Generating capacity loss assessment method of photovoltaic system

Abstract

The invention discloses a generating capacity loss assessment method for a photovoltaic system, and relates to the technical field of photovoltaic power generation, and the method comprises the following steps: an MPPT control system monitors the output parameters of a photovoltaic module in real time, captures the output characteristics of the photovoltaic module in real time during illumination change through the obtained operation parameter information, and determines the output characteristics of the photovoltaic module; and the MPPT control system can sense the change of the illumination condition. By monitoring the sunlight penetration rate and the surface reflectance in real time, the MPPT control system can quickly recognize illumination changes and accurately adjust the working point of the photovoltaic module, it is ensured that the system is close to the maximum power point, and energy conversion can be optimized even in complex weather. The dynamic adaptability improves the overall generating capacity and economic benefits, and meanwhile, by comparing an external environment evaluation index with a reference threshold value, a control strategy is optimized, power fluctuation is reduced, the voltage and frequency stability of a power grid is maintained, the coordination of a photovoltaic system and the power grid is enhanced, and the reliability and sustainable development of renewable energy sources are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method for evaluating power generation loss of a photovoltaic system. Background Art

[0002] The power generation loss assessment of the photovoltaic system is to determine the power loss of the system in actual operation relative to ideal conditions by analyzing and quantifying various factors that affect the efficiency of photovoltaic power generation. In theory, photovoltaic power generation is determined by factors such as solar radiation, conversion efficiency of photovoltaic modules and system configuration, but in actual operation, environmental conditions (such as temperature, dust, and shading) and equipment performance degradation (such as module aging and reduced inverter efficiency) will cause the actual power generation to be lower than expected. By comparing the actual monitored data with the theoretically calculated power generation, the specific loss of power generation can be evaluated. The monitoring data usually includes environmental factors such as irradiance and temperature, as well as equipment status such as the output power of photovoltaic modules and inverter efficiency. Through these data analysis, the operator can find the main causes of power generation loss, such as decreased efficiency due to increased temperature, reduced power due to dust coverage, equipment aging or failure, etc., and optimize the system design and maintenance strategy accordingly.

[0003] The prior art has the following deficiencies: Photovoltaic systems achieve maximum power output under different lighting conditions through maximum power point tracking (MPPT). MPPT is a key control function in photovoltaic systems, but in rapidly changing weather conditions, MPPT control systems face the challenge of how to strike a balance between rapid adjustment and system stability. When the controller frequently adjusts the operating point, especially when the weather changes drastically, the system power output will fluctuate violently. This not only weakens the power generation stability of the photovoltaic system, but may also have a negative impact on the grid-connected power grid. Since the grid needs to maintain a balance between supply and demand, frequent fluctuations in the photovoltaic system may cause abnormal voltage or frequency in the local grid, endangering the stability of the grid. Once such fluctuations exceed the range allowed by the grid, the grid operator may take measures to limit the output of the photovoltaic power station or even force it to temporarily disconnect from the grid. For photovoltaic power stations that rely on grid connection, this means direct power generation losses and economic damage.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for assessing the power generation loss of a photovoltaic system. By real-time monitoring of sunlight penetration and surface reflectivity, the MPPT control system can quickly identify changes in illumination and accurately adjust the working point of the photovoltaic module, thereby ensuring that the system always operates close to the maximum power point and can achieve the best energy conversion rate even under complex weather conditions. This dynamic adaptability not only improves the overall power generation and economic benefits of the photovoltaic system, but also optimizes the control strategy by comparing the external environment assessment index with the reference threshold, reduces power output fluctuations, maintains the voltage and frequency stability of the power grid, enhances the coordination between the photovoltaic system and the power grid, and supports the reliability and sustainable development of renewable energy to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: comprising the following steps: The MPPT control system monitors the output parameters of the photovoltaic modules in real time. By acquiring the operating parameter information, it captures the output characteristics of the photovoltaic modules when the light changes in real time, so that the MPPT control system can sense the changes in light conditions. While obtaining the output parameter information of the photovoltaic modules, the external environment information related to photovoltaic power generation is collected, the collected external environment raw data is processed, the key features that affect the power generation performance of the photovoltaic system are identified, and the identified key features are analyzed and processed. The analyzed and processed key features are input into the pre-learned machine model, and the prediction results of future environmental changes are generated through the analysis of the model; Based on the analysis results of the machine learning model, the external environment changes are divided into rapid environmental changes and normal environmental changes; For normal environmental changes, the photovoltaic system continues to adjust the operating point using the set adjustment method. In the case of rapid environmental changes, the photovoltaic system needs to quickly adapt to changes in external conditions based on the adjustment method of normal environmental changes. The MPPT control system will apply intelligent adjustment strategies and dynamically adjust the operating point frequency of the photovoltaic system through a real-time feedback mechanism.

[0007] Preferably, the MPPT control system captures the output characteristics of the photovoltaic module when the light changes in real time and senses the changes in the light conditions according to the acquired operating parameter information through the following steps: The MPPT control system is equipped with sensors and data acquisition modules to continuously monitor the output parameters of the PV modules; The MPPT control system records the output parameters in real time and generates an output characteristic curve; By comparing and analyzing the real-time output characteristic curves, the MPPT control system can identify the characteristics of light changes.

[0008] Preferably, the extracted external environmental characteristics of the photovoltaic system's power generation stability include the ability of sunlight to penetrate the atmosphere and the ability of the ground surface to reflect solar radiation. After extraction, the ability of sunlight to penetrate the atmosphere and the ability of the ground surface to reflect solar radiation are placed under a detection window for anomaly analysis to generate an atmospheric transmittance index and a surface reflectivity index, respectively.

[0009] Preferably, the analyzed and sorted atmospheric transmittance index and surface reflectivity index are input into a pre-learned machine model, an external environment evaluation index is generated by the machine model, and the external environment change state is predicted based on the external environment evaluation index.

[0010] Preferably, the external environment evaluation index generated based on the machine learning model is compared and analyzed with the preset reference threshold of the external environment evaluation index, and the changes in the external environment are divided into categories. The specific divisions are as follows: If the external environment evaluation index is greater than or equal to a preset external environment evaluation index reference threshold, the current external environment change situation is classified as a rapid environment change state; If the external environment assessment index is less than a preset external environment assessment index reference threshold, the current external environment change situation is classified as a normal environment change state.

[0011] Preferably, under the detection window, the ability of sunlight to penetrate the atmosphere is analyzed abnormally, and the logic of generating the atmospheric transmittance index is as follows: In the detection window, the data on solar radiation and atmospheric conditions are obtained through monitoring equipment, and the total irradiance is calculated. The calculation expression is as follows: , where is the total irradiance, is the direct irradiance, represents the diffuse irradiance; In the absence of atmospheric influence, the ideal light intensity is calculated using the following expression: , where represents the light intensity under ideal conditions, Indicates the irradiance under theoretical standard conditions, is the angle between the sun and the horizontal plane; Evaluate the impact of the atmosphere on solar radiation, determine the ratio between actual irradiance and ideal irradiance, and calculate the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance; The atmospheric transmittance index is generated by the atmospheric transmittance, and the calculation expression is as follows: , where represents the atmospheric transmittance index, is the absorption coefficient Represents the supplementary part of atmospheric transmittance.

[0012] Preferably, under the detection window, the steps of performing an impact analysis on the surface reflectivity and generating a surface reflectivity index are as follows: The surface reflectivity is calculated by the incident solar radiation intensity and the reflected light intensity. The calculation expression is as follows: , where Represents the intensity of reflected light, is the intensity of incident solar radiation, Represents the surface reflectivity; Adjust the surface reflectivity to eliminate potential environmental interference factors. The calculation expression is as follows: , where represents the adjusted surface reflectivity, represents the humidity coefficient, is the current ambient temperature, is the reference temperature, is the temperature sensitivity coefficient; Convert the adjusted surface reflectance into the final surface reflectance index, , where represents the surface reflectivity index, is the enhancement factor of the surface reflectivity index.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: By real-time monitoring and analysis of external environmental characteristics, such as sunlight penetration and surface reflectivity, the MPPT control system can quickly identify changes in illumination and accurately adjust the operating point of the photovoltaic module. This dynamic adaptability ensures that the system always operates close to the maximum power point, achieving the best energy conversion rate even in complex weather conditions. This efficiency improvement not only increases the overall power generation of the photovoltaic system, but also helps improve economic benefits and ensure maximum return on investment.

[0014] By comparing the external environment assessment index with the reference threshold, the system can effectively identify the rapid environmental changes and normal environmental changes, thereby adjusting the control strategy. In a rapidly changing environment, the application of intelligent adjustment strategies can reduce the drastic fluctuations in power output, reduce the impact on the grid-connected power grid, and maintain the voltage and frequency stability of the grid. This not only reduces the power rationing demand of the grid operator for photovoltaic power stations, but also enhances the coordination between the photovoltaic system and the grid, ensuring the reliability and safety of renewable energy, thereby supporting the goal of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 The present invention is a flowchart of a method for assessing power generation loss of a photovoltaic system. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0018] The present invention provides Figure 1 A method for assessing power generation loss of a photovoltaic system is shown, comprising the following steps: The MPPT control system monitors the output parameters of the photovoltaic modules in real time. By acquiring the operating parameter information, it captures the output characteristics of the photovoltaic modules when the light changes in real time, so that the MPPT control system can sense the changes in light conditions. The MPPT control system uses the following steps to capture the output characteristics of the photovoltaic modules in real time when the light changes, and sense the changes in light conditions based on the acquired operating parameter information: The MPPT control system is equipped with sensors and data acquisition modules to continuously monitor the output parameters of the PV modules; The output parameters of photovoltaic modules mainly include output voltage, output current, output power, temperature and irradiance. Through high-frequency data acquisition, the controller can obtain the real-time performance of photovoltaic modules under various lighting conditions; The MPPT control system records the output parameters in real time and generates output characteristic curves (IV curve and PV curve); The output characteristic curves (IV curve and PV curve) of photovoltaic modules are key graphics that reflect their performance. Under different light intensity and temperature conditions, the output characteristics of photovoltaic modules will change. The MPPT control system records these output parameters in real time and generates the current IV curve and PV curve, which enables the controller to analyze the working status of the photovoltaic module in real time and identify its maximum power point under the current light conditions; By comparing and analyzing the real-time output characteristic curves, the MPPT control system can identify the characteristics of light changes; When clouds move quickly, the irradiance will decrease rapidly, causing the output power of the photovoltaic module to drop. The MPPT control system determines whether there is a sudden change in the lighting conditions by observing the rate of change of the output power. Once a change in the lighting conditions is detected, the controller can quickly adjust the working point to ensure that the system can still operate at a position close to the maximum power output under the new lighting conditions; While obtaining the output parameter information of the photovoltaic modules, the external environment information related to photovoltaic power generation is collected, the collected external environment raw data is processed, the key features that affect the power generation performance of the photovoltaic system are identified, and the identified key features are analyzed and processed. The analyzed and processed key features are input into the pre-learned machine model, and the prediction results of future environmental changes are generated through the analysis of the model; The extracted external environmental characteristics of photovoltaic system power generation stability include the ability of sunlight to penetrate the atmosphere and the ability of the ground to reflect solar radiation. After extraction, the ability of sunlight to penetrate the atmosphere and the ability of the ground to reflect solar radiation are placed under the detection window for abnormal analysis, and the atmospheric transmittance index and the ground reflectance index are generated respectively. The analyzed atmospheric transmittance index and the ground reflectance index are input into the pre-learned machine model, and the external environment evaluation index is generated through the machine model. The external environment change state is predicted based on the external environment evaluation index; The impact of changes in the atmospheric transmittance index in the external environment on the photovoltaic system cannot be ignored; when the atmospheric transmittance decreases, the ability of sunlight to penetrate the atmosphere is weakened, and the light intensity received by the photovoltaic modules will drop sharply; in this case, the MPPT control system will quickly detect the change in output power and frequently adjust the working point to track the new maximum power point; however, frequent adjustments may lead to instability in the system power output, which manifests as violent fluctuations; this not only weakens the power generation stability of the photovoltaic system, but may also have a negative impact on the grid-connected power grid; the grid operator needs to monitor the supply and demand relationship in real time, and the power fluctuation of the photovoltaic system will cause abnormal fluctuations in the grid voltage and frequency, affecting the overall stability of the grid; in addition, if such fluctuations exceed the allowable range of the grid, it may force the grid operator to take measures to limit the output power of the photovoltaic power station, or even temporarily disconnect from the grid, which directly leads to loss of power generation and economic damage; Under the detection window, the ability of sunlight to penetrate the atmosphere is analyzed abnormally, and the logic of generating the atmospheric transmittance index is as follows: In the detection window, the data on solar radiation and atmospheric conditions are obtained through monitoring equipment, and the total irradiance is calculated. The calculation expression is as follows: , where Represents total irradiance, which is the total solar radiation energy received by the photovoltaic module. Indicates direct irradiance, which is the radiation energy directly transmitted from the solar light source to the surface of the photovoltaic module. represents diffuse irradiance, which is the radiation generated by atmospheric scattering; It should be noted that obtaining data about solar radiation and atmospheric conditions through sensors and monitoring equipment is an important part of photovoltaic system optimization and performance evaluation. The following is a detailed description of the steps in the acquisition process, as well as the types of commonly used sensors and equipment; 1. Solar radiation monitoring a. Solar radiation sensor (radiometer) Function: Used to measure the intensity of solar radiation reaching the ground; type: Spectroradiometer: measures radiation at different wavelengths to assess spectral properties; Pyranometer: measures the sum of direct and diffuse radiation, often used to calculate photovoltaic power generation potential; b. Installation location Usually installed above or near the photovoltaic system, ensure that the sensor is not blocked by shadows and can receive direct sunlight; 2. Environmental Condition Monitoring a. Temperature sensor Function: measure the ambient temperature and the temperature of photovoltaic modules; Type: thermocouple, thermal resistor, etc. b. Humidity sensor Function: Monitor relative humidity to assess its impact on photovoltaic performance; Type: capacitive humidity sensor, impedance humidity sensor; c. Wind speed and direction sensor Function: Measure wind speed and direction, which have an important impact on component heat dissipation and cooling effect; Type: anemometer, wind vane; 3. Atmospheric condition monitoring a. Atmospheric pressure sensor Function: Measure ambient atmospheric pressure, which affects radiation propagation and lighting conditions; Type: pressure sensor, barometer; b. Particle monitoring sensor Function: Monitor the concentration of PM2.5 and PM10 particles in the air, and evaluate the air quality and its impact on photovoltaic performance; Type: Laser particle counter, optical sensor; In the absence of atmospheric influence, the ideal light intensity is calculated using the following expression: , where represents the light intensity under ideal conditions, Indicates the theoretical irradiance under standard conditions, usually 1000W / m². is the angle between the sun and the horizontal plane (the angle of incidence); It should be noted that the atmospheric impact is mainly reflected in the following aspects: 1. Scattering: Gases and particles in the atmosphere scatter sunlight, causing the intensity of direct radiation to decrease; this scattering makes the light more uniform, forming scattered radiation; 2. Absorption: Gases such as water vapor and carbon dioxide in the atmosphere absorb light of specific wavelengths, reducing the intensity of radiation reaching the ground, especially in the infrared and ultraviolet bands; 3. Reflection: Some of the solar radiation is reflected back into space by clouds or the ground, especially in cloudy or overcast conditions; 4. Aerosols: Tiny particles in the atmosphere (such as dust and pollutants) can also affect the propagation of light and may lead to a reduction in radiation intensity; Ideal light intensity Ideal light intensity refers to the maximum solar radiation intensity that a photovoltaic module can receive without any atmospheric influence. It is usually expressed as standard solar irradiance \( I_{sc} \), which is usually 1000 W / m². This value is measured under standard conditions (such as clear sky, direct sunlight) and is the benchmark for photovoltaic module design and performance evaluation. When evaluating the performance of a photovoltaic system, it is critical to understand the atmospheric effects and ideal light intensity, which helps to identify and optimize power generation efficiency. By comparing the actual received irradiance with the ideal irradiance, it is possible to better understand how the system performs under different environmental conditions. Evaluate the impact of the atmosphere on solar radiation, determine the ratio between actual irradiance and ideal irradiance, and calculate the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance; The atmospheric transmittance index is generated by the atmospheric transmittance, and the calculation expression is as follows: , where represents the atmospheric transmittance index, is the absorption coefficient, which is a parameter that characterizes the intensity of radiation absorption in the atmosphere. It represents the supplementary part of atmospheric transmittance; In the detection window, the atmospheric transmittance index calculation expression shows that the larger the atmospheric transmittance index performance value, it usually means that the photovoltaic system frequently adjusts the working point under unfavorable climatic conditions (such as cloudy, haze, etc.), which leads to the instability of the system power output. In this case, the power generation fluctuation of the photovoltaic system will directly affect the stability of the grid-connected power grid and increase the probability of abnormal voltage or frequency of the local grid. On the contrary, the smaller the atmospheric transmittance index performance value, it indicates that the photovoltaic system operates under relatively stable lighting conditions, and the frequency of working point adjustment is low, which helps to maintain the stability of the grid and reduce the risk of abnormal voltage or frequency of the local grid. Changes in the surface reflectivity in the external environment have a significant impact on the power generation stability of the photovoltaic system. When the surface reflectivity changes, especially in cloudy or rainy weather conditions, the angle and intensity of the reflected light may fluctuate sharply, which causes the effective light intensity received by the photovoltaic modules to change instantly. At this time, the MPPT control system will frequently adjust the working point because it cannot adapt to these changes in real time, causing the system power output to fluctuate violently. This fluctuation not only weakens the power generation stability of the photovoltaic system, but may also have a negative impact on the grid-connected power grid. Because the grid needs to maintain a balance between supply and demand, the frequent fluctuations in photovoltaic power generation may lead to unstable voltage and frequency, which may cause grid overload or system failure. In extreme cases, the grid operator may limit the output power of the photovoltaic power station or even force it to go offline, causing direct power generation losses and economic damage. Therefore, the impact of changes in surface reflectivity on the photovoltaic system is not only a technical challenge, but also related to the broader security and stability of the grid. In the detection window, the impact analysis of the surface reflectivity is carried out and the steps to generate the surface reflectivity index are as follows: The surface reflectivity is calculated by the incident solar radiation intensity and the reflected light intensity. The calculation expression is as follows: , where Represents the intensity of reflected light, is the intensity of incident solar radiation, Represents the surface reflectivity; The incident solar radiation intensity refers to the solar radiation energy received per unit area, usually measured in watts per square meter (W / m²); this parameter reflects the energy intensity of solar radiation reaching the surface after passing through the atmosphere; the incident solar radiation intensity is affected by many factors, including the position of the sun (such as time and season), atmospheric conditions (such as clouds, pollutants), geographical location and topography; high incident solar radiation intensity means that the photovoltaic system can generate more electricity; Reflected light intensity refers to the amount of light radiation energy reflected from the ground or other surfaces back into the atmosphere, also measured in watts per square meter (W / m²); reflected light intensity depends on the properties of the surface (such as color, material and roughness) and the intensity and angle of the incident light; different surfaces have different reflectivity to light, smooth, light-colored surfaces generally have higher reflectivity, while rough, dark-colored surfaces absorb more light; reflected light intensity is an important parameter in optics, environmental monitoring and photovoltaic applications, because it affects the actual power generation capacity of photovoltaic modules and the overall energy balance; Adjust the surface reflectivity to eliminate potential environmental interference factors. The calculation expression is as follows: , where represents the adjusted surface reflectivity, represents the humidity coefficient, is the current ambient temperature, is the reference temperature, is the temperature sensitivity coefficient; It should be noted that the potential environmental interference is as follows: 1. Humidity: Impact: When humidity is high, water vapor will scatter light, resulting in a reduction in the effective light intensity received by the photovoltaic system, thus affecting power generation efficiency; 2. Temperature: Impact: High temperature will cause the efficiency of photovoltaic modules to decrease, and the reflectivity and absorption rate of the materials may also change, affecting the overall power generation capacity; 3. Dust and pollutants: Impact: Dust and contaminants on the ground or on PV panels can block sunlight, reduce available solar radiation, and thus reduce power generation; 4. Clouds and weather changes: Impact: Rapidly changing weather can cause dramatic fluctuations in light intensity, affecting the stability and power generation capacity of photovoltaic systems; 5. Surface reflection characteristics: Impact: Differences in reflectivity of different surfaces (such as soil, vegetation, and water bodies) will affect the reflection and scattering of incident light, which in turn affects the performance of photovoltaic systems; 6. Angle and direction: Impact: The installation angle and orientation of the photovoltaic module have a direct impact on the effectiveness of receiving light. Different angles may lead to uneven light reception; The reference temperature is usually obtained in the following ways: 1. Weather station data: Method: Obtain long-term climate data from a nearby weather station, using a specific time period (e.g., annual mean or seasonal mean) as reference temperature; 2. Historical temperature data: Methods: Analyze historical temperature data and select the average temperature under specific climate conditions as the reference temperature; 3. Experimental measurement: Methods: Experiments were conducted in a controlled environment to measure the performance of PV modules under different temperature conditions and an ideal temperature value was selected as a reference; 4. Standard reference conditions: Method: Set the reference temperature according to industry standards (such as the International Electrotechnical Commission IEC) or national standards (such as national solar energy standards), usually 25°C; These methods ensure that the reference temperature reflects the changes in the actual environment, making it more accurate and practical in calculations and analyses; Convert the adjusted surface reflectance into the final surface reflectance index, , where represents the surface reflectivity index, is the enhancement factor of the surface reflectivity index; In the detection window, the surface reflectivity index calculation expression shows that the larger the surface reflectivity index performance value, the stronger the surface's ability to reflect incident light. This may cause the photovoltaic controller to frequently adjust the working point to adapt to the changing light conditions. Such frequent adjustments will cause drastic fluctuations in the system power output, thereby increasing the probability of abnormal voltage or frequency in the local power grid. On the contrary, if the surface reflectivity index performance value is smaller, it means that the light conditions are relatively stable, and the controller adjustment frequency is reduced, making the system power output more stable, so that the probability of abnormal voltage and frequency in the local power grid is also reduced. The machine learning model is not specifically limited here, and can achieve the atmospheric transmittance index and surface reflectivity index Conduct comprehensive analysis to generate external environment assessment index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; External Environment Assessment Index The resulting calculation formula is: , where , Atmospheric transmittance index and surface reflectivity index The preset scaling factor of , All are greater than 0; It can be seen from the calculation expression of the external environment assessment index that the larger the atmospheric transmittance index performance value generated after the atmospheric transmittance anomaly analysis, the larger the surface reflectance index performance value generated after the surface reflectance anomaly analysis, indicating that the external environment changes rapidly, and the MPPT control system needs to frequently adjust the working point, resulting in a greater possibility of frequent fluctuations in the photovoltaic system. On the contrary, it means that the external environment is a normal change, and the MPPT control system does not need to frequently adjust the working point; Based on the analysis results of the machine learning model, the external environment changes are divided into rapid environment change states and normal environment change states; The external environment assessment index generated based on the machine learning model is compared and analyzed with the pre-set reference threshold of the external environment assessment index, and the changes in the external environment are divided into the following categories: If the external environment evaluation index is greater than or equal to a preset external environment evaluation index reference threshold, the current external environment change situation is classified as a rapid environment change state; If the external environment assessment index is less than a preset external environment assessment index reference threshold, the current external environment change situation is classified as a normal environment change state.

[0019] For normal environmental changes, the photovoltaic system continues to adjust the working point using the set adjustment method. In the case of rapid environmental changes, the photovoltaic system needs to quickly adapt to changes in external conditions based on the adjustment method of normal environmental changes. The MPPT control system will apply intelligent adjustment strategies and dynamically adjust the working point frequency of the photovoltaic system through a real-time feedback mechanism. For normal environmental changes, the photovoltaic system continuously adjusts its operating point according to the preset regulation strategy, which means that the system can respond to these changes in a timely manner within the range of changes in light, temperature and other environmental conditions to maintain the optimal power generation efficiency; this adjustment is based on historical data and algorithm models to ensure that when the environmental changes are not drastic, the system can stably track the maximum power point, thereby achieving efficient power generation; in this process, the system uses maximum power point tracking (MPPT) technology to dynamically monitor output parameters, optimize the working state, and ensure that the photovoltaic modules are always in the best working conditions to maximize energy conversion efficiency and reduce power generation losses; In the case of rapid environmental changes, the photovoltaic system needs to be adjusted based on the normal environmental changes. The steps to quickly adapt to changes in external conditions are as follows: The adjustment strategy coefficient is calculated based on the external environment assessment index and the reference threshold of the external environment assessment index to dynamically adjust the working point frequency of the photovoltaic system. The calculation expression is as follows: , where represents the adjustment strategy coefficient, represents the reference threshold of the external environment assessment index, , is the adjustment coefficient, ranging from 0.01 to 0.1. is the current power output; After calculating the adjustment strategy coefficient, the photovoltaic system will dynamically adjust its operating point according to the coefficient to optimize energy output. During specific execution, the controller will apply the adjustment coefficient to the current power output and calculate the adjusted power target; then, the system will gradually adjust the working state of the photovoltaic module according to the real-time monitored environmental parameters and output performance to ensure that it achieves the best operating efficiency under the new light and temperature conditions. By real-time monitoring and analysis of external environmental characteristics, such as sunlight transmittance and surface reflectivity, the MPPT control system of the present invention can quickly identify changes in light and accurately adjust the working point of the photovoltaic module. This dynamic adaptability ensures that the system always operates close to the maximum power point and achieves the best energy conversion rate even in complex weather conditions. This efficiency improvement not only increases the overall power generation of the photovoltaic system, but also helps to improve economic benefits and ensure maximum return on investment.

[0020] By comparing the external environment assessment index with the reference threshold, the system can effectively identify the rapid environmental changes and normal environmental changes, thereby adjusting the control strategy. In a rapidly changing environment, the application of intelligent adjustment strategies can reduce the drastic fluctuations in power output, reduce the impact on the grid-connected power grid, and maintain the voltage and frequency stability of the grid. This not only reduces the power rationing demand of the grid operator for photovoltaic power stations, but also enhances the coordination between the photovoltaic system and the grid, ensuring the reliability and safety of renewable energy, thereby supporting the goal of sustainable development.

[0021] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for assessing power generation loss of a photovoltaic system, characterized in that: The following steps are involved: The MPPT control system monitors the output parameters of the photovoltaic modules in real time. By acquiring the operating parameter information, it captures the output characteristics of the photovoltaic modules when the light changes in real time, so that the MPPT control system can sense the changes in light conditions. While obtaining the output parameter information of the photovoltaic modules, the external environment information related to photovoltaic power generation is collected, the collected external environment raw data is processed, the key features that affect the power generation performance of the photovoltaic system are identified, and the identified key features are analyzed and processed. The analyzed and processed key features are input into the pre-learned machine model, and the prediction results of future environmental changes are generated through the analysis of the model; Based on the analysis results of the machine learning model, the external environment changes are divided into rapid environmental changes and normal environmental changes; For normal environmental changes, the photovoltaic system continues to adjust the operating point using the set adjustment method. In the case of rapid environmental changes, the photovoltaic system needs to quickly adapt to changes in external conditions based on the adjustment method of normal environmental changes. The MPPT control system will apply intelligent adjustment strategies and dynamically adjust the operating point frequency of the photovoltaic system through a real-time feedback mechanism.

2. A method for assessing power generation loss of a photovoltaic system according to claim 1, characterized in that: The MPPT control system uses the following steps to capture the output characteristics of the photovoltaic modules in real time when the light changes, and sense the changes in light conditions based on the acquired operating parameter information: The MPPT control system is equipped with sensors and data acquisition modules to continuously monitor the output parameters of the PV modules; The MPPT control system records the output parameters in real time and generates an output characteristic curve; By comparing and analyzing the real-time output characteristic curves, the MPPT control system can identify the characteristics of light changes.

3. A method for assessing power generation loss of a photovoltaic system according to claim 1, characterized in that: The extracted external environmental characteristics of the photovoltaic system's power generation stability include the ability of sunlight to penetrate the atmosphere and the ability of the ground surface to reflect solar radiation. After extraction, the ability of sunlight to penetrate the atmosphere and the ability of the ground surface to reflect solar radiation are placed under the detection window for anomaly analysis to generate the atmospheric transmittance index and the surface reflectance index, respectively.

4. A method for assessing power generation loss of a photovoltaic system according to claim 3, characterized in that: The analyzed atmospheric transmittance index and surface reflectivity index are input into the pre-learned machine model, and the external environment evaluation index is generated through the machine model. The external environment change state is predicted based on the external environment evaluation index.

5. A method for assessing power generation loss of a photovoltaic system according to claim 4, characterized in that: The external environment assessment index generated based on the machine learning model is compared and analyzed with the pre-set reference threshold of the external environment assessment index, and the changes in the external environment are divided into the following categories: If the external environment evaluation index is greater than or equal to a preset external environment evaluation index reference threshold, the current external environment change situation is classified as a rapid environment change state; If the external environment assessment index is less than a preset external environment assessment index reference threshold, the current external environment change situation is classified as a normal environment change state.

6. A method for assessing power generation loss of a photovoltaic system according to claim 5, characterized in that: Under the detection window, the ability of sunlight to penetrate the atmosphere is analyzed abnormally, and the logic of generating the atmospheric transmittance index is as follows: In the detection window, the data on solar radiation and atmospheric conditions are obtained through monitoring equipment, and the total irradiance is calculated. The calculation expression is as follows: , where is the total irradiance, is the direct irradiance, represents the diffuse irradiance; In the absence of atmospheric influence, the ideal light intensity is calculated using the following expression: , where represents the light intensity under ideal conditions, Indicates the irradiance under theoretical standard conditions, is the angle between the sun and the horizontal plane; Evaluate the impact of the atmosphere on solar radiation, determine the ratio between actual irradiance and ideal irradiance, and calculate the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance; The atmospheric transmittance index is generated by the atmospheric transmittance, and the calculation expression is as follows: , where represents the atmospheric transmittance index, is the absorption coefficient Represents the supplementary part of atmospheric transmittance.

7. A method for assessing power generation loss of a photovoltaic system according to claim 5, characterized in that: In the detection window, the impact analysis of the surface reflectivity is carried out and the steps to generate the surface reflectivity index are as follows: The surface reflectivity is calculated by the incident solar radiation intensity and the reflected light intensity. The calculation expression is as follows: , where Represents the intensity of reflected light, is the intensity of incident solar radiation, Represents the surface reflectivity; Adjust the surface reflectivity to eliminate potential environmental interference factors. The calculation expression is as follows: , where represents the adjusted surface reflectivity, represents the humidity coefficient, is the current ambient temperature, is the reference temperature, is the temperature sensitivity coefficient; Convert the adjusted surface reflectance into the final surface reflectance index, , where represents the surface reflectivity index, is the enhancement factor of the surface reflectivity index.

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