Power generation regulation and control method for photovoltaic power generation system

Abstract

The invention provides a power generation regulation and control method for a photovoltaic power generation system. The power generation regulation and control method for the photovoltaic power generation system comprises the following steps: S1, real-time monitoring and data acquisition: (1) various sensors are deployed in the photovoltaic power generation system, and solar radiation intensity data, photovoltaic module temperature data, cell panel output voltage data, current data and inverter output data are monitored in real time through the various sensors; (2) combining external meteorological data of environment temperature, humidity and air pressure to realize all-around system state acquisition; s2, maximum power point tracking (MPPT) and optimal load adjustment: (1) when the illumination condition changes, a multi-objective optimization algorithm is adopted, so that the working mode of the inverter can be adjusted in real time; the power generation regulation and control method for the photovoltaic power generation system has the advantages that the power generation efficiency of the photovoltaic system can be effectively improved, the stability and the reliability of the photovoltaic system are enhanced, and meanwhile the operation cost is reduced.

Description

Technical Field

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

[0002] With the transformation of the global energy structure, photovoltaic power generation, as a clean and renewable energy, has received extensive attention. The photovoltaic power generation system converts solar energy into electrical energy, with advantages such as pollution-free, low cost, and easy maintenance. However, during the actual operation of the photovoltaic power generation system, affected by various factors, such as climate change, light intensity, temperature change, and the decline of the performance of system components, the power generation efficiency will fluctuate.

[0003] Currently, the general photovoltaic power generation system adopts the maximum power point tracking (MPPT) technology to improve its efficiency. However, most of these existing technologies only focus on the tracking of the maximum power point and do not comprehensively optimize the overall operating state of the system. In addition, with the continuous expansion of the scale of the photovoltaic system, a single adjustment method cannot meet the performance requirements in complex environments. For example, how to reasonably regulate the output power of the photovoltaic system at different times and under different loads to ensure its stable and efficient operation.

[0004] Therefore, it is necessary to provide a new power generation regulation method for a photovoltaic power generation system to solve the above technical problems. Summary of the Invention

[0005] The technical problem solved by the present invention is to provide a power generation regulation method for a photovoltaic power generation system that can effectively improve the power generation efficiency of the photovoltaic system, enhance its stability and reliability, and at the same time reduce the operating cost.

[0006] To solve the above technical problems, the power generation regulation method for a photovoltaic power generation system provided by the present invention includes the following steps:

[0007] S1. Real-time Monitoring and Data Acquisition:

[0008] (1). By deploying a variety of sensors in the photovoltaic power generation system, the solar radiation intensity data, photovoltaic module temperature data, output voltage data of the battery panel, current data, and output data of the inverter are real-time monitored through the variety of sensors;

[0009] (2). Combining the external meteorological data such as environmental temperature, humidity, and air pressure, the all-round system state acquisition is realized;

[0010] S2. Maximum Power Point Tracking (MPPT) and Optimal Load Regulation:

[0011] (1). When the lighting conditions change, a multi-objective optimization algorithm is adopted to enable real-time adjustment of the inverter's operating mode to ensure maximum system output power;

[0012] (2). According to the load demand and grid status, an intelligent load prediction and adjustment strategy is adopted to avoid overloading or underloading, thereby improving the reliability and stability of photovoltaic power generation;

[0013] S3. Temperature compensation and thermal management mechanism:

[0014] (1). Based on the fact that the operating efficiency of photovoltaic modules is closely related to temperature, it is to avoid the decrease in the output power of photovoltaic modules caused by too high temperature;

[0015] (2). Through the temperature monitoring system, when the temperature of the photovoltaic system exceeds the set threshold, the active thermal management mechanism can be started and the inverter output can be adjusted or the panel angle can be adjusted to reduce the system load and maintain the optimal operating temperature of the photovoltaic module;

[0016] S4. Prediction and dynamic adjustment of lighting changes:

[0017] (1). By using a prediction algorithm and analyzing historical data and meteorological prediction information, the lighting change trend in the next period of time can be predicted, and the operating parameters of the power generation system can be dynamically adjusted;

[0018] (2). Under cloudy, rainy or other complex weather conditions, it can effectively avoid power generation fluctuations caused by excessive lighting fluctuations and ensure stable system output;

[0019] S5. Adaptive control strategy and intelligent learning:

[0020] (1). Using machine learning algorithms, various data during the system operation, such as output power, ambient temperature, and lighting intensity, are used to continuously optimize the control strategy;

[0021] (2). Through adaptive control, the system can dynamically adjust the operating mode according to real-time data, thereby achieving the best power generation efficiency;

[0022] S6. Remote monitoring and fault diagnosis:

[0023] (1). Based on the remote monitoring function of the system, the operating status, fault alarm, and maintenance requirement information of the photovoltaic power generation system are displayed in real time;

[0024] (2). Combining data analysis and fault diagnosis technologies to quickly identify possible faults or performance degradation problems in the system and issue alarms in a timely manner to provide decision-making support for maintenance personnel.

[0025] As a further aspect of the present invention, the multiple sensors include, but are not limited to, a solar radiation intensity sensor, a photovoltaic module temperature sensor, a panel output voltage sensor, a panel output current sensor, and an inverter output data sensor. The multiple sensors aggregate the measurement data through a data acquisition system (DAS), which specifically includes sensor data acquisition, data transmission, and data storage and processing. The sensor data acquisition is as follows:

[0026] (1). Data acquisition module (DAQ): The output signals of all sensors are digitally processed by the data acquisition module. The DAQ module can convert different types of signals, such as analog voltage and current signals, into digital signals for unified management;

[0027] (2). Data acquisition frequency: According to the dynamic characteristics of the photovoltaic power generation system, an appropriate data acquisition frequency is selected.

[0028] As a further aspect of the present invention, the data transmission is as follows:

[0029] (1). Wireless transmission: For a distributed photovoltaic system, data can be transmitted to a remote server or cloud platform wirelessly, such as via Wi-Fi, Zigbee, or LoRa. The wireless communication device should be installed in the monitoring center of the system or integrated with the acquisition device;

[0030] (2). Wired transmission: If the scale of the photovoltaic system is large or the transmission distance is long, then a wired method, such as RS485, Ethernet, or Modbus protocol, can be used to transmit data from each sensor to a central processor;

[0031] The data storage and processing are as follows:

[0032] (1). Data storage: The data collected by the sensors is saved through a cloud platform, a database, or a local storage system. The data can be stored in time periods of daily, weekly, or monthly for easy later query and analysis;

[0033] (2). Data processing: After the sensor data is stored, it can be further analyzed through a data analysis and processing platform to obtain the operating status information, power generation efficiency information, and fault warning information of the photovoltaic power generation system. The data processing algorithms include maximum power point tracking (MPPT), efficiency calculation, and fault diagnosis.

[0034] As a further aspect of the present invention, the maximum power point tracking (MPPT) and the dynamic regulation of the inverter are as follows:

[0035] (1). To achieve real-time adjustment of the inverter working mode, it is first necessary to monitor the change of ambient light in real time, as follows:

[0036] 1). Light intensity monitoring: Install a solar radiation sensor, such as a radiometer, to obtain real-time solar radiation intensity data.

[0037] 2). Temperature monitoring: Monitor the temperature of the photovoltaic modules to avoid the impact of high temperature on efficiency.

[0038] 3). Output voltage and current monitoring of photovoltaic panels: Use voltage and current sensors to obtain real-time output data of the photovoltaic panels.

[0039] (2). Adopt a multi-objective optimization algorithm, and adjust the operating parameters of the system in real time according to the current environmental data, such as light intensity, temperature, and the status of the photovoltaic panels, such as voltage and current. The algorithm is as follows:

[0040] 1). Maximize power output: Adjust the operating state of the inverter so that the system output power reaches the maximum. The formula is as follows:

[0041] P max =f(V out ,I out )

[0042] Where P max is the maximum power, and (V out ,I out ) are the output voltage and current of the photovoltaic system respectively.

[0043] (3). Optimize the operating mode of the inverter: Ensure that the inverter is in the best operating state to improve system efficiency, as follows:

[0044] Efficiency=g(Power,Factor,Vin,Iin)

[0045] Where Vin and Iin are the voltage and current input to the inverter, and the power factor is the efficiency index of the inverter.

[0046] As a further solution of the present invention, the adjustment of the operating mode of the inverter is specifically as follows:

[0047] (1). Maximum power point tracking (MPPT): The inverter calculates the maximum power point based on the output voltage and current of the photovoltaic panels, and adjusts its input voltage and current so that the system always operates at the maximum power point, as follows:

[0048] 1). Perturb and observe method: Find the maximum power point by observing the power change through a small perturbation.

[0049] 2). Incremental conductance method: Adjust the maximum power point in real time through the change of conductance.

[0050] (2). Inverter regulation:

[0051] 1). Input voltage regulation: The inverter can adjust its input voltage according to the voltage output of the photovoltaic module, so that the system can output the maximum power under different lighting conditions;

[0052] 2). Working mode switching: The inverter may adjust its working mode according to the load requirements or changes in light intensity, such as switching from the grid-connected mode to the off-grid mode, or changing the power factor;

[0053] (3). Dynamic load management: When the lighting conditions change, the system can dynamically adjust the load according to the current power output and load demand, avoiding overload or underload, and improving the stability and power utilization rate of the system.

[0054] As a further solution of the present invention, in the photovoltaic power generation system, the intelligent load prediction and adjustment strategy according to the load demand and grid status can effectively avoid overload or underload and ensure the stability and reliability of the system. The specific method steps are as follows:

[0055] (1). Real-time monitoring of load demand and grid status:

[0056] 1). Load monitoring: By installing load sensors and smart meters, the power consumption of each load is monitored in real time to obtain load demand information;

[0057] 2). Grid status monitoring: Monitor grid voltage, current, and frequency parameters to understand the power supply status of the grid, especially the load-carrying capacity and stability of the grid;

[0058] (2). Schedule the output power of the system to meet the load demand and ensure grid stability:

[0059] 1). Goal 1: Avoid overload: Ensure that the output power of the photovoltaic system is within the load demand range to avoid grid overload;

[0060] 2). Goal 2: Avoid underload: Ensure that the photovoltaic system can provide sufficient power when the grid needs more power;

[0061] 3). Goal 3: Improve system efficiency: Adjust the power output of the photovoltaic system according to the light intensity and load demand to achieve the best efficiency.

[0062] As a further solution of the present invention, the dynamic adjustment of the output power of the photovoltaic system is to dynamically adjust the photovoltaic power generation according to the prediction results and the optimized scheduling strategy, avoiding overload or underload conditions, as follows:

[0063] (1). Output power adjustment:

[0064] 1). Load balancing with the power grid: Adjust the output power of the photovoltaic system according to the load requirements of the power grid. When the load is low, reduce the system's output power to avoid overloading the power grid; when the load is high, increase the power output of the photovoltaic system.

[0065] 2). Adjustment based on predicted load: If an increase or decrease in load demand is predicted, adjust the output of the photovoltaic system in advance to avoid instantaneous overload or power shortage.

[0066] (2). Inverter regulation: The inverter can adjust the output power by regulating the input voltage, current, and power factor to ensure that the photovoltaic system can respond quickly to fluctuations in load demand.

[0067] (3). Energy storage charge and discharge: Store excess electricity in the battery when the light is sufficient, and supply power through the battery when the light is insufficient to maintain a stable power supply.

[0068] As a further solution of the present invention, to achieve dynamic adjustment of power, the following load management strategies are adopted:

[0069] (1). Time-sharing scheduling: Divide the load into different time periods or priorities, and schedule according to the urgency and importance of the load. For example, non-critical loads can be enabled when photovoltaic power generation is sufficient, and critical loads are given priority for power supply.

[0070] (2). Load peak shaving and valley filling: According to the changes in the power grid load, balance the load fluctuations of the power grid by delaying non-essential electricity demands. For example, increase the output of photovoltaic power when the power grid load is low, and appropriately reduce it during the peak load period to avoid excessive load on the power grid.

[0071] As a further solution of the present invention, predicting the future light change trend through a prediction algorithm and dynamically adjusting the operating parameters of the photovoltaic power generation system includes the following:

[0072] (1). Data collection and preparation:

[0073] 1). Meteorological data: Collect historical meteorological data, including solar radiation intensity, cloud cover, temperature, humidity, wind speed. These factors will affect the light intensity and can be obtained through weather stations or weather APIs, such as OpenWeather and meteorological bureau data.

[0074] 2). Historical power generation data: Collect historical power generation data of the photovoltaic power generation system, including power generation power information, equipment operation status information, and light intensity information.

[0075] 3). Geographic information data: Include the installation location, tilt angle, and azimuth of the photovoltaic panels to help calculate the sunshine situation.

[0076] (2). Data preprocessing:

[0077] 1). Data cleaning: Denoise and remove outliers from the collected data to ensure the accuracy and validity of the data;

[0078] 2). Feature engineering: Extract features useful for sunlight prediction from the original data. For example, build a prediction model of sunlight intensity using historical weather data and seasonal changes.

[0079] As a further aspect of the present invention, based on meteorological data such as temperature, humidity, wind speed, combined with historical sunlight data and using regression analysis, the future sunlight intensity can be predicted.

[0080] Compared with the related technologies, the power generation regulation method provided by the present invention for a photovoltaic power generation system has the following beneficial effects:

[0081] 1. Through real-time regulation and multiple feedback mechanisms, the present invention can maintain the optimal power generation efficiency of the photovoltaic system under different sunlight, temperature, and load conditions, thereby improving the energy utilization rate of the entire system;

[0082] 2. By means of temperature compensation, load regulation, dynamic adjustment, etc., the present invention can effectively cope with environmental changes and system load fluctuations, improve the stability and reliability of the photovoltaic power generation system, and reduce power fluctuations caused by environmental changes;

[0083] 3. Through remote monitoring, the system can keep track of the health status of the photovoltaic power generation system in real time, reduce the failure rate, extend the service life of the equipment, and thus reduce the long-term operation cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the drawings.

[0085] Figure 1 is a schematic flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0086] Please refer to Figure 1 , wherein, Figure 1 is a schematic flow chart of the present invention. The power generation regulation method for a photovoltaic power generation system includes the following steps:

[0087] S1. Real-time monitoring and data collection:

[0088] (1). By deploying a variety of sensors in the photovoltaic power generation system, real-time monitor solar radiation intensity data, photovoltaic module temperature data, output voltage data of the battery panel, current data, and output data of the inverter through a variety of sensors;

[0089] (2). Integrate external meteorological data such as environmental temperature, humidity, and air pressure to achieve all-round system status collection;

[0090] S2. Maximum Power Point Tracking (MPPT) and Optimal Load Regulation:

[0091] (1). When the light conditions change, adopt a multi-objective optimization algorithm to enable real-time adjustment of the inverter's operating mode to ensure maximum system output power;

[0092] (2). According to the load demand and grid status, adopt an intelligent load prediction and adjustment strategy to avoid overloading or underloading, thereby improving the reliability and stability of photovoltaic power generation;

[0093] S3. Temperature Compensation and Thermal Management Mechanism:

[0094] (1). Based on the fact that the working efficiency of photovoltaic modules is closely related to temperature, avoid the decrease in the output power of photovoltaic modules caused by too high temperature;

[0095] (2). Through the temperature monitoring system, when the temperature of the photovoltaic system exceeds the set threshold, the active thermal management mechanism can be started, and the inverter output can be adjusted or the panel angle can be adjusted to reduce the system load and maintain the optimal working temperature of the photovoltaic module;

[0096] S4. Prediction and Dynamic Adjustment of Light Intensity Changes:

[0097] (1). Adopt a prediction algorithm. By analyzing historical data and meteorological prediction information, the trend of light intensity changes in the future period can be predicted, and the operating parameters of the power generation system can be dynamically adjusted;

[0098] (2). Under cloudy, rainy or other complex weather conditions, it can effectively avoid power generation fluctuations caused by excessive light intensity fluctuations and ensure stable system output;

[0099] S5. Adaptive Control Strategy and Intelligent Learning:

[0100] (1). Utilize machine learning algorithms to continuously optimize the control strategy according to various data during the operation of the system, such as output power, environmental temperature, and light intensity;

[0101] (2). Through adaptive control, the system can dynamically adjust the operating mode according to real-time data, thereby achieving the best power generation efficiency;

[0102] S6. Remote Monitoring and Fault Diagnosis:

[0103] (1). Based on the remote monitoring function of the system, the operating status, fault alarm, and maintenance requirement information of the photovoltaic power generation system are displayed in real time;

[0104] (2). Combine data analysis and fault diagnosis technologies to quickly identify potential faults or performance degradation issues in the system, and issue alerts in a timely manner to provide decision-making support for maintenance personnel.

[0105] The multiple sensors include, but are not limited to, solar radiation intensity sensors, photovoltaic module temperature sensors, panel output voltage sensors, panel output current sensors, and inverter output data sensors. All the multiple sensors aggregate the measurement data through a data acquisition system (DAS), which specifically includes sensor data acquisition, data transmission, and data storage and processing. The sensor data acquisition is as follows:

[0106] (1). Data acquisition module (DAQ): The output signals of all sensors are digitized through the data acquisition module. The DAQ module can convert different types of signals, such as analog voltage and current signals, into digital signals for unified management.

[0107] (2). Data acquisition frequency: Select an appropriate data acquisition frequency according to the dynamic characteristics of the photovoltaic power generation system.

[0108] The data transmission is as follows:

[0109] (1). Wireless transmission: For distributed photovoltaic systems, data can be transmitted wirelessly, such as via Wi-Fi, Zigbee, LoRa, to a remote server or cloud platform. The wireless communication device should be installed in the system's monitoring center or integrated with the acquisition device.

[0110] (2). Wired transmission: If the scale of the photovoltaic system is large or the transmission distance is far, then a wired method, such as RS485, Ethernet, Modbus protocol, can be used to transmit data from each sensor to the central processor.

[0111] The data storage and processing are as follows:

[0112] (1). Data storage: Save the data collected by the sensors through a cloud platform, database, or local storage system. The data can be stored by time periods such as daily, weekly, and monthly for easy later query and analysis.

[0113] (2). Data processing: After the sensor data is stored, it can be further analyzed through a data analysis and processing platform to obtain the operating status information, power generation efficiency information, and fault warning information of the photovoltaic power generation system. The data processing algorithms include maximum power point tracking (MPPT), efficiency calculation, and fault diagnosis.

[0114] The maximum power point tracking (MPPT) and the dynamic regulation of the inverter are as follows:

[0115] (1). To achieve real-time adjustment of the inverter operating mode, it is necessary to monitor the change of ambient light in real time, as follows:

[0116] 1). Light monitoring: Install a solar radiation sensor, such as a radiometer, to obtain solar radiation intensity data in real time;

[0117] 2). Temperature monitoring: Monitor the temperature of the photovoltaic module to avoid the influence of high temperature on efficiency;

[0118] 3). Output voltage and current monitoring of photovoltaic panels: Obtain the output data of photovoltaic panels in real time through voltage and current sensors;

[0119] (2). Adopt a multi-objective optimization algorithm, and adjust the operating parameters of the system in real time according to the current environmental data, such as light, temperature, and the state of photovoltaic panels, such as voltage and current. The algorithm is as follows:

[0120] 1). Maximize power output: Adjust the inverter operating state so that the system output power reaches the maximum. The formula is as follows:

[0121] P max =f(V out ,I out )

[0122] Among them, P max is the maximum power, and (V out ,I out ) are the output voltage and current of the photovoltaic system respectively;

[0123] (3). Optimize the inverter operating mode: Ensure that the inverter is in the best operating state to improve system efficiency, as follows:

[0124] Efficiency=g(Power,Factor,Vin,Iin)

[0125] Among them, Vin and Iin are the voltage and current input to the inverter, and the power factor is the efficiency index of the inverter.

[0126] The adjustment of the inverter operating mode is specifically as follows:

[0127] (1). Maximum power point tracking (MPPT): The inverter calculates the maximum power point based on the output voltage and current of the photovoltaic panel, and adjusts its input voltage and current so that the system always operates at the maximum power point, as follows:

[0128] 1). Perturb and observe method: Find the maximum power point by observing the power change through small perturbations;

[0129] 2). Incremental conductance method: Adjust the maximum power point in real time through the change of conductance;

[0130] (2). Inverter regulation:

[0131] 1). Input voltage regulation: The inverter can adjust its input voltage according to the voltage output of the photovoltaic module, so that the system can output the maximum power under different lighting conditions;

[0132] 2). Working mode switching: The inverter may adjust its working mode according to the load requirements or changes in light intensity, such as switching from grid-connected mode to off-grid mode, or changing the power factor;

[0133] (3). Dynamic load management: When the lighting conditions change, the system can dynamically adjust the load according to the current power output and load demand, avoiding overload or underload, and improving the stability and power utilization rate of the system.

[0134] In the photovoltaic power generation system, the specific method steps of intelligent load prediction and adjustment strategy according to load demand and grid status can effectively avoid overload or underload and ensure the stability and reliability of the system are as follows:

[0135] (1). Real-time monitoring of load demand and grid status:

[0136] 1). Load monitoring: By installing load sensors and smart meters, the power consumption of each load is monitored in real time to obtain load demand information;

[0137] 2). Grid status monitoring: Monitor grid voltage, current, and frequency parameters to understand the power supply status of the grid, especially the load-carrying capacity and stability of the grid;

[0138] (2). Scheduling the output power of the system to meet load demand and ensure grid stability:

[0139] 1). Goal 1: Avoid overload: Ensure that the output power of the photovoltaic system is within the load demand range to avoid grid overload;

[0140] 2). Goal 2: Avoid underload: Ensure that the photovoltaic system can provide sufficient power when the grid needs more power;

[0141] 3). Goal 3: Improve system efficiency: Adjust the power output of the photovoltaic system according to light intensity and load demand to achieve the best efficiency.

[0142] The dynamic adjustment of the output power of the photovoltaic system is based on the prediction results and optimization scheduling strategy. The system dynamically adjusts the photovoltaic power generation amount to avoid overload or underload, as follows:

[0143] (1). Output power adjustment:

[0144] 1). Load balancing with the power grid: Adjust the output power of the photovoltaic system according to the load requirements of the power grid. When the load is low, reduce the system's output power to avoid overloading the power grid; when the load is high, increase the power output of the photovoltaic system;

[0145] 2). Adjustment based on predicted load: If an increase or decrease in load demand is predicted, adjust the output of the photovoltaic system in advance to avoid instantaneous overload or power shortage;

[0146] (2). Inverter regulation: The inverter can adjust the output power by regulating the input voltage, current, and power factor to ensure that the photovoltaic system can respond quickly to fluctuations in load demand;

[0147] (3). Energy storage charge and discharge: Store excess electricity in the battery when sunlight is sufficient, and supply power through the battery when sunlight is insufficient to maintain a stable power supply.

[0148] To achieve dynamic adjustment of power, the following load management strategies are adopted:

[0149] (1). Time-of-use scheduling: Divide the load into different time periods or priorities, and schedule according to the urgency and importance of the load. For example, non-critical loads can be enabled when photovoltaic power generation is sufficient, and critical loads are given priority for power supply;

[0150] (2). Load peak shaving and valley filling: According to the changes in the power grid load, balance the load fluctuations of the power grid by delaying non-essential electricity demands. For example, increase the output of photovoltaic power when the power grid load is low, and appropriately reduce it during the peak load period to avoid excessive load on the power grid.

[0151] The prediction of the future light change trend through a prediction algorithm and the dynamic adjustment of the operating parameters of the photovoltaic power generation system include the following:

[0152] (1). Data collection and preparation:

[0153] 1). Meteorological data: Collect historical meteorological data, including solar radiation intensity, cloud cover, temperature, humidity, wind speed. These factors will affect the light intensity and can be obtained through weather stations or weather APIs, such as OpenWeather and meteorological bureau data;

[0154] 2). Historical power generation data: Collect historical power generation data of the photovoltaic power generation system, including power generation power information, equipment operation status information, and light intensity information;

[0155] 3). Geographic information data: Include the installation location, tilt angle, and azimuth of the photovoltaic panels to help calculate the sunshine situation;

[0156] (2). Data preprocessing:

[0157] 1). Data cleaning: Denoise and remove outliers from the collected data to ensure the accuracy and effectiveness of the data;

[0158] 2). Feature engineering: Extract features useful for sunlight prediction from the original data. For example, build a prediction model for sunlight intensity using historical weather data and seasonal variations.

[0159] Based on meteorological data such as temperature, humidity, wind speed, combined with historical sunlight data and using regression analysis, the future sunlight intensity can be predicted.

[0160] Through real-time regulation and multiple feedback mechanisms, the present invention can maintain the optimal power generation efficiency of the photovoltaic system under different sunlight, temperature, and load conditions, thereby improving the energy utilization rate of the entire system;

[0161] By means of temperature compensation, load regulation, dynamic adjustment, etc., the present invention can effectively cope with environmental changes and system load fluctuations, improve the stability and reliability of the photovoltaic power generation system, and reduce power fluctuations caused by environmental changes;

[0162] Through remote monitoring, the system can keep track of the health status of the photovoltaic power generation system in real time, reduce the failure rate, extend the service life of the equipment, and thus reduce the long-term operation cost of the system.

[0163] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A power generation control method for a photovoltaic power generation system, characterized in that: The following steps are involved: S1. Real-time monitoring and data collection: (1) By deploying multiple sensors in the photovoltaic power generation system, the solar radiation intensity data, photovoltaic module temperature data, solar panel output voltage data, current data and inverter output data are monitored in real time through multiple sensors; (2) Combine external meteorological data such as ambient temperature, humidity and air pressure to achieve comprehensive system status collection; S2. Maximum Power Point Tracking (MPPT) and Optimal Load Regulation: (1) When the lighting conditions change, a multi-objective optimization algorithm is used to adjust the inverter's operating mode in real time to ensure maximum system output power; (2) Adopting intelligent load prediction and adjustment strategies based on load demand and grid status can avoid overload or underload, thereby improving the reliability and stability of photovoltaic power generation; S3. Temperature compensation and thermal management mechanism: (1) Based on the close relationship between the working efficiency of photovoltaic modules and temperature, to avoid excessive temperature causing the output power of photovoltaic modules to drop; (2) Through the temperature monitoring system, when the temperature of the photovoltaic system exceeds the set threshold, the system load can be reduced and the optimal operating temperature of the photovoltaic module can be maintained by activating the active thermal management mechanism and adjusting the inverter output or adjusting the angle of the solar panel; S4. Prediction of lighting changes and dynamic adjustment: (1) Using prediction algorithms, by analyzing historical data and weather forecast information, the trend of light changes in the future can be predicted and the operating parameters of the power generation system can be dynamically adjusted; (2) Under cloudy, rainy or other complex weather conditions, it can effectively avoid power generation fluctuations caused by excessive light fluctuations and ensure stable system output; S5. Adaptive control strategy and intelligent learning: (1) Using machine learning algorithms to continuously optimize control strategies based on various data during system operation, such as output power, ambient temperature, and light intensity; (2) Through adaptive control, the system can dynamically adjust the operating mode according to real-time data to achieve optimal power generation efficiency; S6. Remote monitoring and fault diagnosis: (1) Based on the remote monitoring function of the system, the operating status, fault alarm and maintenance demand information of the photovoltaic power generation system are displayed in real time; (2) Combine data analysis and fault diagnosis technology to quickly identify possible system failures or performance degradation problems, and issue alarms in a timely manner to provide decision support for maintenance personnel.

2. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The multiple sensors include but are not limited to solar radiation intensity sensors, photovoltaic module temperature sensors, battery panel output voltage sensors, battery panel output current sensors and inverter output data sensors. The multiple sensors aggregate the measurement data through a data acquisition system (DAS), which specifically includes sensor data acquisition, data transmission and data storage and processing. The sensor data acquisition is specifically as follows: (1) Data acquisition module (DAQ): The output signals of all sensors will be digitized through the data acquisition module. The DAQ module can convert different types of signals, such as analog voltage and current signals, into digital signals for unified management; (2) Data collection frequency: Select the appropriate data collection frequency based on the dynamic characteristics of the photovoltaic power generation system.

3. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The data transmission is specifically as follows: (1) Wireless transmission: For distributed photovoltaic systems, data can be transmitted to remote servers or cloud platforms via wireless methods such as Wi-Fi, Zigbee, and LoRa. Wireless communication equipment should be installed in the system's monitoring center or integrated with the acquisition equipment; (2) Wired transmission: If the scale of the photovoltaic system is large or the transmission distance is long, a wired method such as RS485, Ethernet, or Modbus protocol can be used to transmit data from each sensor to a centralized processor; The data storage and processing are specifically as follows: (1) Data storage: Data collected by sensors is stored through cloud platforms, databases or local storage systems. Data can be stored by day, week or month for easy query and analysis later. (2) Data processing: After the sensor data is stored, it can be further analyzed through the data analysis and processing platform to obtain the operating status information, power generation efficiency information, and fault warning information of the photovoltaic power generation system. The data processing algorithm includes maximum power point tracking (MPPT), efficiency calculation, and fault diagnosis.

4. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The maximum power point tracking (MPPT) and the dynamic adjustment of the inverter are specifically as follows: (1) To achieve real-time adjustment of the inverter working mode, it is necessary to monitor the changes in ambient light in real time, as follows: 1). Light monitoring: Install solar radiation sensors, such as radiometers, to obtain solar radiation intensity data in real time; 2). Temperature monitoring: monitor the temperature of photovoltaic modules to avoid the impact of high temperature on efficiency; 3). Monitoring of photovoltaic panel output voltage and current: obtaining output data of photovoltaic panels in real time through voltage and current sensors; (2) A multi-objective optimization algorithm is used to adjust the system's operating parameters in real time based on current environmental data, such as light, temperature, and the status of the photovoltaic panels, such as voltage and current. The algorithm is as follows: 1). Maximize power output: adjust the inverter working state so that the system output power reaches the maximum. The formula is as follows: P max =f(V out ,I out ) Among them, P max is the maximum power, (V out ,I out ) are the output voltage and current of the photovoltaic system respectively; (3) Optimize the inverter working mode: Ensure that the inverter is in the best working state to improve system efficiency, as follows: Efficiency=g(Power,Factor,Vin,Iin) Among them, Vin and Iin are the voltage and current input to the inverter, and the power factor is the efficiency indicator of the inverter.

5. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The adjustment of the inverter working mode is specifically as follows: (1) Maximum Power Point Tracking (MPPT): The inverter calculates the maximum power point based on the output voltage and current of the photovoltaic panel and adjusts its input voltage and current so that the system always operates at the maximum power point, as follows: 1). Perturbation and observation method: find the maximum power point by observing the power changes through small disturbances; 2). Incremental admittance method: real-time adjustment of the maximum power point through admittance changes; (2). Inverter adjustment: 1). Input voltage regulation: The inverter can adjust its input voltage according to the voltage output of the photovoltaic module, so that the system can output maximum power under different lighting conditions; 2) Working mode switching: The inverter may adjust its working mode according to load requirements or changes in light intensity, such as switching from grid-connected mode to off-grid mode, or changing the power factor; (3) Dynamic load management: When lighting conditions change, the system can dynamically adjust the load according to the current power output and load demand to avoid overload or underload, thereby improving system stability and power utilization.

6. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: In the photovoltaic power generation system, the specific method and steps of performing intelligent load prediction and adjustment strategy according to load demand and grid status can effectively avoid overload or underload and ensure system stability and reliability are as follows: (1) Real-time monitoring of load demand and grid status: 1). Load monitoring: By installing load sensors and smart meters, the power consumption of each load can be monitored in real time to obtain load demand information; 2) Grid status monitoring: monitor grid voltage, current, and frequency parameters to understand the power supply status of the grid, especially the load carrying capacity and stability of the grid; (2) Dispatch the system's output power to meet load demand and ensure grid stability: 1). Goal 1: Avoid overload: Ensure that the output power of the photovoltaic system is within the load demand range to avoid grid overload; 2) Goal 2: Avoid underload: Ensure that the PV system can provide sufficient power when the grid needs more power; 3). Goal 3: Improve system efficiency: Adjust the power output of the photovoltaic system according to light intensity and load demand to achieve optimal efficiency.

7. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The dynamic adjustment of the photovoltaic system output power is based on the prediction results and the optimization scheduling strategy. The system dynamically adjusts the photovoltaic power generation to avoid overload or underload conditions, as follows: (1). Output power adjustment: 1) Load balance with the power grid: adjust the output power of the photovoltaic system according to the load requirements of the power grid. When the load is low, reduce the output power of the system to avoid overloading the power grid; when the load is high, increase the power output of the photovoltaic system; 2) Adjust according to predicted load: If the load demand is predicted to increase or decrease, adjust the output of the photovoltaic system in advance to avoid instantaneous overload or power shortage; (2) Inverter regulation: The inverter can adjust the output power by adjusting the input voltage, current and power factor, ensuring that the photovoltaic system can respond quickly when the load demand fluctuates; (3) Energy storage and charging and discharging: When there is sufficient sunlight, excess electricity is stored in the battery. When there is insufficient sunlight, the battery provides electricity to maintain a stable power supply.

8. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: To achieve dynamic adjustment of power, the following load management strategies are adopted: (1) Time-sharing scheduling: Divide the load into different time periods or priorities and schedule it according to the urgency and importance of the load. For example, non-critical loads can be enabled when photovoltaic power generation is sufficient, and critical loads are given priority power supply; (2) Load peak shaving and valley filling: According to the changes in the grid load, grid load fluctuations can be balanced by delaying non-essential electricity demand. For example, photovoltaic power output can be increased when the grid load is low, and appropriately reduced during peak load periods to avoid excessive grid load.

9. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The prediction algorithm is used to predict the trend of light changes in the future and dynamically adjust the operating parameters of the photovoltaic power generation system, including the following: (1) Data collection and preparation: 1) Meteorological data: Collect historical meteorological data, including solar radiation intensity, cloud coverage, temperature, humidity, and wind speed. These factors will affect the intensity of light and can be obtained through weather stations or meteorological APIs, such as OpenWeather and meteorological bureau data. 2). Historical power generation data: Collect historical power generation data of photovoltaic power generation systems, including power generation information, equipment operation status information, and light intensity information; 3). Geographic information data: including the installation location, tilt angle, and orientation of the photovoltaic panels to help calculate sunshine conditions; (2) Data preprocessing: 1). Data cleaning: denoising and removing outliers from the collected data to ensure the accuracy and validity of the data; 2) Feature engineering: Extract features useful for light prediction from raw data. For example, build a prediction model for light intensity using historical weather data and seasonal changes.

10. The power generation control method for a photovoltaic power generation system according to claim 1, characterized in that: The weather data, such as temperature, humidity, wind speed, combined with historical light data and regression analysis can be used to predict future light intensity.

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