data acquisition module, a data processing module, an abnormal early warning module, a control module and a safety detection module. The data acquisition module is used for collecting the temperature, voltage and current of the solar photovoltaic. The data processing module is used for calculating the power of the solar cell panel for converting light energy into electric energy. The abnormal early warning module is used for early warning and providing a solution when the power or temperature exceeds a threshold value. The control module is used for processing the early warning solar cell panel. The safety detection module is used for detecting the processed solar photovoltaic again. The application can detect and evaluate the solar photovoltaic, and early warn and quickly find various problems, thereby facilitating scientific management.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic intelligent detection technology, specifically to an Internet of Things-based intelligent detection system and method for solar photovoltaics. Background Technology

[0002] Against the backdrop of developing a low-carbon economy, solar photovoltaic (PV) power generation, compared to traditional power generation methods, effectively absorbs sunlight and provides insulation and cooling. Therefore, PV power generation systems hold a significant position in the energy sector. However, laboratory testing of solar cell modules is conducted under optimal light and temperature / humidity control conditions, which cannot be replicated on-site. This necessitates accurate evaluation of power plants. Furthermore, after a period of operation, the power generation characteristics of solar cells are affected, and environmental factors such as plastic bags can cause damage, leading to differences in power generation efficiency compared to their factory specifications. Therefore, a crucial aspect of power plant operation and management is testing the characteristics of the solar panels during operation. Currently, due to the relatively short construction time of domestic power plants and the reliance on manual monitoring of panel characteristics, efficiency is low, and data management lacks uniformity. This makes it difficult to trace problems when they arise, as there are no effective methods for rapid testing and evaluation of various components of the power plant. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent solar photovoltaic detection system and method based on the Internet of Things to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, the present invention provides the following technical solution: an IoT-based intelligent solar photovoltaic detection system, comprising a data acquisition module, a data processing module, an anomaly warning module, a control module, and a security detection module; The data acquisition module is used to collect the temperature, output voltage, and current of the solar photovoltaic system; the data processing module calculates the power of each solar panel in converting light energy into electrical energy, estimates the predicted values ​​of temperature and power based on the collected temperature and calculated power, and saves the data for comparison with the data of the next cycle in the current time period; the anomaly warning module is used to issue a warning when the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds a threshold; the control module is used to process the solar panels that have issued warnings; the safety detection module is used to re-detect the processed solar photovoltaic system. The output of the data acquisition module is connected to the input of the data processing module; the output of the data processing module is connected to the input of the anomaly warning module; the output of the anomaly warning module is connected to the input of the control module; and the output of the control module is connected to the input of the safety detection module.

[0005] According to the above technical solution, the data acquisition module collects the temperature, output voltage, and current of the solar panel every time T with a fixed period of time T, and uploads the collected data to the data processing module. The temperature is measured by sensors such as thermocouples to obtain the corresponding electrical signal; the voltage value is obtained by resistor voltage division; the current value is measured by a shunt to obtain the current signal; T is a constant; the function of the solar panel is to convert light into electromotive force (volts), and other devices that work with it to generate electricity are called solar photovoltaic modules.

[0006] According to the above technical solution, the data processing module includes a storage unit, a power calculation unit, and a comparison unit; The storage unit is used to store three types of data: the maximum and minimum temperatures uploaded by different solar panels at time intervals T, denoted as T. max T min The maximum and minimum current values ​​transmitted through different solar panels at time intervals T are denoted as I. max I min The maximum and minimum voltage values ​​transmitted between different solar panels at time intervals T are denoted as U. max U min The power calculation unit calculates the average power per cycle based on the uploaded maximum and minimum current values ​​and maximum and minimum voltage values. The formula for calculating the average power per cycle is as follows:

[0007] Among them, P avg The power average for each cycle; define a cycle index that includes the power average and the maximum temperature. The comparison unit calculates the predicted value of the periodic index according to the corresponding historical data, and compares it with the actual detection data of the time period. The specific steps are as follows: S3-1: Taking a single solar panel as a unit, based on the Hole phenomenon trend model:

[0008] in, This represents the estimated level for this cycle; This represents the predicted trend for that cycle. This is the output of the seasonal smoothing equation; is the periodic index value for this period; m is the period length; The smoothing parameter is horizontal; For smoothing the trend; For seasonal smoothing parameters; S3-2: The predicted value of the cycle index for the next cycle is:

[0009] in, This is the cycle index value for the next cycle; To predict the number of days ahead; S3-3: The daily sunshine duration is divided into three stages according to the light intensity. The different stages form a set of predicted periodic index values ​​H, which are compared with the actual detection data. The three stages are divided according to the light intensity: the first stage is the light enhancement stage, i.e., from sunrise to noon; the second stage is the light stability stage, i.e., from noon to afternoon; and the third stage is the light weakening stage, i.e., from afternoon to sunset. The specific times of noon and afternoon are equally divided according to the light intensity of the day.

[0010] According to the above technical solution, the comparison with the data is performed by performing an overlay calculation for each additional bit of recorded data. When historical data exceeds the threshold, the data is discarded and does not participate in the overlay calculation. The result of each overlay calculation is saved for comparison with the next uploaded data. A power threshold Q and a temperature threshold P are set. When the absolute value of the difference between the predicted value and the actual detected value of any cycle index exceeds the threshold, an early warning is issued.

[0011] According to the above technical solution, the power threshold Q is determined as follows: The scenario condition to which the most recent average power value belongs is determined. This scenario condition is based on historical prediction data and the weather conditions of the day, dividing the prediction values ​​into three scenarios: abnormal weather, standard weather, and obstruction. Abnormal weather refers to cloudy, rainy, foggy, or overcast weather, where solar visibility is low, resulting in less solar energy absorption and conversion into electricity by the solar panels. Standard weather refers to sunny or partly cloudy weather, where the solar panels absorb and convert more solar energy. Obstruction occurs when a warning is issued and an obstruction is present. The power threshold Q for each scenario condition is derived from historical data for that scenario. The process of deriving the power threshold Q for each scenario condition is as follows: The minimum value of the set of average power values ​​from the previous three periods within the same phase is taken, denoted as S. bmin The formula for calculating Q is:

[0012] Where z is the environmental impact factor, and the efficiency of solar energy in converting light energy into electrical energy varies under different scenarios; the temperature threshold P is related to the type of solar module, and is taken as the maximum value of the set of actual temperature values ​​uploaded by the solar panels at each time interval T in the first three cycles of the same stage, denoted as S. amax The formula for calculating P is:

[0013] Where P is the temperature threshold; c is the influence factor of different materials; f is the average temperature borne by the solar panel in this period; s0 is the light-receiving area of ​​the solar panel. When the difference between the predicted value and the actual detected value of the current index exceeds either the threshold Q or the threshold P, an early warning is issued.

[0014] Furthermore, the control module is used to perform an emergency short circuit on the solar panels involved when it receives an early warning message, and at the same time remind the staff to conduct on-site inspections at the system front end.

[0015] A smart detection method for solar photovoltaic based on the Internet of Things, the method includes the following steps: Step S100: Collect data on the temperature, output voltage, and current of the solar photovoltaic system; Step S200: Calculate the power of each solar panel in converting light energy into electrical energy. Based on the temperature and the calculated power, calculate the predicted values ​​of the two sets of data and save the data for comparison with the data of the next cycle in the current time period. Step S300: When the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds the threshold, an early warning is issued and a solution is proposed. Step S400: Process the solar panels for which warnings have been issued individually; Step S500: Test the processed solar photovoltaic system again.

[0016] Furthermore, the re-detection of the processed solar photovoltaic data in step S500 refers to starting from step S100, re-detecting and collecting solar photovoltaic data, and processing the data collected in the next cycle according to the method steps until no warning occurs.

[0017] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention can evaluate and test solar panels and modules in solar power plants, verify the temperature and power output of individual solar panels for different types of solar panels, and promptly address specific panels when problems are detected, reducing the waste of time and resources caused by excessive processing time. The intelligent solar photovoltaic detection system can assess problems and provide early warning modes for newly built solar power plants, objectively calculate their power generation capacity, and quickly detect various problems within the system, facilitating scientific management. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the process structure of an IoT-based intelligent detection method for solar photovoltaics. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 The present invention provides a technical solution: an IoT-based intelligent solar photovoltaic detection system, which includes a data acquisition module, a data processing module, an anomaly warning module, a control module, and a safety detection module; The data acquisition module is used to collect the temperature, output voltage, and current of the solar photovoltaic system; the data processing module calculates the power of each solar panel in converting light energy into electrical energy, estimates the predicted values ​​of temperature and power based on the collected temperature and calculated power, and saves the data for comparison with the data of the next cycle in the current time period; the anomaly warning module is used to issue a warning when the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds a threshold; the control module is used to process the solar panels that have issued warnings; the safety detection module is used to re-detect the processed solar photovoltaic system. The output of the data acquisition module is connected to the input of the data processing module; the output of the data processing module is connected to the input of the anomaly warning module; the output of the anomaly warning module is connected to the input of the control module; and the output of the control module is connected to the input of the safety detection module.

[0021] According to the above technical solution, the data acquisition module collects the temperature, output voltage, and current of the solar panel every time T with a fixed period of time T, and uploads the collected data to the data processing module. The temperature is measured by sensors such as thermocouples to obtain the corresponding electrical signal; the voltage value is obtained by resistor voltage division; the current value is measured by a shunt to obtain the current signal; T is a constant; the function of the solar panel is to convert light into electromotive force (volts), and other devices that work with it to generate electricity are called solar photovoltaic modules.

[0022] According to the above technical solution, the data processing module includes a storage unit, a power calculation unit, and a comparison unit; The storage unit is used to store three types of data: the maximum and minimum temperatures uploaded by different solar panels at time intervals T, denoted as T. max T min The maximum and minimum current values ​​transmitted through different solar panels at time intervals T are denoted as I. max I min The maximum and minimum voltage values ​​transmitted between different solar panels at time intervals T are denoted as U. max U min ; The power calculation unit calculates the average power per cycle based on the uploaded maximum and minimum current values ​​and maximum and minimum voltage values. The formula for calculating the average power per cycle is as follows:

[0023] Among them, P avg The power average for each cycle; define a cycle index that includes the power average and the maximum temperature. The comparison unit calculates the predicted value of the periodic index according to the corresponding historical data, and compares it with the actual detection data of the time period. The specific steps are as follows: S3-1: Taking a single solar panel as a unit, based on the Hole phenomenon trend model:

[0024] in, This represents the estimated level for this cycle; This represents the predicted trend for that cycle. This is the output of the seasonal smoothing equation; is the periodic index value for this period; m is the period length; The smoothing parameter is horizontal; For smoothing the trend; For seasonal smoothing parameters; S3-2: The predicted value of the cycle index for the next cycle is:

[0025] in, This is the cycle index value for the next cycle; To predict the number of days ahead; S3-3: The daily sunshine duration is divided into three stages according to the light intensity. The different stages form a set of predicted periodic index values ​​H, which are compared with the actual detection data. The three stages are divided according to the light intensity: the first stage is the light enhancement stage, i.e., from sunrise to noon; the second stage is the light stability stage, i.e., from noon to afternoon; and the third stage is the light weakening stage, i.e., from afternoon to sunset. The specific times of noon and afternoon are equally divided according to the light intensity of the day.

[0026] According to the above technical solution, the comparison with the data is performed by performing an overlay calculation for each additional bit of recorded data. When historical data exceeds the threshold, the data is discarded and does not participate in the overlay calculation. The result of each overlay calculation is saved for comparison with the next uploaded data. A power threshold Q and a temperature threshold P are set. When the absolute value of the difference between the predicted value and the actual detected value of any cycle index exceeds the threshold, an early warning is issued.

[0027] According to the above technical solution, the power threshold Q is determined as follows: The scenario condition to which the most recent average power value belongs is determined. This scenario condition is based on historical prediction data and the weather conditions of the day, dividing the prediction values ​​into three scenarios: abnormal weather, standard weather, and obstruction. Abnormal weather refers to cloudy, rainy, foggy, or overcast weather, where solar visibility is low, resulting in less solar energy absorption and conversion into electricity by the solar panels. Standard weather refers to sunny or partly cloudy weather, where the solar panels absorb and convert more solar energy. Obstruction occurs when a warning is issued and an obstruction is present. The power threshold Q for each scenario condition is derived from historical data for that scenario. The process of deriving the power threshold Q for each scenario condition is as follows: The minimum value of the set of average power values ​​from the previous three periods within the same phase is taken, denoted as S. bmin The formula for calculating Q is:

[0028] Where z is the environmental impact factor, and the efficiency of solar energy in converting light energy into electrical energy varies under different scenarios; the temperature threshold P is related to the type of solar module, and is taken as the maximum value of the set of pre-actual temperature values ​​uploaded by the solar panels at each time interval T in the first three cycles of the same stage, denoted as S. amax The formula for calculating P is:

[0029] Where P is the temperature threshold; c is the influence factor of different materials; f is the average temperature borne by the solar panel in this period; s0 is the light-receiving area of ​​the solar panel. When the difference between the predicted value and the actual detected value of the current index exceeds either the threshold Q or the threshold P, an early warning is issued.

[0030] Furthermore, the control module is used to perform an emergency short circuit on the solar panels involved when it receives an early warning message, and at the same time remind the staff to conduct on-site inspections at the system front end.

[0031] A smart detection method for solar photovoltaic based on the Internet of Things, the method includes the following steps: Step S100: Collect data on the temperature, output voltage, and current of the solar photovoltaic system; Step S200: Calculate the power of each solar panel in converting light energy into electrical energy. Based on the temperature and the calculated power, calculate the predicted values ​​of the two sets of data and save the data for comparison with the data of the next cycle in the current time period. Step S300: When the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds the threshold, an early warning is issued and a solution is proposed. Step S400: Process the solar panels for which warnings have been issued individually; Step S500: Test the processed solar photovoltaic system again.

[0032] Furthermore, the re-detection of the processed solar photovoltaic data in step S500 refers to starting from step S100, re-detecting and collecting solar photovoltaic data, and processing the data collected in the next cycle according to the method steps until no warning occurs.

[0033] In an embodiment of the present invention, the data acquisition module collects the solar panel temperature and output voltage and current every 10 minutes at a fixed cycle of 10 minutes. Assuming the collected voltage is 0.5V and the current is 4A, the maximum temperature during this cycle is... , The maximum current transmitted by different solar panels every 10 minutes is denoted as . and The maximum voltage transmitted every 10 minutes for different solar panels is recorded as follows: and ; Calculate the average power per cycle, According to the Hole phenomenon trend model: Predict the parameter values ​​for the next period: The formula for calculating the threshold Q is:

[0034] The formula for calculating the threshold P is:

[0035] Based on the actual test data, analyze and calculate whether the difference between the parameter values ​​for this period and the predicted data is within the threshold range.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart solar photovoltaic detection system based on the Internet of Things, characterized in that, The system includes a data acquisition module, a data processing module, an anomaly warning module, a control module, and a security detection module; The data acquisition module is used to collect the temperature, output voltage, and current of the solar photovoltaic system; the data processing module calculates the power of each solar panel in converting light energy into electrical energy, estimates the predicted values ​​of temperature and power based on the collected temperature and calculated power, and saves the data for comparison with the data of the next cycle in the current time period; the anomaly warning module is used to issue an early warning when the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds the set power threshold Q or temperature threshold P. The power threshold Q is determined as follows: The scene condition to which the most recent output power belongs is determined, including abnormal weather conditions, standard weather conditions, and foreign object obstruction conditions; the daily illumination time is divided into k stages according to illumination intensity, and the minimum value of the set of power averages from the previous three cycles in the same stage is taken, denoted as S. bmin The formula for calculating Q is: ； Where z is the environmental impact factor; P avg S represents the average power over each cycle; S is the maximum value of the set of actual temperature values ​​transmitted by the solar panels at time intervals T for the first three cycles of the same period. amax The formula for calculating P is: ； Where P is the temperature threshold; c is the influence factor of different materials; f is the average temperature borne by the solar panel during this period; and s0 is the light-receiving area of ​​the solar panel. The control module is used to process the solar panels that have been given warnings; the safety detection module is used to re-detect the processed solar photovoltaic panels. The output of the data acquisition module is connected to the input of the data processing module; the output of the data processing module is connected to the input of the anomaly warning module; the output of the anomaly warning module is connected to the input of the control module; and the output of the control module is connected to the input of the safety detection module.

2. The IoT-based intelligent solar photovoltaic detection system according to claim 1, characterized in that: The data acquisition module is used to collect the solar photovoltaic temperature and output voltage and current every time T with a fixed period of time T, and upload the collected data to the data processing module; where T is a constant.

3. The IoT-based intelligent solar photovoltaic detection system according to claim 2, characterized in that: The data processing module includes a storage unit, a power calculation unit, and a comparison unit; The storage unit is used to store three types of data: the maximum and minimum temperatures uploaded by different solar panels at time intervals T, denoted as T. max T min The maximum and minimum current values ​​transmitted through different solar panels at time intervals T are denoted as I. max and I min The maximum and minimum voltage values ​​transmitted between different solar panels at time intervals T are denoted as U. max and U min ; The power calculation unit calculates the average power per cycle based on the uploaded maximum and minimum current values ​​and maximum and minimum voltage values. The formula for calculating the average power per cycle is as follows: ； Among them, P avg The power average for each cycle; define a cycle index that includes the power average and the maximum temperature. The comparison unit calculates predicted values ​​for the periodic index based on corresponding historical data and compares them with the actual detection data for the corresponding time period. The specific steps are as follows: S3-1: Input based on the Hole phenomenon trend model, using a single solar panel as the unit: ； Among them, l t This is the estimated level for this cycle; b t This represents the predicted trend for this period; s t This is the output of the seasonal smoothing equation; x t α is the periodic index value for this period; m is the period length; α is the horizontal smoothing parameter; β is the trend smoothing parameter; γ is the seasonal smoothing parameter. S3-2: Calculate the predicted value of the cycle index for the next cycle: ； in, is the cycle index value for the next cycle; h is the number of days ahead of the forecast; S3-3: Divide the daily light exposure time into k stages according to light intensity, and form a set of periodic index prediction values ​​H for different stages according to the cycle, and compare them with the actual detection data.

4. The IoT-based intelligent solar photovoltaic detection system according to claim 3, characterized in that: The comparison with the actual detection data is achieved by performing a periodic index prediction value superposition calculation for each additional record. The result of each superposition calculation is saved and compared with the next uploaded data. When the absolute value of the difference between any periodic index prediction value and the actual detection value exceeds the threshold, an early warning is issued.

5. The IoT-based intelligent solar photovoltaic detection system according to claim 4, characterized in that: The control module is used to perform an emergency short circuit on the solar panels involved when it receives an early warning message, and at the same time remind the staff to conduct on-site inspections at the system front end.

6. A method for intelligent detection of solar photovoltaic power based on the Internet of Things (IoT), using the intelligent detection system for solar photovoltaic power based on the IoT as described in claim 1, characterized in that: The method includes the following steps: Step S100: Collect data on the temperature, output voltage, and current of the solar photovoltaic system; Step S200: Calculate the power of each solar panel in converting light energy into electrical energy. Based on the temperature and the calculated power, calculate the predicted values ​​of the two sets of data and save the data for comparison with the data of the next cycle in the current time period. Step S300: When the absolute value of the difference between the predicted power or temperature value and the actual detected value exceeds the threshold, an early warning is issued and a solution is proposed. Step S400: Process the solar panels for which warnings have been issued individually; Step S500: Test the processed solar photovoltaic system again.

7. The IoT-based intelligent detection method for solar photovoltaic systems according to claim 6, characterized in that: The process of re-detecting the processed solar photovoltaic data in step S500 refers to starting from step S100, collecting solar photovoltaic data again, and processing the data collected in the next cycle according to the method steps until no warning occurs.

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