A method for evaluating power generation loss of a photovoltaic system

Abstract

The present invention discloses a method for evaluating power generation loss of a photovoltaic system, which relates to the technical field of photovoltaic power generation, and includes the following steps: The MPPT control system monitors the output parameters of photovoltaic modules in real time. By obtaining the operating parameter information, it captures the output characteristics of photovoltaic modules in real time when the light changes, enabling the MPPT control system to perceive the change of light conditions. The present invention can quickly identify the change of light by monitoring the sunlight penetration rate and the ground reflectivity in real time. The MPPT control system can accurately adjust the working point of the photovoltaic modules to ensure that the system is close to the maximum power point, and can optimize the energy conversion even under complex weather conditions. This dynamic adaptability improves the overall power generation and economic benefits. At the same time, by comparing the external environment evaluation index with the reference threshold, the control strategy is optimized, the power fluctuation is reduced, the grid voltage and frequency stability are maintained, the coordination between the photovoltaic system and the grid is enhanced, and the reliability and sustainable development of renewable energy are supported.

Description

Technical Field

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

[0002] The evaluation of the power generation loss of a photovoltaic system is to determine the power loss of the system in actual operation relative to the ideal conditions by analyzing and quantifying various factors affecting the photovoltaic power generation efficiency. Theoretically, the photovoltaic power generation is determined by factors such as solar radiation, the conversion efficiency of photovoltaic modules, and system configuration. However, in actual operation, environmental conditions (such as temperature, dust, and shading) and equipment performance degradation (such as module aging and inverter efficiency reduction) will cause the actual power generation to be lower than expected. By comparing the actually monitored data with the theoretically calculated power generation, the specific power generation loss can be evaluated. The monitored 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 the analysis of these data, the operator can find the main reasons for power generation loss, such as efficiency reduction caused by temperature rise, power reduction caused by dust coverage, equipment aging or failure, etc., and accordingly optimize the system design and maintenance strategy.

[0003] The prior art has the following deficiencies:

[0004] The photovoltaic system achieves maximum power output under different light conditions through maximum power point tracking (MPPT). MPPT is a key control function in the photovoltaic system. However, under rapidly changing weather conditions, the MPPT control system faces the challenge of how to balance rapid adjustment and system stability. When the controller frequently adjusts the operating point, especially when the weather changes violently, the system power output will fluctuate violently. This not only weakens the power generation stability of the photovoltaic system but also may have a negative impact on the grid connected to the grid. Since the grid needs to maintain the balance between supply and demand, the frequent fluctuations of the photovoltaic system may cause abnormal voltage or frequency in the local grid, endangering the stability of the grid. Once this fluctuation exceeds 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 be temporarily disconnected from the grid. For a grid-connected photovoltaic power station, this means direct power generation loss and economic damage.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a method for evaluating the power generation loss of a photovoltaic system. By real-time monitoring of the sunlight penetration rate and the surface reflectivity, the MPPT control system can quickly identify changes in illumination, accurately adjust the operating point of the photovoltaic modules, so as to ensure that the system always operates close to the maximum power point, and 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 evaluation index with the reference threshold, reduces the power output fluctuation, 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, so as to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: including the following steps:

[0008] The MPPT control system real-time monitors the output parameters of the photovoltaic modules. Through the obtained operation parameter information, it can real-time capture the output characteristics of the photovoltaic modules when the illumination changes, enabling the MPPT control system to sense the changes in illumination conditions;

[0009] While obtaining the output parameter information of the photovoltaic modules, it collects the external environment information related to photovoltaic power generation, processes the collected original external environment data, identifies the key features affecting the power generation performance of the photovoltaic system, and analyzes and processes the identified key features. Then, it inputs the analyzed and processed key features into a pre-learned machine model. Through the analysis of the model, it generates a prediction result for future environmental changes;

[0010] Based on the analysis result of the machine learning model, it classifies the external environment changes into rapid environmental changes and normal environmental changes;

[0011] 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 the changes in external conditions based on the adjustment method for normal environmental changes. The MPPT control system will apply an intelligent adjustment strategy and dynamically adjust the operating point frequency of the photovoltaic system through a real-time feedback mechanism.

[0012] Preferably, the MPPT control system captures the output characteristics of the photovoltaic modules when the illumination changes and senses the illumination conditions in real time through the following steps according to the obtained operation parameter information:

[0013] The MPPT control system is equipped with sensors and a data acquisition module to continuously monitor the output parameters of the photovoltaic modules;

[0014] The MPPT control system records the output parameters in real time and generates an output characteristic curve;

[0015] By comparing and analyzing the real-time output characteristic curve, the MPPT control system can identify the characteristics of light intensity changes.

[0016] Preferably, the extracted external environment characteristics of the power generation stability of the photovoltaic system include the ability of sunlight to penetrate the atmosphere and the ability of the earth's surface to reflect solar radiation. After extraction, the ability of sunlight to penetrate the atmosphere and the ability of the earth's surface to reflect solar radiation are placed under the detection window for anomaly analysis, and the atmospheric transmittance index and the surface reflectance index are generated respectively.

[0017] Preferably, the sorted atmospheric transmittance index and surface reflectance index are input into a pre-trained machine model, and an external environment evaluation index is generated through the machine model, and the external environment change state is predicted based on the external environment evaluation index.

[0018] Preferably, the external environment evaluation index generated based on the machine learning model is compared and analyzed with the pre-set reference threshold of the external environment evaluation index, and the external environment changes are classified. The specific classification is as follows:

[0019] If the external environment evaluation index is greater than or equal to the pre-set reference threshold of the external environment evaluation index, the current external environment change is classified as a rapid environment change state;

[0020] If the external environment evaluation index is less than the pre-set reference threshold of the external environment evaluation index, the current external environment change is classified as a normal environment change state.

[0021] Preferably, under the detection window, the anomaly analysis of the ability of sunlight to penetrate the atmosphere is carried out, and the logic for generating the atmospheric transmittance index is as follows:

[0022] Under the detection window, 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 the total irradiance, represents the direct irradiance, represents the diffuse irradiance;

[0023] In the absence of atmospheric influence, the light intensity under ideal conditions is calculated. The calculation expression is as follows: , where represents the light intensity under ideal conditions, represents the irradiance under theoretically standard conditions, is the angle between the sun and the horizontal plane;

[0024] Evaluate the influence of the atmosphere on solar radiation, determine the ratio between the actual irradiance and the ideal irradiance, and calculate the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance;

[0025] Generate an atmospheric transmittance index based on the atmospheric transmittance, and the calculation expression is as follows: , where in the formula, represents the atmospheric transmittance index, is the absorption coefficient represents the supplementary part of the atmospheric transmittance.

[0026] Preferably, under the detection window, the steps of analyzing the influence on the surface reflectance and generating the surface reflectance index are as follows:

[0027] Calculate the surface reflectance through the incident solar radiation intensity and the reflected light intensity, and the calculation expression is as follows: , where in the formula, represents the reflected light intensity, represents the incident solar radiation intensity, represents the surface reflectance;

[0028] Adjust the surface reflectance to eliminate potential environmental interference factors, and the calculation expression is as follows: , where in the formula, represents the adjusted surface reflectance, represents the humidity coefficient, is the current environmental temperature, is the reference temperature, is the temperature sensitivity coefficient;

[0029] Convert the adjusted surface reflectance into the final surface reflectance index, , where in the formula, represents the surface reflectance index, is the enhancement factor of the surface reflectance index.

[0030] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0031] By continuously monitoring and analyzing external environmental characteristics such as sunlight penetration rate and surface reflectance, the MPPT control system can quickly identify changes in light, thereby accurately adjusting the operating point of the photovoltaic module. This dynamic adaptability ensures that the system always operates near the maximum power point, achieving the best energy conversion rate even under complex weather conditions. This efficiency improvement not only increases the overall power generation of the photovoltaic system but also helps to improve economic efficiency, ensuring maximum return on investment.

[0032] By comparing the external environment assessment index with the reference threshold, the system can effectively identify the states of rapid environmental changes and normal environmental changes, and thus adjust the control strategy. In a rapidly changing environment, applying an intelligent adjustment strategy can reduce the drastic fluctuations in power output, reduce the impact on the grid connected to the grid, and maintain the voltage and frequency stability of the grid. This not only reduces the grid operator's demand for power curtailment of the photovoltaic power station, but also enhances the coordination between the photovoltaic system and the grid, ensures the reliability and security of renewable energy, and thus supports the goal of sustainable development. Brief Description of the Drawings

[0033] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a method flow chart of a method for evaluating the power generation loss of a photovoltaic system according to the present invention. Detailed Embodiments

[0035] Now, the exemplary embodiments will be described more comprehensively with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; on the contrary, these exemplary embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0036] The present invention provides a method for evaluating the power generation loss of a photovoltaic system as shown in Figure 1 and includes the following steps:

[0037] The MPPT control system continuously monitors the output parameters of the photovoltaic modules. By obtaining the operating parameter information, it can capture the output characteristics of the photovoltaic modules when the light changes in real time, enabling the MPPT control system to sense the changes in the light conditions.

[0038] The MPPT control system captures the output characteristics of the photovoltaic modules when the light changes in real time and senses the changes in the light conditions according to the obtained operating parameter information through the following steps:

[0039] The MPPT control system is equipped with sensors and a data acquisition module to continuously monitor the output parameters of the photovoltaic modules.

[0040] The output parameters of the 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 the photovoltaic modules under various light conditions.

[0041] The MPPT control system records the output parameters in real time and generates output characteristic curves (I-V curve and P-V curve).

[0042] The output characteristic curves (I-V curve and P-V curve) of photovoltaic modules are key graphs reflecting 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 I-V curve and P-V curve, which enables the controller to analyze the working state of photovoltaic modules in real time and identify the maximum power point under the current light conditions.

[0043] By comparing and analyzing the real-time output characteristic curves, the MPPT control system can identify the characteristics of light changes.

[0044] When the cloud moves quickly, the irradiance will decrease rapidly, resulting in a decrease in the output power of the photovoltaic module. The MPPT control system judges whether there is a sudden change in light conditions by observing the change rate of the output power. Once a change in light conditions is detected, the controller can quickly adjust the working point to ensure that the system can still operate near the maximum power output under the new light conditions.

[0045] While obtaining the output parameter information of photovoltaic modules, collect the external environment information related to photovoltaic power generation, process the collected raw external environment data, identify the key characteristics affecting the power generation performance of the photovoltaic system, and analyze and process the identified key characteristics. Input the analyzed and processed key characteristics into a pre-trained machine model. Through the analysis of the model, generate the prediction results of future environmental changes.

[0046] The extracted external environment characteristics of the power generation stability of the photovoltaic system include the ability of sunlight to penetrate the atmosphere and the ability of the ground to reflect solar radiation. After extraction, place the ability of sunlight to penetrate the atmosphere and the ability of the ground to reflect solar radiation under the detection window for anomaly analysis, and generate the atmospheric transmittance index and the surface reflectance index respectively. Input the analyzed and sorted atmospheric transmittance index and surface reflectance index into a pre-trained machine model, and generate the external environment evaluation index through the machine model. Predict the external environment change state based on the external environment evaluation index.

[0047] The impact of the change 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 weakens, and the light intensity received by the photovoltaic modules will decrease sharply; in this case, the MPPT control system will quickly detect the change in output power and frequently adjust the operating point to track the new maximum power point; however, frequent adjustments may lead to unstable power output of the system, manifested 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-demand relationship in real time, and the power fluctuations of the photovoltaic system will cause abnormal fluctuations in the grid voltage and frequency, affecting the overall stability of the grid; in addition, if this kind of fluctuation exceeds 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 disconnect from the grid temporarily, resulting in direct losses in power generation and economic damage;

[0048] Under the detection window, an anomaly analysis is carried out on the ability of sunlight to penetrate the atmosphere, and the logic of generating the atmospheric transmittance index is as follows:

[0049] Under the detection window, data on solar radiation and atmospheric conditions are obtained through monitoring devices, and the total irradiance is calculated. The calculation expression is as follows: , where represents the total irradiance, which is the total solar radiation energy received on the photovoltaic module, represents the direct irradiance, which is the radiation energy directly propagated from the solar light source to the surface of the photovoltaic module, represents the diffuse irradiance, which is the radiation generated by atmospheric scattering;

[0050] It should be noted that obtaining data on solar radiation and atmospheric conditions through sensors and monitoring devices is an important part of photovoltaic system optimization and performance evaluation; the following are the steps to elaborate on the acquisition process, as well as the types of common sensors and devices;

[0051] 1. Solar radiation monitoring

[0052] a. Solar radiation sensor (radiometer)

[0053] Function:

[0054] Used to measure the intensity of solar radiation reaching the ground;

[0055] Type:

[0056] Spectral radiometer: Measures radiation of different wavelengths to evaluate spectral characteristics;

[0057] Total radiation meter: Measures the sum of direct and diffuse radiation and is often used to calculate the photovoltaic power generation potential;

[0058] b. Installation location

[0059] It is usually installed above or near the photovoltaic system to ensure that the sensor is not shaded and can directly receive sunlight;

[0060] 2. Environmental condition monitoring

[0061] a. Temperature sensor

[0062] Function: Measure the environmental temperature and the temperature of the photovoltaic module;

[0063] Type: Thermocouple, thermal resistance, etc.;

[0064] b. Humidity sensor

[0065] Function: Monitor the relative humidity to evaluate its impact on photovoltaic performance;

[0066] Type: Capacitive humidity sensor, impedance humidity sensor;

[0067] c. Wind speed and direction sensor

[0068] Function: Measure the wind speed and direction, which have an important impact on the heat dissipation and cooling effect of the module;

[0069] Type: Anemometer, wind vane;

[0070] 3. Atmospheric condition monitoring

[0071] a. Atmospheric pressure sensor

[0072] Function: Measure the environmental atmospheric pressure, which affects the radiation propagation and lighting conditions;

[0073] Type: Pressure sensor, barometer;

[0074] b. Particulate matter monitoring sensor

[0075] Function: Monitor the concentration of particulate matters such as PM2.5 and PM10 in the air to evaluate the air quality and its impact on photovoltaic performance;

[0076] Type: Laser particle counter, optical sensor;

[0077] In the absence of atmospheric influence, calculate the ideal light intensity, and the calculation formula is as follows: , where represents the ideal light intensity, represents the irradiance under theoretically standard conditions, usually 1000 W / m², is the angle between the sun and the horizontal plane (angle of incidence);

[0078] It should be noted that the atmospheric influence is mainly reflected in the following aspects:

[0079] 1. Scattering: Gases and particulate matter in the atmosphere scatter sunlight, resulting in a decrease in the intensity of direct radiation; this scattering makes the light more uniform, forming scattered radiation;

[0080] 2. Absorption: Gases such as water vapor and carbon dioxide in the atmosphere absorb light at specific wavelengths, reducing the radiation intensity reaching the ground, especially in the infrared and ultraviolet bands;

[0081] 3. Reflection: A portion of solar radiation is reflected back into space by clouds or the Earth's surface, especially in the case of cloudy or overcast skies;

[0082] 4. Aerosols: Tiny particulate matter (such as dust and pollutants) in the atmosphere also affects the propagation of light and may cause a decrease in radiation intensity;

[0083] Ideal light intensity

[0084] The ideal light intensity refers to the maximum solar radiation intensity that a photovoltaic module can receive in the absence of any atmospheric effects; it is usually represented by the standard solar irradiance \(I_{sc}\), which is typically 1000 W / m²; this value is measured under standard conditions (such as a clear sky and direct sunlight) and is a benchmark for the design and performance evaluation of photovoltaic modules;

[0085] When evaluating the performance of a photovoltaic system, it is crucial to understand the atmospheric effects and the ideal light intensity, which helps to identify and optimize the power generation efficiency; by comparing the actual received irradiance with the irradiance under ideal conditions, one can better understand the performance of the system under different environmental conditions;

[0086] Evaluate the impact of the atmosphere on solar radiation, determine the ratio between the actual irradiance and the ideal irradiance, and calculate the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance;

[0087] Generate the atmospheric transmittance index through the atmospheric transmittance. The calculation expression is as follows: , where represents the atmospheric transmittance index, is the absorption coefficient, and the absorption coefficient is a parameter characterizing the intensity of radiation absorption in the atmosphere, represents the supplementary part of the atmospheric transmittance;

[0088] Under the detection window, according to the calculation expression of the atmospheric transmittance index, the larger the performance value of the atmospheric transmittance index, usually means that the photovoltaic system frequently adjusts the operating point under adverse climate conditions (such as cloudy, haze, etc.), resulting in an increase in 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, increasing the probability of abnormal local grid voltage or frequency; conversely, the smaller the performance value of the atmospheric transmittance index, it indicates that the photovoltaic system operates under relatively stable light conditions, with a lower frequency of operating point adjustment, which helps to maintain the stability of the power grid and reduce the risk of abnormal local grid voltage or frequency;

[0089] The change of the surface reflectivity in the external environment has an important impact on the power generation stability of the photovoltaic system; when the surface reflectivity changes, especially under 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 module to change instantaneously; at this time, the MPPT control system will frequently adjust the operating point because it cannot adapt to these changes in real time, resulting in a sharp fluctuation in the system power output; this kind of fluctuation not only weakens the power generation stability of the photovoltaic system, but also may have a negative impact on the grid-connected power grid; because the power grid needs to maintain the balance between supply and demand, and the frequent fluctuation of photovoltaic power generation may lead to the instability of 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 disconnect from the grid, causing direct power generation losses and economic damages; therefore, the impact of surface reflectivity change on the photovoltaic system is not only a technical challenge, but also related to the broader grid security and stability;

[0090] Under the detection window, the steps to analyze the influence of the surface reflectivity and generate the surface reflectivity index are as follows:

[0091] Calculate the surface reflectivity through the incident solar radiation intensity and the reflected light intensity, and the calculation expression is as follows: , where represents the reflected light intensity, represents the incident solar radiation intensity, represents the surface reflectivity;

[0092] 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 the solar radiation reaching the earth's surface after passing through the atmosphere; the incident solar radiation intensity is affected by various factors, including the position of the sun (such as time and season), atmospheric conditions (such as clouds, pollutants), geographical location and terrain, etc.; a high incident solar radiation intensity means that the photovoltaic system can generate more electric energy;

[0093] The reflected light intensity refers to the light radiation energy reflected back from the earth's surface or other surfaces into the atmosphere, and is also measured in watts per square meter (W / m²); the reflected light intensity depends on the nature of the surface (such as color, material, and roughness), as well as the intensity and angle of the incident light; different surfaces have different light reflection capabilities, with smooth and light-colored surfaces usually having a higher reflectivity, while rough and dark-colored surfaces absorb more light; the 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;

[0094] Adjust the surface reflectivity to eliminate potential environmental interference factors, and 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;

[0095] It should be noted that the potential environmental interferences are as follows:

[0096] 1. Humidity:

[0097] Effect: When the humidity is high, water vapor will scatter light, resulting in a reduction in the effective light intensity received by the photovoltaic system, thus affecting the power generation efficiency;

[0098] 2. Temperature:

[0099] Effect: High temperature will cause the efficiency of photovoltaic modules to decline, and the reflectivity and absorptivity of materials may also change accordingly, affecting the overall power generation capacity;

[0100] 3. Dust and pollutants:

[0101] Effect: Dust and pollutants on the ground or photovoltaic modules will block light, reducing the available solar radiation, thus reducing the power generation;

[0102] 4. Clouds and weather changes:

[0103] Effect: Rapid weather changes will cause drastic fluctuations in light intensity, affecting the stability and power generation capacity of the photovoltaic system;

[0104] 5. Surface reflection characteristics:

[0105] Effect: The difference in reflectivity of different surfaces (such as soil, vegetation, water bodies) will affect the reflection and scattering of incident light, and thus affect the performance of the photovoltaic system;

[0106] 6. Angle and azimuth:

[0107] Impact: The installation angle and orientation of photovoltaic modules have a direct impact on the effectiveness of receiving sunlight. Different angles may result in uneven sunlight reception;

[0108] The reference temperature is usually obtained in the following ways:

[0109] 1. Weather station data:

[0110] Method: Obtain long-term climate data from a nearby weather station and use a specific time period (such as annual average or seasonal average) as the reference temperature;

[0111] 2. Historical temperature data:

[0112] Method: Analyze historical temperature data and select the average temperature under specific climate conditions as the reference temperature;

[0113] 3. Experimental measurement:

[0114] Method: Conduct experiments in a controlled environment, measure the performance of photovoltaic modules under different temperature conditions, and select an ideal temperature value as the reference;

[0115] 4. Standard reference conditions:

[0116] Method: Set the reference temperature according to industry standards (such as the International Electrotechnical Commission IEC) or national standards (such as national solar standards), generally 25°C;

[0117] These methods ensure that the reference temperature can reflect the changes in the actual environment, making it more accurate and practical in calculations and analyses;

[0118] Convert the adjusted surface reflectance into the final surface reflectance index, , where, represents the surface reflectance index, is the enhancement factor of the surface reflectance index;

[0119] Under the detection window, from the calculation expression of the surface reflectance index, the larger the value of the surface reflectance index performance, usually means that the surface's ability to reflect incident light is enhanced, which may cause the photovoltaic controller to frequently adjust the operating point to adapt to the changing light conditions; this frequent adjustment will cause severe fluctuations in the system power output, thereby increasing the probability of abnormal local grid voltage or frequency; conversely, if the value of the surface reflectance index performance is smaller, it indicates that the light conditions are relatively stable, the controller adjustment frequency decreases, so that the system power output is more stable, and the probability of abnormal voltage and frequency of the local grid also decreases accordingly;

[0120] The machine learning model is not specifically limited here and can achieve converting the atmospheric transmittance index and the surface reflectivity index are comprehensively analyzed to generate an external environment evaluation index Any machine learning model can be used. To implement the technical solution of the present invention, the present invention provides a specific implementation method;

[0121] External environment evaluation index The calculation formula for generation is: , where and are respectively the preset proportionality coefficients of the atmospheric transmittance index and the surface reflectivity index , and , are both greater than 0;

[0122] It can be seen from the calculation expression of the external environment evaluation index that the larger the performance value of the atmospheric transmittance index generated after analyzing the atmospheric transmittance anomaly and the larger the performance value of the surface reflectivity index generated after analyzing the surface reflectivity anomaly, the larger the external environment evaluation index, indicating that the external environment has changed rapidly, and the MPPT control system needs to frequently adjust the operating point, resulting in a greater possibility of frequent fluctuations in the photovoltaic system. On the contrary, it indicates that the external environment belongs to normal changes and the MPPT control system does not need to frequently adjust the operating point;

[0123] Based on the analysis results of the machine learning model, the external environment change situation is divided into a rapid environment change state and a normal environment change state;

[0124] The external environment evaluation index generated based on the machine learning model is compared and analyzed with the preset external environment evaluation index reference threshold to divide the external environment change situation. The specific division is as follows:

[0125] If the external environment evaluation index is greater than or equal to the preset external environment evaluation index reference threshold, the current external environment change situation is divided into a rapid environment change state;

[0126] If the external environment evaluation index is less than the preset external environment evaluation index reference threshold, the current external environment change situation is divided into a normal environment change state.

[0127] For the normal environment change state, the photovoltaic system continues to adjust the operating point using the set adjustment method. In the case of the rapid environment change state, the photovoltaic system needs to quickly adapt to the change of external conditions based on the adjustment method of the normal environment change state. The MPPT control system will apply an intelligent adjustment strategy to dynamically adjust the operating point frequency of the photovoltaic system through a real-time feedback mechanism;

[0128] ​For normal environmental change states, the photovoltaic system continuously adjusts its operating point according to a preset adjustment strategy, which means that within the range of changes in light, temperature, and other environmental conditions, the system can respond to these changes in a timely manner to maintain optimal power generation efficiency; this adjustment is based on historical data and algorithm models to ensure that when environmental changes are not drastic, the system can stably track the maximum power point, thus achieving efficient power generation; during this process, the system uses the maximum power point tracking (MPPT) technology to dynamically monitor output parameters and optimize the operating state to ensure that the photovoltaic modules are always in the best working conditions to maximize the energy conversion efficiency and reduce power generation losses;

[0129] In the case of rapid environmental change states, the steps for the photovoltaic system to quickly adapt to changes in external conditions based on the adjustment method for normal environmental change states are as follows:

[0130] Calculate the adjustment strategy coefficient based on the external environment evaluation index and the reference threshold of the external environment evaluation index, and dynamically adjust the operating 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 evaluation index, , is the adjustment coefficient, and its value range is between 0.01 and 0.1, is the current power output;

[0131] After calculating the adjustment strategy coefficient, the photovoltaic system will dynamically adjust its operating point according to this coefficient to optimize the energy output. Specifically, during execution, the controller will apply the adjustment coefficient to the current power output to calculate the adjusted power target; subsequently, the system will gradually adjust the operating state of the photovoltaic modules according to the real-time monitored environmental parameters and output performance to ensure that it reaches the best operating efficiency under the new light and temperature conditions. Through real-time monitoring and analysis of external environmental characteristics such as sunlight penetration rate and ground reflectivity, the MPPT control system of the present invention can quickly identify light changes, thereby accurately adjusting the operating point of the photovoltaic modules. This dynamic adaptability ensures that the system always operates near the maximum power point, and even under complex weather conditions, it can achieve the best energy conversion rate. This efficiency improvement not only increases the overall power generation of the photovoltaic system but also helps to improve economic benefits and ensure the maximization of investment returns.

[0132] By comparing the external environment assessment index with the reference threshold, the system can effectively identify the states of rapid environmental changes and normal environmental changes, thereby adjusting the control strategy. In a rapidly changing environment, the application of an intelligent adjustment strategy can reduce the drastic fluctuations in power output, reduce the impact on the grid connected to the grid, and maintain the voltage and frequency stability of the grid. This not only reduces the power curtailment requirements of the grid operator for the PV power plant, but also enhances the coordination between the PV system and the grid, ensuring the reliability and safety of renewable energy, and thus supporting the goal of sustainable development.

[0133] Only some exemplary embodiments of the present invention have been described by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different 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 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. 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 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 abnormal analysis to generate the atmospheric transmittance index and the ground surface reflectance index respectively. 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: total =I direct +I diffuse , where I total Represents the total irradiance, I direct Indicates direct irradiance, I diffuse represents the diffuse irradiance; In the absence of atmospheric influence, the ideal light intensity is calculated using the following expression: ideal =I sc ×cos(θ), where, I ideal Indicates the ideal light intensity, I sc It represents the irradiance under theoretical standard conditions, and θ 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, τ represents the atmospheric transmittance; The atmospheric transmittance index is generated by the atmospheric transmittance. The calculation expression is as follows: AER = e -α·(1-τ) , where AER represents the atmospheric transmittance index, α is the absorption coefficient, and (1-τ) represents the supplementary part of the atmospheric transmittance; 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: In the formula, I ref Represents the reflected light intensity, I inc represents the intensity of incident solar radiation, and R represents the surface reflectivity; Adjust the surface reflectivity to eliminate potential environmental interference factors. The calculation expression is as follows: R adj =R·(1-H)·(1+k·(TT ref )), where R adj represents the adjusted surface reflectivity, H represents the humidity coefficient, T is the current ambient temperature, and T ref is the reference temperature, k is the temperature sensitivity coefficient; Convert the adjusted surface reflectance into the final surface reflectance index, In the formula, R index represents the surface reflectivity index, and p is the enhancement factor of the surface reflectivity index; The calculation formula for the external environment assessment index EEA is: Where β1 and β2 are the atmospheric transmittance index AER and the surface reflectivity index R, respectively. index The preset proportional coefficient, and β1 and β2 are both greater than 0; 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: Based on the external environment assessment index and the reference threshold of the external environment assessment index, the adjustment strategy coefficient is calculated to dynamically adjust the working point frequency of the photovoltaic system. The calculation expression is as follows: P adjusted =P current ·(1+k·(EEA-EEA ref )), where P adjusted represents the adjustment strategy coefficient, EEA ref Indicates the reference threshold of the external environment assessment index, EEA>EEA ref , k is the adjustment coefficient, ranging from 0.01 to 0.1, P current is the current power output; After calculating the regulation strategy coefficient, the photovoltaic system will dynamically adjust its operating point according to the regulation strategy 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; Subsequently, based on the real-time monitored environmental parameters and output performance, the working status of the photovoltaic modules is gradually adjusted to ensure that they achieve optimal operating efficiency under new light and temperature conditions.

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 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.

4. A method for assessing power generation loss of a photovoltaic system according to claim 1, 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.

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