Photovoltaic power generation efficiency evaluation optimization system based on Internet of Things

Abstract

The invention discloses a photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things. The photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things comprises a data acquisition module, a model construction module, a data analysis module and an optimization module. According to the method, factors influencing the photovoltaic power generation efficiency are divided into light-induced attenuation, heat-induced attenuation and electricity-induced attenuation; the method comprises the following steps: obtaining a light-induced attenuation evaluation model, a thermally-induced attenuation evaluation model and an electrically-induced attenuation evaluation model, obtaining a light-induced attenuation efficiency evaluation coefficient, a thermally-induced attenuation efficiency evaluation coefficient and an electrically-induced attenuation efficiency evaluation coefficient according to the models, and obtaining a photovoltaic power generation efficiency evaluation model according to the evaluation coefficients, thereby obtaining a photovoltaic power generation efficiency evaluation coefficient. The purpose of effectively improving the accuracy of the photovoltaic power generation efficiency evaluation model is achieved; the problem that the accuracy of a photovoltaic power generation efficiency evaluation model cannot be effectively improved in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things. Background Art

[0002] As renewable energy becomes more and more important, distributed photovoltaic power stations are springing up. However, the rapid growth of distributed photovoltaic power stations also brings challenges in management and operation. Distributed photovoltaic power stations are numerous and cover a wide range. Traditional technical routes are difficult to achieve full coverage, and the management and operation costs are too high. In order to improve the operating efficiency and management level of photovoltaic power generation systems and reduce operating costs, photovoltaic power generation efficiency evaluation and optimization systems have emerged.

[0003] The existing photovoltaic power generation efficiency evaluation system based on the Internet of Things adds a sensor module with security encryption authentication function to the inverter of the distributed photovoltaic power station to collect data and communicate, and then uses the background system for unified regulation. Through this technology, real-time monitoring, remote control, data analysis and evaluation, and operation optimization of the distributed photovoltaic power generation system can be achieved.

[0004] For example, the public invention application with publication number: CN116054738A discloses a solar power generation monitoring and management system and method for the Internet of Things, including: obtaining the solar radiation intensity of the weather in area A in real time, and comparing the solar radiation intensity in area A with the average solar radiation intensity required to be absorbed by the solar energy storage device obtained from historical data; combining the comparison result with the energy storage situation of the solar cell array, analyzing the relationship between the placement angle of the solar photovoltaic panel and the energy storage of the solar photovoltaic panel energy storage device, and adjusting the placement angle of the solar photovoltaic panel in real time; according to the energy storage situation of the solar cell array, combined with weather factors and the rated working voltage value of the electrical equipment, and implementing the transmission of electric energy of the light-controlled solar cell array; according to the energy storage situation of the solar cell array and the rated working voltage value of the electrical equipment, by analyzing and predicting the energy storage situation of the solar cell array and the length of time of the electrical equipment, issuing a warning signal, and taking manual power replenishment operations according to the warning signal.

[0005] For example, the invention disclosed in the publication number CN114884447A discloses a distributed solar power generation device and a power generation system based on the control of the Internet of Things, including: the distributed solar power generation device based on the control of the Internet of Things monitors the external light conditions in real time through the photosensitive sensor on the mounting frame, and transmits the light conditions to the NB-IoT communication center through the controller; when the communication center determines that the external light conditions are suitable, the control information is sent to the flip motor through the controller, and the transmission chain is driven to rotate by the flip motor, and then the shaft drives the multiple rotating frames to rotate a certain angle to obtain the best light angle; when strong convective weather occurs, the communication center controls the operation of the reversing motor through the controller, so that the shaft drives the multiple rotating frames to rotate to a position parallel to the wind direction, and then fully reduces the wind collision area of ​​the sailboard; when heavy rain and snow weather occurs, the communication center controls the operation of the reversing motor and the driving cylinder through the controller, and when the two driving telescopic rods at the same relative position are close to each other, the rain, snow, dust and other debris retained on the surface of the sailboard are cleaned through the push rod connection to avoid snow accumulation affecting lighting or damaging the photovoltaic sailboard.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, although the Internet of Things technology can monitor the operating status of the photovoltaic power generation system in real time in coordinating the photovoltaic power generation efficiency based on the Internet of Things, how to accurately obtain and analyze data under complex and changeable environmental factors is still a major problem. In addition, the temperature, light intensity, and capacitance and resistance of photovoltaic cells that affect the efficiency of photovoltaic power generation are difficult to accurately predict and control, which poses a challenge to the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things. The existing photovoltaic power generation efficiency evaluation lacks a detailed evaluation of each affected aspect, and there is a problem that the accuracy of the photovoltaic power generation efficiency calculation model cannot be improved. Summary of the invention

[0008] The embodiment of the present application solves the problem that the accuracy of the photovoltaic power generation efficiency evaluation model cannot be effectively improved in the prior art by providing a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things, thereby achieving the effect of effectively improving the accuracy of the photovoltaic power generation efficiency evaluation model.

[0009] The embodiment of the present application provides a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things, including: a data acquisition module, used to acquire optical-related data of light-induced attenuation, thermal-related data of heat-induced attenuation, and electrical-related data of electric attenuation; a model construction module, used to construct the obtained optical-related data of light-induced attenuation, thermal-related data of heat-induced attenuation, and electrical-related data of electric attenuation into a model to obtain a photovoltaic power generation efficiency evaluation model; a data analysis module, used to analyze the obtained photovoltaic power generation efficiency evaluation model, and evaluate different attenuation evaluation coefficients respectively through different attenuation photovoltaic power generation efficiency evaluation models, and evaluate the photovoltaic power generation efficiency evaluation coefficient through the photovoltaic power generation efficiency evaluation model; an optimization module, used to compare the photovoltaic power generation efficiency evaluation coefficient with the photovoltaic power generation efficiency evaluation threshold, and obtain a photovoltaic power generation efficiency optimization method.

[0010] Furthermore, the specific analysis process of the efficiency evaluation data of the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things is: different methods are adopted for the efficiency evaluation data of the photovoltaic power generation efficiency evaluation and optimization system with different attenuation coefficients, and the light-induced attenuation data, thermal-induced attenuation data, and electrical-induced attenuation data are considered according to different factors affecting the attenuation coefficient.

[0011] Furthermore, the efficiency evaluation optimization model of the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things includes: a light-induced attenuation evaluation model, a thermal-induced attenuation evaluation model, and an electrical attenuation evaluation model; the photovoltaic power generation efficiency evaluation data based on the Internet of Things includes: a light-induced attenuation evaluation coefficient, a thermal-induced attenuation evaluation coefficient, and an electrical attenuation evaluation coefficient.

[0012] Furthermore, the specific analysis process of respectively evaluating the corresponding photovoltaic power generation efficiency evaluation data based on the Internet of Things through the photovoltaic power generation efficiency evaluation model based on the Internet of Things is: evaluating according to the light-induced attenuation data through the light-induced attenuation evaluation model to obtain the light-induced attenuation evaluation coefficient; evaluating according to the thermal-induced attenuation data through the thermal-induced attenuation evaluation model to obtain the thermal-induced attenuation evaluation coefficient; evaluating according to the electrical attenuation data through the electrical attenuation evaluation model to obtain the electrical attenuation evaluation coefficient.

[0013] Furthermore, the specific analysis process for obtaining the photo-induced attenuation evaluation coefficient is: statistically extracting photo-induced attenuation optical related data that actually affects the photovoltaic power generation efficiency from the photo-induced attenuation data, including: initial photovoltaic cell output power, photo-induced attenuation coefficient, light intensity, battery performance decay rate, and battery performance at the initial moment; thereby analyzing and obtaining the photo-induced attenuation evaluation coefficient.

[0014] Furthermore, the specific analysis process for obtaining the thermal attenuation evaluation coefficient is: statistically extracting thermal attenuation thermal related data that actually affects the photovoltaic power generation efficiency from the thermal attenuation data, including: open circuit voltage at standard temperature, short circuit current at standard temperature, current temperature, and standard temperature; thereby analyzing and obtaining the thermal attenuation evaluation coefficient.

[0015] Furthermore, the specific analysis process for obtaining the electro-attenuation evaluation coefficient is: statistically extracting the electro-attenuation electrical related data that actually affects the photovoltaic power generation efficiency from the electro-attenuation data, including: the capacitance at the initial moment, the resistance at the initial moment, the power loss of the capacitance, and the power loss of the resistance; thereby analyzing and obtaining the electro-attenuation evaluation coefficient.

[0016] Furthermore, the specific analysis process of the efficiency evaluation optimization model of the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things is as follows: the efficiency evaluation optimization model includes: a light-induced attenuation evaluation model, a thermal-induced attenuation evaluation model, and an electrical attenuation evaluation model; the photovoltaic cell power under light-induced attenuation is obtained through the light-induced attenuation evaluation model; the photovoltaic cell power under thermal attenuation is obtained through the thermal-induced attenuation evaluation model; the photovoltaic cell power under electrical attenuation is obtained through the electrical attenuation evaluation model; the photovoltaic cell power under light-induced attenuation, the photovoltaic cell power under thermal attenuation, and the photovoltaic cell power under electrical attenuation are compared with the photovoltaic cell power in the original state to obtain the actual difference in the photovoltaic cell power under light-induced attenuation, the actual difference in the photovoltaic cell power under thermal attenuation, and the actual difference in the photovoltaic cell power under electrical attenuation, and then the evaluation coefficient under each attenuation is calculated according to the actual difference of the photovoltaic cell under each attenuation.

[0017] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0018] 1. Through the Internet of Things technology, the influencing factors of photovoltaic power generation under different attenuation conditions in photovoltaic power generation components are studied in detail, and the corresponding attenuation model is constructed to obtain the attenuation evaluation coefficient to obtain different collection results, thereby improving the accuracy of the photovoltaic power generation efficiency evaluation and optimization system, and effectively solving the problem that the existing technology cannot effectively improve the accuracy of the photovoltaic power generation efficiency evaluation and optimization system.

[0019] 2. The corresponding evaluation data are obtained through the photovoltaic power generation efficiency evaluation model, and the evaluation is performed from the aspects of light-induced attenuation, thermal-induced attenuation, and electrical attenuation, so as to more comprehensively obtain the impact of different attenuations on the power of photovoltaic cells. The power of photovoltaic cells under different attenuations is diverse, and the photovoltaic power generation efficiency evaluation system under different attenuations can be used to develop photovoltaic power generation components that are more targeted at different attenuations, thereby improving the comprehensiveness of the photovoltaic power generation efficiency evaluation data under different attenuations.

[0020] 3. The efficiency evaluation optimization model is corrected by using the photovoltaic power generation efficiency evaluation data under different attenuation conditions. The corrected model can more accurately calculate the impact of different attenuation conditions on photovoltaic power generation efficiency, which will help relevant researchers to optimize photovoltaic power generation components. The relevant research data obtained can more accurately reflect the actual situation, thereby improving the scientific nature of the photovoltaic power generation efficiency evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the structure of a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of the structure of a model building module of a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of the structure of the photovoltaic power generation efficiency evaluation coefficient in the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiment of the present application solves the problem in the prior art that the accuracy of the photovoltaic power generation efficiency evaluation and optimization system cannot be effectively improved by providing a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things. The efficiency evaluation optimization model is corrected by the photovoltaic power generation efficiency evaluation coefficient under different attenuation conditions, thereby achieving the effect of improving the accuracy of the photovoltaic power generation efficiency evaluation and optimization system.

[0025] The technical solution in the embodiment of the present application is to solve the above problem that the accuracy of the photovoltaic power generation efficiency evaluation and optimization system cannot be effectively improved. The overall idea is as follows:

[0026] By constructing a photovoltaic power generation efficiency evaluation model to obtain photovoltaic power generation efficiency evaluation data and correcting the efficiency evaluation optimization model, the accuracy of the photovoltaic power generation efficiency evaluation optimization system was achieved.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1As shown, it is a structural schematic diagram of a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application includes: a data acquisition module, which is used to obtain optical-related data of light-induced attenuation, thermal-related data of heat-induced attenuation, and electrical-related data of electric attenuation; a model construction module, which is used to construct the obtained optical-related data of light-induced attenuation, thermal-related data of heat-induced attenuation, and electrical-related data of electric attenuation into a model to obtain a photovoltaic power generation efficiency evaluation model; a data analysis module, which is used to analyze the obtained photovoltaic power generation efficiency evaluation model, and evaluate different attenuation evaluation coefficients respectively through different attenuation photovoltaic power generation efficiency evaluation models, and evaluate the photovoltaic power generation efficiency evaluation coefficient through the photovoltaic power generation efficiency evaluation model; an optimization module, which is used to compare the photovoltaic power generation efficiency evaluation coefficient with the photovoltaic power generation efficiency evaluation threshold, and obtain a photovoltaic power generation efficiency optimization method. In this embodiment, the factors affecting the photovoltaic power generation efficiency are divided into three aspects, namely, light-induced attenuation, thermal attenuation, and electrical attenuation. The various factors that affect the photovoltaic power generation efficiency, such as temperature, light intensity, dust, and capacitance and resistance of photovoltaic cells, are all included in these three aspects and are referred to by light-induced attenuation coefficient, thermal attenuation, and electrical attenuation. By monitoring the data affecting the three attenuation coefficients through sensors, efficiency evaluation optimization models of photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things corresponding to the three attenuation coefficients are constructed respectively, and efficiency evaluation data of photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things corresponding to the three attenuation coefficients are obtained respectively; according to the efficiency light-induced attenuation coefficient, thermal-induced attenuation and electrical-induced attenuation evaluation data of photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things, the original efficiency evaluation optimization model is corrected to obtain the efficiency evaluation optimization model of photovoltaic power generation efficiency evaluation optimization system based on the Internet of Things.

[0029] Furthermore, the photovoltaic power generation efficiency evaluation model includes: light-induced attenuation evaluation model, thermal-induced attenuation evaluation model, and electrical attenuation evaluation model; the photovoltaic power generation efficiency evaluation data based on the Internet of Things includes: light-induced attenuation evaluation coefficient, thermal-induced attenuation evaluation coefficient, and electrical attenuation evaluation coefficient.

[0030] In this embodiment, if Figure 2 , which is a schematic diagram of the structure of a photovoltaic power generation efficiency evaluation model of a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in an embodiment of the present application.

[0031] Furthermore, the specific analysis process for obtaining the photo-induced attenuation evaluation coefficient is as follows: statistically extracting the photo-induced attenuation optical related data that actually affects the efficiency of photovoltaic power generation, including: the rated output power of the photovoltaic cell, the spectral response degree, the photo-induced attenuation coefficient, the wavelength of light, the performance attenuation rate of the photovoltaic cell and the performance of the photovoltaic cell, thereby analyzing and obtaining the photo-induced attenuation evaluation coefficient.

[0032] In this embodiment, the light-induced decay evaluation coefficient is used to quantify the coefficient of the influence of light-induced decay on the output efficiency of photovoltaic cells. The light-induced decay evaluation coefficient obtained by analysis can be obtained using the following methods, including but not limited to: hardware equipment, photometer, spectroradiometer, dosimeter; professional software, using professional optical simulation software, such as Zemax, LightTools, TracePro, etc. These hardware software can calculate the evaluation coefficient based on climate conditions, terrain and other factors; through comparative analysis, a reference value can be obtained.

[0033] The light-induced decay evaluation coefficient is obtained from this analysis. The specific model constraints of the light-induced decay efficiency evaluation model are as follows:

[0034]

[0035] In the formula, a number of light intensity monitoring points are set for the photovoltaic power generation efficiency evaluation system and are numbered in sequence, d0 = 1, 2, ..., d, d0 represents the number of the light intensity monitoring point, d represents the total number of the light intensity monitoring points, a number of time monitoring points are set for the photovoltaic power generation efficiency evaluation system and are numbered in sequence, t0 = 1, 2, 3, ..., t, t0 represents the number of the time monitoring point, t represents the total number of the time monitoring points, ξ1 represents the light-induced attenuation evaluation coefficient, which is used to evaluate the influence of light-induced attenuation on photovoltaic power generation efficiency, P0 represents the rated output power of the photovoltaic cell, It represents the spectral response degree calculated by the light-induced attenuation coefficient at the t0th time monitoring point and the d0th light intensity monitoring point. δ represents the light-induced attenuation coefficient, A represents the wavelength of light, and ΔW represents the attenuation rate of the photovoltaic module. It represents the photovoltaic cell performance at the t0th time monitoring point and the d0th light intensity monitoring point.

[0036] The rated output power of a photovoltaic cell refers to the nominal power of a newly installed or newly manufactured photovoltaic module under standard test conditions. These standard test conditions are usually defined as: temperature of 25°C, solar intensity of 1000 watts / square meter, and spectral distribution of AM1.5G.

[0037] The photoinduced attenuation coefficient of a photovoltaic cell is a measure of the performance attenuation of a photovoltaic cell due to the absorption of light and the recombination of photogenerated charges under long-term illumination. This attenuation is usually caused by defects in the photovoltaic cell material or the recombination of photogenerated charges, which increase over time and cause the efficiency of the photovoltaic cell to decrease.

[0038] The spectral response of a photovoltaic cell refers to the absorption capacity and conversion efficiency of the photovoltaic cell for light of different wavelengths. Each photovoltaic cell material has its own specific band structure, which determines the cell's absorption cutoff frequency for light, that is, the wavelength range of light that the cell can effectively absorb, usually between 300-1100 nanometers.

[0039] The photovoltaic cell performance decay rate of photovoltaic cells refers to the speed at which the performance of photovoltaic cells decays over time during long-term use. In practical applications, the performance decay of photovoltaic cells is a complex process that may involve the interaction of multiple mechanisms.

[0040] It should be understood that in this example, the evaluation and analysis of photo-induced decay in a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things is performed by analyzing and quantifying the initial photovoltaic cell output power, photo-induced decay coefficient, light intensity, and photovoltaic cell performance decay rate to obtain the level of impact of photo-induced decay on photovoltaic power generation efficiency.

[0041] The thermal degradation related data that actually affect the efficiency of photovoltaic power generation are statistically extracted, including open circuit voltage and short circuit current, and the thermal degradation evaluation coefficient is obtained by analysis.

[0042] In this embodiment, the thermal attenuation evaluation coefficient is a coefficient that quantifies the effect of thermal attenuation on the output power of photovoltaic cells. The thermal attenuation evaluation coefficient obtained by analysis can be obtained in the following manner. Including but not limited to: hardware equipment, photovoltaic cell tester, electronic load, data acquisition system. Professional software, photovoltaic system simulation software, such as PVSyst, RETScreen and other software, can simulate the performance of photovoltaic cells under different environmental conditions and calculate open circuit voltage and short circuit current. These software usually require the input of photovoltaic cell parameters for simulation calculations. The evaluation coefficient is determined in combination with the experience and expertise of relevant staff. Field tests and demonstration projects are carried out to evaluate the actual impact of thermal attenuation on photovoltaic power generation efficiency.

[0043] The thermal attenuation evaluation coefficient is obtained from this analysis. The specific model constraints of the thermal attenuation efficiency evaluation model are as follows:

[0044]

[0045] In the formula, a number of temperature monitoring points are set and numbered in sequence, B0 = 1, 2, ..., B, B0 represents the number of the temperature monitoring point, B represents the total number of the temperature monitoring point, a number of time monitoring points are set for the photovoltaic power generation efficiency evaluation system, and they are numbered in sequence, C0 = 1, 2, 3, ..., C, C0 represents the number of the time monitoring point, C represents the total number of the time monitoring point, ξ2 represents the thermal attenuation evaluation coefficient, which is used to evaluate the influence of thermal attenuation on photovoltaic power generation efficiency, It represents the open circuit voltage at the C0th time monitoring point at standard temperature. It indicates the short-circuit current at the C0th time monitoring point at the standard temperature. It represents the open circuit voltage at the C0th time monitoring point under the B0th temperature monitoring point. It represents the short-circuit current at the C0th time monitoring point under the B0th temperature monitoring point. It indicates the standard temperature of the C0th time monitoring point under standard temperature. Indicates the temperature of the C0th time monitoring point under the B0th temperature monitoring point.

[0046] The open circuit voltage of a photovoltaic cell refers to the voltage generated across the photovoltaic cell under no-load conditions, that is, when the current is zero;

[0047] Under standard test conditions, usually defined as a PV cell surface temperature of 25°C and a standard solar intensity, the open circuit voltage is considered a standard reference value. Since the performance of PV cells changes with temperature, the open circuit voltage is no exception. Generally, the open circuit voltage decreases as the temperature increases. This is because temperature changes affect the conductivity of various semiconductor materials inside the PV cell and the diffusion and recombination process of carriers. Specifically, when the temperature rises, the carrier recombination in the PV cell increases, while the diffusion length shortens, and these factors together lead to a decrease in the open circuit voltage.

[0048] The short-circuit current of a photovoltaic cell refers to the maximum current that the photovoltaic cell can output when the voltage across the photovoltaic cell is zero, that is, the open-circuit voltage. Under standard test conditions, the short-circuit current is an important parameter that reflects the maximum output current capability of the photovoltaic cell under ideal lighting and temperature conditions; at standard temperature, that is, 25°C, the short-circuit current of a photovoltaic cell is usually its maximum current output value; however, similar to the open-circuit voltage, the short-circuit current will also change with temperature. Generally, the short-circuit current of a photovoltaic cell increases with increasing temperature. This is because the increase in temperature will increase the conductivity of the semiconductor material in the photovoltaic cell, thereby increasing the number and mobility of carriers.

[0049] It should be understood that in this example, the evaluation and analysis of thermal attenuation in a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things is quantified by analyzing the open-circuit voltage at standard temperature, the short-circuit current at standard temperature, the current temperature, and the standard temperature to obtain the statistical extraction of the electrical-related data of the electrical attenuation that actually affects the photovoltaic power generation efficiency under thermal attenuation, including capacitance, maximum power point of capacitance, equivalent series resistance, maximum power point of equivalent series resistance, power loss of capacitance, power loss of resistance, temperature coefficient of capacitance, and temperature coefficient of equivalent series resistance, thereby analyzing and obtaining the electrical attenuation evaluation coefficient.

[0050] In this embodiment, the electro-degradation evaluation coefficient is a coefficient that quantifies the effect of electro-degradation on the output power of the photovoltaic cell. The electro-degradation evaluation coefficient obtained by analysis can be obtained in the following manner. Including but not limited to: hardware equipment, inductance capacitance resistance measuring instrument, oscilloscope, impedance analyzer, multimeter, DC power supply and electronic load, volt-ampere characteristic tester. Professional software, photovoltaic system simulation software, such as PVSyst, RETScreen and other software, can measure the performance of photovoltaic cells and give estimated values ​​of parameters such as capacitance and resistance. Data analysis software, such as MATLAB, Python, etc., can be used to analyze the data measured by hardware equipment and perform more complex calculations and modeling. These software usually require the input of photovoltaic cell parameters for simulation calculations. The evaluation coefficient is determined in combination with the experience and expertise of relevant staff. Field tests and demonstration projects are carried out to evaluate the actual impact of electro-degradation on photovoltaic power generation efficiency.

[0051] The electro-degradation evaluation coefficient is obtained from this analysis. The specific model constraints of the electro-degradation efficiency evaluation model are as follows:

[0052]

[0053] In the formula, a number of time monitoring points are set for the photovoltaic power generation efficiency evaluation system and numbered in sequence, X0 = 1, 2, 3, ..., X, X0 represents the number of the time monitoring point, X represents the total number of the time monitoring point, ξ3 represents the electro-degradation evaluation coefficient, which is used to evaluate the impact of electro-degradation on photovoltaic power generation efficiency. represents the capacitance at the X0th time monitoring point, represents the equivalent series resistance of the X0th time monitoring point, Indicates the maximum power point of the capacitor at the X0th time monitoring point, Indicates the maximum power point of the equivalent series resistance at the X0th time monitoring point, μ F Indicates the capacitor power loss at the X0th time monitoring point, μ R It represents the equivalent series resistance power loss at the X0th time monitoring point, θ1 represents the temperature coefficient of the capacitor, and θ2 represents the temperature coefficient of the equivalent series resistance.

[0054] The equivalent series resistance of a photovoltaic cell refers to the resistance value inside the photovoltaic cell that is equivalent to the series resistance, which affects the load capacity and efficiency of the cell. A higher equivalent series resistance means that the voltage drop of the photovoltaic cell under a given load will be greater, which may lead to a decrease in the output power of the photovoltaic cell; the specific resistance value of the photovoltaic cell is usually provided in the data sheet of the photovoltaic cell or module, or can be obtained through experimental measurement.

[0055] The maximum power point of a photovoltaic cell refers to the maximum power output point that a photovoltaic cell can produce under certain environmental conditions. In a photovoltaic system, the maximum power point is very important because it represents the maximum power output level that the battery can achieve. The maximum power point of a photovoltaic cell is usually located at the intersection of the open circuit voltage and the short circuit current. At this point, the output power of the photovoltaic cell reaches its maximum value.

[0056] The temperature coefficient of a photovoltaic cell refers to the relationship between the performance parameters of a photovoltaic cell (such as open circuit voltage, short circuit current, peak power, etc.) and temperature. The temperature coefficient of a photovoltaic cell reflects the performance changes of the cell when the temperature changes. The temperature coefficient of a photovoltaic cell is usually negative, which means that as the temperature increases, the performance parameters of the cell decrease. Taking peak power as an example, the temperature coefficient of a photovoltaic cell is usually between -0.35% and -0.45%.

[0057] The capacitance loss power of photovoltaic cells is a parameter that describes the degradation of the capacitance characteristics of photovoltaic cells during long-term operation. The capacitance loss power usually refers to the ratio of the change of the equivalent series resistance of photovoltaic cells over time, which is used to evaluate the degree of aging of photovoltaic cells under the influence of environmental factors such as continuous light and temperature changes. The capacitance loss power can be determined by comparing the equivalent series resistance values ​​of photovoltaic cells before and after installation. If the capacitance loss power is high, it means that the equivalent series resistance value of the photovoltaic cell increases rapidly over time, which usually indicates that the performance of the photovoltaic cell is rapidly degrading. On the contrary, if the capacitance loss power is low, it indicates that the performance stability of the photovoltaic cell is good and the capacitance degradation phenomenon is not obvious. The capacitance loss power is an important parameter in the life assessment and reliability research of photovoltaic cells. It represents the aging characteristics of photovoltaic cells under different environmental conditions and is determined through special tests and analysis.

[0058] The resistance loss power of photovoltaic cells is a parameter used to describe the degradation of the resistance characteristics of photovoltaic cells during long-term operation. This coefficient is usually used to evaluate the performance degradation of photovoltaic cells under the influence of factors such as continuous illumination, temperature changes, and environmental aging.

[0059] The resistance loss power can be determined by comparing the equivalent series resistance or equivalent parallel resistance of the photovoltaic cell before and after installation and use. If the resistance loss power is high, it means that the resistance value of the photovoltaic cell increases rapidly over time, which usually indicates that the performance of the photovoltaic cell is rapidly degrading. On the contrary, if the resistance loss power is low, it indicates that the performance stability of the photovoltaic cell is good and the resistance degradation phenomenon is not obvious. The resistance degradation of photovoltaic cells is a complex process, which may involve multiple factors such as material aging, interface degradation, and changes in electrode materials. The resistance loss power is an important parameter in the life assessment and reliability research of photovoltaic cells.

[0060] It should be understood that in this example, the evaluation and analysis of electro-degradation in a photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things is performed by analyzing and quantifying the initial capacitance, the power loss of the capacitance, the equivalent series resistance, and the power loss of the resistance to obtain the level of impact of electro-degradation on photovoltaic power generation efficiency.

[0061] Furthermore, the specific analysis process of the photovoltaic power generation efficiency evaluation model includes the following constraints:

[0062]

[0063] Wherein, ω1 represents the power tolerance under light-induced attenuation, ω2 represents the power tolerance under thermal-induced attenuation, ω3 represents the power tolerance under electrical attenuation, ω4 represents the power tolerance under ideal conditions, P0 represents the rated output power of the photovoltaic cell, ξ1 represents the evaluation coefficient of light-induced attenuation, ξ2 represents the evaluation coefficient of thermal-induced attenuation, ξ3 represents the evaluation coefficient of electrical attenuation, and ξ4 represents the evaluation coefficient of photovoltaic power generation efficiency.

[0064] The power tolerance of photovoltaic cells refers to the deviation range between the nominal power and the actual measured power of photovoltaic cells during production and use. Power tolerance is an important indicator to measure the performance stability and manufacturing accuracy of photovoltaic cells.

[0065] The photovoltaic power generation efficiency evaluation coefficient is obtained by comprehensive evaluation of the light-induced attenuation evaluation coefficient, the thermal-induced attenuation evaluation coefficient and the electrical attenuation evaluation coefficient. The photovoltaic power generation efficiency evaluation coefficient reflects the comprehensive impact of light-induced attenuation, thermal-induced attenuation and electrical attenuation on the photovoltaic power generation efficiency.

[0066] In this embodiment, the photovoltaic power generation efficiency evaluation coefficient can also be obtained by the following methods, including but not limited to: professional software simulation, using professional photovoltaic system simulation software, the power generation efficiency of the photovoltaic system can be simulated and predicted according to factors such as the type of photovoltaic panels, installation angle, geographical location, and climatic conditions. Research papers and literature, research papers and literature in the photovoltaic field may also provide evaluation coefficients of photovoltaic power generation efficiency under different conditions.

[0067] Furthermore, the specific process of determining the photovoltaic power generation efficiency evaluation threshold through the optimization module is: according to the internationally recognized photovoltaic power generation efficiency standards and technical index requirements, the photovoltaic power generation efficiency evaluation threshold is determined, which is generally 20%.

[0068] In this embodiment, the photovoltaic power generation efficiency evaluation coefficient calculated by the model is compared with the photovoltaic power generation efficiency evaluation threshold. In the comparison results of the photoinduced attenuation coefficient, the thermal attenuation coefficient and the electrical attenuation coefficient with the photovoltaic power generation efficiency evaluation threshold, if the photoinduced attenuation coefficient deviates more from the photovoltaic power generation efficiency evaluation coefficient, the position of the photovoltaic cell can be replaced; if the thermal attenuation coefficient deviates more from the photovoltaic power generation efficiency evaluation coefficient, operations such as heat dissipation and cooling of the photovoltaic cell can be taken; if the electrical attenuation coefficient deviates more from the photovoltaic power generation efficiency evaluation coefficient, capacitors and resistors can be replaced.

[0069] It should be understood that if Figure 3 As shown, it is a structural schematic diagram of the photovoltaic power generation efficiency evaluation coefficient in the photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things provided in the embodiment of the present application. The photovoltaic power generation efficiency evaluation coefficient obtained in this embodiment is obtained by correcting the light-induced attenuation, thermal-induced attenuation and electrical attenuation. The light-induced attenuation evaluation coefficient, the thermal-induced attenuation evaluation coefficient and the electrical attenuation evaluation coefficient are all evaluation corrections for the photovoltaic power generation efficiency. However, the power generation of photovoltaic modules is diversified. Different types of photovoltaic modules have different spectral frequencies, capacitances and resistances, light-induced attenuation coefficients, etc., which will affect the final results. Therefore, the evaluation coefficient can be adjusted according to the actual photovoltaic modules, which can reduce some uncertainty and improve the accuracy of the system.

[0070] The technical scheme in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to a solar power generation monitoring management system and method of the Internet of Things disclosed in publication number: CN116054738A, the embodiment of the present application evaluates the three aspects that affect the photovoltaic power generation efficiency, and can more accurately study the photovoltaic power generation efficiency and thus optimize the photovoltaic power generation efficiency, thereby achieving the improvement of the comprehensiveness of the photovoltaic power generation efficiency evaluation data; relative to a distributed solar power generation device and power generation system based on Internet of Things control disclosed in publication number: CN114884447A, the embodiment of the present application corrects the photovoltaic power generation efficiency evaluation model through the photovoltaic power generation efficiency evaluation data to obtain the photovoltaic power generation efficiency evaluation coefficient, thereby making the photovoltaic power generation efficiency evaluation model more accurately reflect the actual situation, thereby achieving the improvement of the scientific nature of the photovoltaic power generation efficiency evaluation model.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things, characterized in that: include: A data acquisition module, used to acquire optical data related to light-induced attenuation, thermal data related to heat-induced attenuation, and electrical data related to electrical attenuation; A model building module is used to build a model from the obtained optical-related data of light-induced attenuation, thermal-related data of heat-induced attenuation and electrical-related data of electricity-induced attenuation to obtain a photovoltaic power generation efficiency evaluation model; A data analysis module is used to analyze the obtained photovoltaic power generation efficiency evaluation model, evaluate different attenuation evaluation coefficients through different attenuation photovoltaic power generation efficiency evaluation models, and evaluate the photovoltaic power generation efficiency evaluation coefficient through the photovoltaic power generation efficiency evaluation model; The optimization module is used to compare the photovoltaic power generation efficiency evaluation coefficient with the photovoltaic power generation efficiency evaluation threshold to obtain the photovoltaic power generation efficiency optimization method.

2. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The photovoltaic power generation efficiency evaluation model includes: a light-induced decay efficiency evaluation model, a thermal-induced decay efficiency evaluation model and an electrical-induced decay efficiency evaluation model.

3. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of obtaining the photovoltaic power generation efficiency evaluation coefficient through the photovoltaic power generation efficiency evaluation model is as follows: According to the light-induced attenuation efficiency evaluation coefficient, the thermal-induced attenuation efficiency evaluation coefficient and the electrical attenuation efficiency evaluation coefficient, the photovoltaic power generation efficiency evaluation coefficient is obtained through the photovoltaic power generation efficiency evaluation model.

4. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The photovoltaic power generation efficiency evaluation coefficient includes: a light-induced attenuation efficiency evaluation coefficient, a thermal-induced attenuation efficiency evaluation coefficient and an electrical-induced attenuation efficiency evaluation coefficient.

5. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of obtaining different attenuation evaluation coefficients by respectively evaluating different attenuation photovoltaic power generation efficiency evaluation models is as follows: According to the optical data related to light-induced decay, the light-induced decay efficiency evaluation model is used to evaluate the working efficiency of the photovoltaic cell, and the light-induced decay efficiency evaluation coefficient is obtained; According to the thermal data related to thermal attenuation, the thermal attenuation efficiency evaluation model is used to evaluate the working efficiency of the photovoltaic cell, and the thermal attenuation efficiency evaluation coefficient is obtained; According to the electrical related data of electro-degradation, the electro-degradation efficiency evaluation model is used for evaluation, and the working efficiency of the photovoltaic cell is evaluated by the electro-degradation efficiency evaluation model to obtain the electro-degradation efficiency evaluation coefficient.

6. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 5, characterized in that: The specific analysis process for obtaining the light-induced degradation efficiency evaluation coefficient is as follows: Statistically extract the photo-induced attenuation optical data that actually affects the efficiency of photovoltaic power generation, including: the rated output power of the photovoltaic cell, the spectral response degree, the photo-induced attenuation coefficient, the wavelength of light, the performance attenuation rate of the photovoltaic cell, and the performance of the photovoltaic cell; The light-induced decay evaluation coefficient is obtained from this analysis. The specific model constraints of the light-induced decay efficiency evaluation model are as follows: In the formula, a number of light intensity monitoring points are set for the photovoltaic power generation efficiency evaluation system and are numbered in sequence, d0 = 1, 2, ..., d, d0 represents the number of the light intensity monitoring point, d represents the total number of the light intensity monitoring points, a number of time monitoring points are set for the photovoltaic power generation efficiency evaluation system and are numbered in sequence, t0 = 1, 2, 3, ..., t, t0 represents the number of the time monitoring point, t represents the total number of the time monitoring points, ξ1 represents the light-induced attenuation evaluation coefficient, which is used to evaluate the influence of light-induced attenuation on photovoltaic power generation efficiency, P0 represents the rated output power of the photovoltaic cell, It represents the spectral response degree calculated by the light-induced attenuation coefficient at the t0th time monitoring point and the d0th light intensity monitoring point. δ represents the light-induced attenuation coefficient, A represents the wavelength of light, and ΔW represents the attenuation rate of the photovoltaic module. Represents the photovoltaic cell performance at the t0th time monitoring point and the d0th light intensity monitoring point.

7. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 5, characterized in that: The specific analysis process for obtaining the thermal degradation efficiency evaluation coefficient is as follows: Statistically extract thermal-related data that actually affect the efficiency of photovoltaic power generation, including open-circuit voltage and short-circuit current; The thermal attenuation evaluation coefficient is obtained from this analysis. The specific model constraints of the thermal attenuation efficiency evaluation model are as follows: In the formula, a number of temperature monitoring points are set and numbered in sequence, B0 = 1, 2, ..., B, B0 represents the number of the temperature monitoring point, B represents the total number of the temperature monitoring point, a number of time monitoring points are set for the photovoltaic power generation efficiency evaluation system, and they are numbered in sequence, C0 = 1, 2, 3, ..., C, C0 represents the number of the time monitoring point, C represents the total number of the time monitoring point, ξ2 represents the thermal attenuation evaluation coefficient, which is used to evaluate the influence of thermal attenuation on photovoltaic power generation efficiency, It represents the open circuit voltage at the C0th time monitoring point at standard temperature. It indicates the short-circuit current at the C0th time monitoring point at the standard temperature. It represents the open circuit voltage at the C0th time monitoring point under the B0th temperature monitoring point. It represents the short-circuit current at the C0th time monitoring point under the B0th temperature monitoring point. It indicates the standard temperature of the C0th time monitoring point under standard temperature. Indicates the temperature of the C0th time monitoring point under the B0th temperature monitoring point.

8. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 5, characterized in that: The specific analysis process for obtaining the electro-degradation efficiency evaluation coefficient is as follows: Statistically extract the electrical data related to the electro-attenuation that actually affects the efficiency of photovoltaic power generation, including capacitance, temperature coefficient of capacitance, equivalent series resistance, temperature coefficient of equivalent series resistance, power loss of capacitance and power loss of equivalent series resistance; The electro-degradation evaluation coefficient is obtained from this analysis. The specific model constraints of the electro-degradation efficiency evaluation model are as follows: In the formula, several time monitoring points are set and numbered in sequence, X0 = 1, 2, 3, ..., X, X0 represents the number of the time monitoring point, X represents the total number of the time monitoring point, ξ3 represents the electro-degradation evaluation coefficient, which is used to evaluate the influence of electro-degradation on photovoltaic power generation efficiency. represents the capacitance at the X0th time monitoring point, represents the equivalent series resistance of the X0th time monitoring point, represents the temperature coefficient of the capacitance at the X0th time monitoring point, represents the temperature coefficient of the equivalent series resistance at the X0th time monitoring point, Represents the capacitor power loss at the X0th time monitoring point, represents the equivalent series resistance loss power at the X0th time monitoring point, The weight factor representing the power loss of the capacitor, A weighting factor that represents the power loss in the equivalent series resistance.

9. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of the photovoltaic power generation efficiency evaluation model is as follows: According to different attenuation assessment correction data, the corresponding different attenuation calculation models are corrected. The specific calculation formula of the photovoltaic power generation efficiency assessment model is as follows: Wherein, ω1 represents the power tolerance under light-induced attenuation, ω2 represents the power tolerance under thermal-induced attenuation, ω3 represents the power tolerance under electrical attenuation, ω4 represents the power tolerance under ideal conditions, P0 represents the rated output power of the photovoltaic cell, ξ1 represents the evaluation coefficient of light-induced attenuation, ξ2 represents the evaluation coefficient of thermal-induced attenuation, ξ3 represents the evaluation coefficient of electrical attenuation, and ξ4 represents the evaluation coefficient of photovoltaic power generation efficiency.

10. The photovoltaic power generation efficiency evaluation and optimization system based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of the photovoltaic power generation efficiency optimization method is as follows: The photovoltaic power generation efficiency evaluation coefficient is obtained according to the photovoltaic power generation efficiency evaluation model, and the photovoltaic power generation efficiency evaluation coefficient is compared with the photovoltaic power generation efficiency evaluation threshold. If the two are closer, the optimization effect of the photovoltaic power generation efficiency evaluation optimization system is better.

Images