High-efficiency and high-reliability power conversion control method for photovoltaic power generation

Abstract

The invention relates to the technical field of electric power energy storage scheduling, and provides a photovoltaic power generation high-efficiency and high-reliability power conversion control method, which dynamically adjusts equipment parameters by monitoring the environment and equipment state of a photovoltaic power station in real time and utilizing an environment monitoring and early warning model, a maximum power point tracking algorithm and a generated power calculation model, thereby realizing high-efficiency and high-reliability power conversion control of photovoltaic power generation. And the photovoltaic cell is ensured to operate at the maximum power point. If the actual power does not accord with the expectation, the genetic algorithm is adopted to optimize the generation power calculation model, and the photovoltaic power station equipment control command is corrected, the stability and reliability problems of a power conversion system in the extreme environment are solved, the energy conversion efficiency is improved, the operation and maintenance cost and the fault rate are reduced, and efficient and stable operation of the photovoltaic power station is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage dispatching, and in particular to a photovoltaic power generation high-efficiency and high-reliability power conversion control method. Background Art

[0002] Photovoltaic power generation with high efficiency and reliability refers to the efficient and stable conversion of DC power generated by photovoltaic cells into AC power in photovoltaic power generation systems by using advanced technologies and methods to meet the needs of grid access or load use. High efficiency means that the energy conversion efficiency is high during the power conversion process, minimizing energy loss as much as possible. High reliability means that the power conversion system can operate stably under various environmental conditions, reducing the occurrence of failures and maintenance costs.

[0003] In the field of photovoltaic power generation, efficient and reliable power conversion control methods mainly use maximum power point tracking technology and high-efficiency inverter design. Maximum power point tracking technology monitors the working state of photovoltaic cells in real time and adjusts its working point to maintain the maximum power output state, thereby improving energy conversion efficiency. High-efficiency inverter design uses sinusoidal pulse width modulation or space vector pulse width modulation technology to improve the output voltage waveform quality of the inverter and reduce harmonic content. High-efficiency inverter design further reduces the harmonics of the output voltage and improves the power quality by increasing the number of output levels of the inverter. Soft switching technology is applied in inverter design to reduce energy loss during switching and improve inverter efficiency.

[0004] Maximum power point tracking technology can improve energy conversion efficiency, but existing methods such as perturbation observation method and incremental conductance method have stability errors or slow tracking speed. Under rapidly changing environmental conditions such as drastic changes in light intensity, although intelligent control systems provide real-time monitoring and diagnosis functions, under extreme environmental conditions such as high temperature, high humidity, and strong electromagnetic interference, the stability and reliability of the power conversion system are affected. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a photovoltaic power generation high-efficiency and high-reliability power conversion control method to solve the problem that the stability and reliability of the power conversion system are affected under extreme environmental conditions, such as high temperature, high humidity, and strong electromagnetic interference.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides a photovoltaic power generation high efficiency and high reliability power conversion control method, comprising: Step S101, obtaining photovoltaic power station data information, determining photovoltaic power station equipment and photovoltaic power station equipment location information according to the photovoltaic power station data information, matching corresponding data information to be obtained in a preset knowledge base according to the photovoltaic power station equipment and photovoltaic power station equipment location information, and obtaining power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment according to the data information to be obtained; Step S102, receiving the environmental monitoring parameters of the photovoltaic power station, the early warning parameters of the photovoltaic power station, and the expected photovoltaic power generation power, substituting the environmental monitoring parameters of the photovoltaic power station into a preset environmental monitoring model of the photovoltaic power station, using the preset environmental monitoring model of the photovoltaic power station to monitor the operating environmental data of the equipment in the photovoltaic power station, and obtaining the monitoring results of the environmental data of the photovoltaic power station, substituting the early warning parameters of the photovoltaic power station into the early warning model of the photovoltaic power station, and using the early warning model of the photovoltaic power station to warn the operating status of the equipment in the photovoltaic power station, and obtaining the early warning results of the photovoltaic power station; Step S103, substituting the environmental data monitoring results of the photovoltaic power station and the early warning results of the photovoltaic power station into the maximum power point tracking algorithm, calculating the control adjustment parameters of the equipment in the photovoltaic power station, converting the control adjustment parameters of the equipment in the photovoltaic power station into the control commands of the equipment in the photovoltaic power station, and collecting data information after the equipment in the photovoltaic power station executes the control commands of the equipment in the photovoltaic power station; Step S104, calculating the data information after the equipment in the photovoltaic power station executes the control command of the equipment in the photovoltaic power station through a preset photovoltaic power station power generation calculation model, obtaining the photovoltaic power station power generation after the control command is executed, and comparing the photovoltaic power station power generation after the control command is executed with the expected photovoltaic power generation to obtain a comparison result between the actual power and the expected power; Step S105, if the actual power is inconsistent with the expected power in the comparison result between the actual power and the expected power, feature extraction is performed on the data generated in the process of monitoring the operating environment data of the equipment in the photovoltaic power station, and feature extraction is performed on the data generated in the process of using the photovoltaic power station early warning model to warn the operating status of the equipment in the photovoltaic power station to obtain environmental data and early warning data feature sets, and the environmental data and early warning data feature sets, the photovoltaic power station environmental monitoring parameters and the photovoltaic power station early warning parameters are used to optimize the photovoltaic power station power generation calculation model through a genetic algorithm to obtain an optimized photovoltaic power station power generation calculation model, and the optimized photovoltaic power station power generation calculation model is used to calculate the data information after the equipment in the photovoltaic power station executes the photovoltaic power station equipment control command to obtain a corrected photovoltaic power station equipment control command.

[0007] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S101, comprises: Determine the interface and communication protocol for data communication with the photovoltaic power station. The communication protocols include HTTP and MQTT, and obtain the data of the equipment in the photovoltaic power station through the interface.

[0008] The communication protocol data format adopts XML format, and a corresponding encryption model and decryption model are set for the communication protocol data in XML format; Receive data sent by the photovoltaic power station in real time, parse the received data, and extract data information of the photovoltaic power station. The data information of the photovoltaic power station includes equipment data and environmental monitoring parameters.

[0009] The extracted equipment data is classified according to the data type to obtain power generation operation data, temperature data, humidity data and electromagnetic field strength data.

[0010] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S101, comprises: The server identifies the photovoltaic power station equipment corresponding to each data item through the equipment identifier, and the equipment identifier includes the equipment ID and the equipment serial number; The device location information is obtained through pre-set location coordinates or a real-time positioning system, and the corresponding location information is queried in the database according to the device identifier; According to the equipment and location information of the photovoltaic power station, the server matches the corresponding data information to be obtained in the preset knowledge base, which includes the performance parameters, historical operation data and environmental data of the equipment in the photovoltaic power station; After acquiring the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment, the server preprocesses the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment to obtain preprocessed operation data.

[0011] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S102, comprises: Receive environmental monitoring parameters from the photovoltaic power station in real time. The environmental monitoring parameters include the temperature of the equipment in the photovoltaic power station, the humidity of the equipment in the photovoltaic power station, the wind speed of the equipment in the photovoltaic power station, and the light intensity of the equipment in the photovoltaic power station. The early warning parameters include the temperature threshold, the humidity threshold, the equipment failure early warning threshold, and the expected photovoltaic power generation power; Pre-deploy and load the PV power station environmental monitoring model, which is built by training machine learning algorithms based on historical data and historical technical solutions, and is used to evaluate the operating environment status of equipment in the PV power station; The received environmental monitoring parameters are substituted into the loaded PV power station environmental monitoring model.

[0012] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S102, comprises: Use the preset PV power station environmental monitoring model to calculate the input environmental monitoring parameters to monitor the operating environment data of the equipment in the PV power station: After the calculation of the photovoltaic power station environment monitoring model is completed, the photovoltaic power station environment data monitoring results are output. The photovoltaic power station environment data monitoring results include the operating status of the equipment. The operating status of the equipment includes normal, abnormal, warning, real-time values ​​of environmental parameters, and predicted values ​​of environmental parameters; Substitute the received PV power station warning parameters into the PV power station warning model to conduct warning analysis of the equipment operation status. The PV power station warning model determines the equipment operation status based on the preset threshold value to detect potential equipment failures; The environmental monitoring results and early warning results are integrated to form the environmental data monitoring data of the photovoltaic power station. The environmental data monitoring data of the photovoltaic power station includes the equipment operation status, environmental parameters and early warning information.

[0013] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S103, comprises: Integrate the photovoltaic power station environmental data monitoring results and early warning results obtained in step S102, the integrated data set includes real-time monitoring values ​​of temperature, humidity, light intensity environmental parameters, equipment operating status and early warning information, the early warning information includes equipment about to overheat and power generation is lower than expected; The integrated data set is calculated through the maximum power point tracking algorithm, which calculates the optimal operating voltage and current based on environmental data and the real-time status of the equipment, and maximizes the power output of the photovoltaic cell; Based on the output of the maximum power point tracking algorithm, the control adjustment parameters of the equipment in the photovoltaic power station are calculated. The control adjustment parameters of the equipment in the photovoltaic power station include the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, and the charging and discharging rate of the battery pack.

[0014] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S103, comprises: The calculated control adjustment parameters are converted into device control commands, which are sent to the inverter, tracking system, and battery management system in the photovoltaic power station; After receiving the control command, the equipment in the photovoltaic power station performs corresponding operations, including adjusting the output voltage and changing the tilt angle of the photovoltaic panel; Collect data information after the device executes the control command, including the actual working status, output power, current and voltage parameters of the device.

[0015] Furthermore, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S104, comprises: The data information collected after the equipment in the photovoltaic power station executes the control command includes the output power of the inverter, the voltage and current of the photovoltaic panel, and the charge and discharge status of the battery pack.

[0016] Pre-deploy and load the photovoltaic power station power generation calculation model to predict or calculate its power generation according to the operating status of the photovoltaic power station; The preprocessed data is input into a preset photovoltaic power station power generation calculation model, and the photovoltaic power station power generation calculation model calculates the actual power generation of the photovoltaic power station based on the input data light intensity, temperature, equipment status and the logic of the photovoltaic power station power generation calculation model.

[0017] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S104, comprises: The calculated actual power generation is compared with the expected photovoltaic power generation, which is set based on historical data, weather forecasts or user demand.

[0018] Analyze the comparison results of actual power generation and expected photovoltaic power generation. If the comparison results of actual power generation and expected photovoltaic power generation are consistent or the difference between the comparison results of actual power generation and expected photovoltaic power generation is within the preset error range, the operation status of the photovoltaic power station is good and the control command execution is effective. If the difference between the comparison results of actual power generation and expected photovoltaic power generation is not within the preset error range, the operation status of the photovoltaic power station is abnormal. The abnormal operation status of the photovoltaic power station includes equipment failure, environmental condition change or control strategy error.

[0019] Furthermore, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S105, comprises: Extract features from the data generated during environmental data monitoring using a preset photovoltaic power station environmental monitoring model. The features to be extracted include real-time values, historical change trends, extreme values, and other features of environmental parameters such as temperature, humidity, and light intensity; The data generated in the process of using the photovoltaic power station early warning model to warn about the equipment operation status is also feature extracted, including equipment fault warning signals, abnormal status identification, and maintenance history features; Integrate the data features extracted from the environmental monitoring and early warning process to form a set of environmental data and early warning data features; Use genetic algorithms to optimize the photovoltaic power generation calculation model. Take environmental data and early warning data feature sets, photovoltaic power plant environmental monitoring parameters and early warning parameters as inputs to the genetic algorithm. Genetic algorithms generate new candidate solutions for the power generation calculation model and evaluate the new candidate solutions based on the deviation between actual power and expected power, the prediction accuracy of the model, and the stability index of the model. The optimal power generation calculation model is converged to by iterative genetic algorithm, and the optimized power generation calculation model is verified. When the optimized power generation calculation model is verified, the optimized power generation calculation model is deployed on the server and replaces the original power generation calculation model. Use the optimized power generation calculation model to calculate the data information after the equipment in the photovoltaic power station executes the control command, and calculate the power generation power of the photovoltaic power station; According to the calculation results of the optimized model, the control commands of the photovoltaic power station are corrected. The correction includes adjusting the output voltage and frequency of the inverter, changing the tilt angle of the photovoltaic panel, optimizing the charging and discharging strategy of the battery pack, and sending the corrected control commands to the equipment in the photovoltaic power station.

[0020] Beneficial effects of the present invention: By real-time monitoring of the operating environment and equipment status of the photovoltaic power station, the present invention can promptly discover potential problems under extreme environmental conditions, and carry out early warning and processing, thereby improving the stability and reliability of the power conversion system. The extreme environmental conditions include high temperature, high humidity, and strong electromagnetic interference.

[0021] By adopting the maximum power point tracking algorithm, the present invention can dynamically adjust the working state of the equipment in the photovoltaic power station, so that the photovoltaic cells are always kept in the maximum power output state, thereby improving the energy conversion efficiency and reducing energy loss.

[0022] The power calculation model of photovoltaic power station is optimized by genetic algorithm. The present invention can generate new model candidate solutions according to real-time monitoring data and early warning information, and evaluate and optimize them, so that the model can adapt to changing environmental conditions, thereby improving the system's adaptability and intelligence level.

[0023] The present invention collects data information after the equipment executes the control command in real time during the control process, and performs feedback control. The real-time monitoring and feedback mechanism enables the system to respond quickly to environmental changes and adjust the control strategy in time, thereby maintaining the stable operation of the system.

[0024] The present invention can reduce the occurrence of equipment failures and reduce operation and maintenance costs by early warning and timely handling of potential problems. At the same time, the efficient and stable power conversion system also helps to improve the overall economic benefits of the photovoltaic power station.

[0025] In summary, the beneficial effects of the present invention are mainly reflected in improving the stability and reliability of the power conversion system, enhancing the energy conversion efficiency, realizing intelligent optimization and adaptive adjustment, enhancing real-time monitoring and feedback control capabilities, and reducing operation and maintenance costs and failure rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0027] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0029] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0030] See also Figure 1 The present invention provides a photovoltaic power generation high-efficiency and high-reliability power conversion control method, comprising: The present invention provides a photovoltaic power generation high-efficiency and high-reliability power conversion control method, comprising: Step S101, obtaining photovoltaic power station data information, determining photovoltaic power station equipment and photovoltaic power station equipment location information according to the photovoltaic power station data information, matching corresponding data information to be obtained in a preset knowledge base according to the photovoltaic power station equipment and photovoltaic power station equipment location information, and obtaining power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment according to the data information to be obtained; First, the data of the equipment in the photovoltaic power station is obtained through the preset interface and communication protocol (such as HTTP and MQTT).

[0031] Acquire photovoltaic power station data information in real time and analyze the photovoltaic power station data information to extract useful information.

[0032] The extracted data types include equipment data and environmental monitoring parameters. The equipment data is further classified into power generation operation data, temperature data, humidity data, and electromagnetic field strength data.

[0033] Power generation operation data refers to the data directly related to power generation performance generated by a photovoltaic power station during operation, such as the current, voltage, and power generation of photovoltaic panels.

[0034] Temperature data records the temperature of equipment in photovoltaic power plants, including the temperature of key components such as photovoltaic panels, inverters, and batteries. It is very important for evaluating the working status of equipment and preventing overheating.

[0035] Humidity data reflects the humidity conditions of the operating environment of the photovoltaic power station. High humidity may affect the insulation performance and operating stability of the equipment.

[0036] The electromagnetic field strength data is used to monitor the electromagnetic environment around the photovoltaic power station. Strong electromagnetic fields may interfere with the normal operation of the equipment.

[0037] The photovoltaic power station data information is input into the preset photovoltaic power station environment monitoring model to monitor the operating environment status of the equipment in the photovoltaic power station in real time. At the same time, based on the photovoltaic power station data information, the photovoltaic power station early warning model can warn of potential equipment failures to ensure the stable operation of the photovoltaic power station.

[0038] Power generation operation data, temperature data, humidity data and electromagnetic field strength data are also used to calculate the optimal control adjustment parameters of the equipment in the photovoltaic power station, such as the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, etc. The dynamic adjustment of the control parameters is aimed at ensuring that the photovoltaic cells operate at the maximum power point, thereby improving the energy conversion efficiency.

[0039] Before the acquired data is used for monitoring, early warning and control strategy adjustment, it is usually pre-processed to improve the accuracy and reliability of the data. The data information after the control command is executed will also be collected and fed back to evaluate the effect of the control command and make further adjustments and optimizations as needed.

[0040] In summary, the power generation operation data, temperature data, humidity data and electromagnetic field strength data of photovoltaic power station equipment play a vital role in the monitoring, early warning and control process of photovoltaic power stations.

[0041] Step S102, receiving the environmental monitoring parameters of the photovoltaic power station, the early warning parameters of the photovoltaic power station, and the expected photovoltaic power generation power, substituting the environmental monitoring parameters of the photovoltaic power station into a preset environmental monitoring model of the photovoltaic power station, using the preset environmental monitoring model of the photovoltaic power station to monitor the operating environmental data of the equipment in the photovoltaic power station, and obtaining the monitoring results of the environmental data of the photovoltaic power station, substituting the early warning parameters of the photovoltaic power station into the early warning model of the photovoltaic power station, and using the early warning model of the photovoltaic power station to warn the operating status of the equipment in the photovoltaic power station, and obtaining the early warning results of the photovoltaic power station; The photovoltaic power station environmental monitoring model is a model used to evaluate the operating environment status of equipment in a photovoltaic power station. It is constructed by inputting the environmental monitoring parameters of the photovoltaic power station (such as temperature, humidity, wind speed, light intensity, etc.), using historical data and technical solutions to train machine learning algorithms. The main function of the photovoltaic power station environmental monitoring model is to monitor the operating environment data of the equipment in the photovoltaic power station in real time, including calculating the real-time and predicted values ​​of environmental parameters, and evaluating the operating status of the equipment (such as normal, abnormal, warning, etc.). In the photovoltaic power generation high-efficiency and high-reliability power conversion control method, the photovoltaic power station environmental monitoring model is used to monitor the operating environment of the photovoltaic power station in real time, and provide data support for subsequent power conversion control and optimization.

[0042] The photovoltaic power station early warning model is a model that determines the operating status of equipment based on preset thresholds. The photovoltaic power station early warning model warns of potential equipment failures by analyzing the early warning parameters of the photovoltaic power station (such as temperature threshold, humidity threshold, equipment failure early warning threshold, etc.).

[0043] The main function of the photovoltaic power station early warning model is to timely discover potential failures of equipment in the photovoltaic power station and improve the stability and reliability of the system through early warning analysis. It evaluates the operating status of the equipment based on the input early warning parameters and generates early warning information.

[0044] In the photovoltaic power generation control process, the early warning model is combined with the environmental monitoring model to provide real-time and accurate data support for the maximum power point tracking algorithm and the power generation calculation model. When the equipment operation status is abnormal, the early warning model can respond quickly and trigger the corresponding processing mechanism.

[0045] In summary, the environmental monitoring model and early warning model of photovoltaic power stations can improve the stable operation of photovoltaic power stations under various environmental conditions and improve energy conversion efficiency and system reliability through real-time monitoring and early warning analysis in the efficient and reliable power conversion control of photovoltaic power generation.

[0046] Step S103, substituting the environmental data monitoring results of the photovoltaic power station and the early warning results of the photovoltaic power station into the maximum power point tracking algorithm, calculating the control adjustment parameters of the equipment in the photovoltaic power station, converting the control adjustment parameters of the equipment in the photovoltaic power station into the control commands of the equipment in the photovoltaic power station, and collecting data information after the equipment in the photovoltaic power station executes the control commands of the equipment in the photovoltaic power station; The main purpose of the maximum power point tracking algorithm is to dynamically adjust the working point of the photovoltaic cell to maintain the maximum power output state, thereby improving the energy conversion efficiency. By real-time monitoring of the working state of the photovoltaic cell and external environmental conditions (such as light intensity, temperature, etc.), the maximum power point tracking algorithm calculates the current maximum power point of the photovoltaic cell and adjusts its operating voltage and current to achieve this point. In photovoltaic power generation systems, the MPPT algorithm is used to ensure that photovoltaic panels can always output maximum power under changing light conditions.

[0047] After obtaining the environmental data monitoring results and early warning results of the photovoltaic power station, the environmental data monitoring results and early warning results of the photovoltaic power station are substituted into the maximum power point tracking algorithm for calculation. The maximum power point tracking algorithm calculates the optimal operating voltage and current according to the environmental data and the real-time status of the equipment, and maximizes the power output of the photovoltaic cells.

[0048] The generation of control adjustment parameters is based on the output of the maximum power point tracking algorithm to calculate the control adjustment parameters of the equipment in the photovoltaic power station. These parameters include the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, and the charging and discharging rate of the battery pack.

[0049] By dynamically adjusting the operating point of the photovoltaic cell, it ensures that it operates at the maximum power point, thereby maximizing the use of solar energy resources. When environmental conditions such as light intensity and temperature change, the MPPT algorithm can respond quickly and adjust the operating point to maintain maximum power output.

[0050] The maximum power point tracking algorithm is integrated into photovoltaic inverters or other power electronic devices to monitor the output voltage and current of photovoltaic cells in real time and achieve maximum power point tracking by adjusting circuit parameters. During the operation of the maximum power point tracking algorithm, it is necessary to collect the output data of photovoltaic cells in real time and continuously adjust the working point based on this data to form a closed-loop control system.

[0051] In summary, the maximum power point tracking algorithm of a photovoltaic power station is one of the key technologies to improve the energy conversion efficiency of a photovoltaic power generation system. By real-time monitoring and adjusting the working point of a photovoltaic cell, the maximum power point tracking algorithm can maintain the maximum power output of the photovoltaic cell under various environmental conditions.

[0052] Step S104, calculating the data information after the equipment in the photovoltaic power station executes the control command of the equipment in the photovoltaic power station through a preset photovoltaic power station power generation calculation model, obtaining the photovoltaic power station power generation after the control command is executed, and comparing the photovoltaic power station power generation after the control command is executed with the expected photovoltaic power generation to obtain a comparison result between the actual power and the expected power; The photovoltaic power generation calculation model is used to predict or calculate the power generation of the photovoltaic power station according to its operating status. By inputting relevant data of the photovoltaic power station (such as light intensity, temperature, equipment status, etc.), the photovoltaic power generation calculation model can output the actual power generation of the photovoltaic power station.

[0053] The data required by the photovoltaic power station power generation calculation model includes the data information after the equipment in the photovoltaic power station executes the control command, such as the output power of the inverter, the voltage and current of the photovoltaic panel, the charge and discharge status of the battery pack, etc. After preprocessing, the data is used as the input parameter of the photovoltaic power station power generation calculation model.

[0054] The photovoltaic power station power generation calculation model calculates the actual power generation of the photovoltaic power station based on the input data (light intensity, temperature, equipment status, etc.) and the logical algorithm within the photovoltaic power station power generation calculation model.

[0055] When the actual power is inconsistent with the expected power, the power calculation model of the photovoltaic power station is optimized through a genetic algorithm. This optimization process extracts data features from the environmental monitoring and early warning process, and combines real-time monitoring parameters and early warning parameters to generate and evaluate new candidate solutions for the power calculation model.

[0056] Through iterative optimization, an optimized power generation calculation model that can adapt to environmental changes and improve prediction accuracy is finally obtained. The optimized power generation calculation model can more accurately predict the power generation of the photovoltaic power station, thereby improving the stability and reliability of the system.

[0057] The application of photovoltaic power station power generation calculation model helps to timely adjust the control strategy of photovoltaic power station, improve the operation of photovoltaic cells at the maximum power point, and improve energy conversion efficiency. The photovoltaic power station power generation calculation model is an important part of the photovoltaic power generation high-efficiency and high-reliability power conversion control method. Through real-time analysis and calculation of photovoltaic power station operation data, it provides a key basis for the dynamic adjustment of the system.

[0058] The comparison between the actual power generation output by the photovoltaic power plant power generation calculation model and the expected power generation is an important indicator for evaluating the system operation status and the execution effect of control commands. If the two are inconsistent, the optimization process of the photovoltaic power plant power generation calculation model will be triggered to improve the system's adaptability and intelligence level.

[0059] In summary, the power calculation model of photovoltaic power station plays a vital role in the efficient and reliable power conversion control method of photovoltaic power generation. By calculating and optimizing the operation data of photovoltaic power station in real time, the stability of the system is improved.

[0060] Step S105, if the actual power is inconsistent with the expected power in the comparison result between the actual power and the expected power, feature extraction is performed on the data generated in the process of monitoring the operating environment data of the equipment in the photovoltaic power station, and feature extraction is performed on the data generated in the process of using the photovoltaic power station early warning model to warn the operating status of the equipment in the photovoltaic power station to obtain environmental data and early warning data feature sets, and the environmental data and early warning data feature sets, the photovoltaic power station environmental monitoring parameters and the photovoltaic power station early warning parameters are used to optimize the photovoltaic power station power generation calculation model through a genetic algorithm to obtain an optimized photovoltaic power station power generation calculation model, and the optimized photovoltaic power station power generation calculation model is used to calculate the data information after the equipment in the photovoltaic power station executes the photovoltaic power station equipment control command to obtain a corrected photovoltaic power station equipment control command.

[0061] When optimizing the calculation model of photovoltaic power station power generation, the present invention needs to clarify the optimization goal, such as improving the prediction accuracy and stability of the model, or maximizing the power generation of the photovoltaic power station. Genetic algorithm is used for optimization. Genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanism. It initializes a population, and then continuously generates new individuals through operations such as selection, crossover and mutation, and evaluates their fitness to find the optimal solution. When optimizing the calculation model of photovoltaic power station power generation, genetic algorithm can be used to search for the optimal model parameter configuration.

[0062] During the operation of the genetic algorithm, the performance of the generated model needs to be continuously evaluated and the model needs to be optimized based on the evaluation results. This usually involves adjusting the parameters and structure of the model to improve the prediction accuracy and stability of the model. When the genetic algorithm finds the optimal model configuration, the model needs to be verified to improve its effectiveness and stability in practical applications. After verification, the optimized model can be deployed to the control system of the photovoltaic power station to improve the power generation efficiency and economic benefits of the photovoltaic power station.

[0063] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S101, comprises: Determine the interface and communication protocol for data communication with the photovoltaic power station. The communication protocols include HTTP and MQTT, and obtain the data of the equipment in the photovoltaic power station through the interface.

[0064] The communication protocol data format adopts XML format, and a corresponding encryption model and decryption model are set for the communication protocol data in XML format; Receive data sent by the photovoltaic power station in real time, parse the received data, and extract data information of the photovoltaic power station. The data information of the photovoltaic power station includes equipment data and environmental monitoring parameters.

[0065] The extracted equipment data is classified according to the data type to obtain power generation operation data, temperature data, humidity data and electromagnetic field strength data.

[0066] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S101, comprises: The server identifies the photovoltaic power station equipment corresponding to each data item through the equipment identifier, and the equipment identifier includes the equipment ID and the equipment serial number; The device location information is obtained through pre-set location coordinates or a real-time positioning system, and the corresponding location information is queried in the database according to the device identifier; According to the equipment and location information of the photovoltaic power station, the server matches the corresponding data information to be obtained in the preset knowledge base, which includes the performance parameters, historical operation data and environmental data of the equipment in the photovoltaic power station; After acquiring the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment, the server preprocesses the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment to obtain preprocessed operation data.

[0067] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S102, includes: Receive environmental monitoring parameters from the photovoltaic power station in real time. The environmental monitoring parameters include the temperature of the equipment in the photovoltaic power station, the humidity of the equipment in the photovoltaic power station, the wind speed of the equipment in the photovoltaic power station, and the light intensity of the equipment in the photovoltaic power station. The early warning parameters include the temperature threshold, the humidity threshold, the equipment failure early warning threshold, and the expected photovoltaic power generation power; Pre-deploy and load the PV power station environmental monitoring model, which is built by training machine learning algorithms based on historical data and historical technical solutions, and is used to evaluate the operating environment status of equipment in the PV power station; The received environmental monitoring parameters are substituted into the loaded PV power station environmental monitoring model.

[0068] Receive environmental monitoring parameters from the data acquisition system of the photovoltaic power station in real time. These parameters are collected in real time through various sensors installed in the photovoltaic power station (such as temperature sensors, humidity sensors, wind speed sensors, light intensity sensors, etc.).

[0069] The received raw data undergoes preprocessing steps, including data cleaning, outlier detection and processing, and data standardization, to ensure the quality and consistency of the data and provide an accurate and reliable data basis for subsequent environmental monitoring and early warning.

[0070] The PV power station environmental monitoring model is built based on a large amount of historical data and historical technical solutions by training machine learning algorithms. It can learn and identify the relationship between the environmental parameters of the PV power station and the operating status of the equipment.

[0071] When building a model, choose a suitable machine learning algorithm, such as random forest, support vector machine, neural network, etc., and use historical data for training and optimization to improve the model's predictive accuracy and generalization ability.

[0072] After training, the model will be deployed to the control system of the photovoltaic power station and loaded when needed to evaluate the operating environment status of the equipment in the photovoltaic power station in real time.

[0073] When real-time environmental monitoring parameters are received from the PV power station, the parameters will be substituted into the loaded PV power station environmental monitoring model. The model will evaluate and predict the operating environment status of the equipment in the PV power station based on the parameter input and the knowledge and rules learned internally.

[0074] The evaluation results can help the control system understand the impact of the current environment on the efficiency of photovoltaic power generation, as well as potential risks and problems that may exist.

[0075] In addition to environmental monitoring parameters, the system also receives a series of warning parameters including temperature thresholds, humidity thresholds, equipment failure warning thresholds, etc.

[0076] When the evaluation results output by the environmental monitoring model exceed these warning thresholds, the corresponding warning mechanism is triggered, such as sending alarm information, initiating emergency response procedures, etc., to ensure the safe and stable operation of the photovoltaic power station.

[0077] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S102, includes: Use the preset PV power station environmental monitoring model to calculate the input environmental monitoring parameters to monitor the operating environment data of the equipment in the PV power station: After the calculation of the photovoltaic power station environmental monitoring model is completed, the photovoltaic power station environmental data monitoring results are output. The photovoltaic power station environmental data monitoring results include the operating status of the equipment. The operating status of the equipment includes normal, abnormal, warning, real-time values ​​of environmental parameters, and predicted values ​​of environmental parameters; Substitute the received PV power station warning parameters into the PV power station warning model to conduct warning analysis of the equipment operation status. The PV power station warning model determines the equipment operation status based on the preset threshold value to detect potential equipment failures; The environmental monitoring results and early warning results are integrated to form the environmental data monitoring data of the photovoltaic power station. The environmental data monitoring data of the photovoltaic power station includes the equipment operation status, environmental parameters and early warning information.

[0078] When the environmental monitoring parameters are input into the PV power plant environmental monitoring model, the PV power plant environmental monitoring model processes and analyzes these parameters using built-in algorithms and logic.

[0079] The PV power plant environmental monitoring model uses a variety of statistical methods, machine learning techniques or deep learning networks to identify the complex relationship between environmental parameters and equipment operating status. During the calculation process, the PV power plant environmental monitoring model will combine historical data and real-time data to comprehensively evaluate the equipment's operating environment, including the impact of factors such as temperature, humidity, wind speed and light intensity on equipment performance.

[0080] After the calculation of the photovoltaic power station environmental monitoring model is completed, detailed photovoltaic power station environmental data monitoring results will be output. The photovoltaic power station environmental data monitoring results not only include the current operating status of the equipment (such as normal, abnormal, warning), but also provide real-time values ​​of environmental parameters and future values ​​predicted by the model. The real-time value reflects the actual situation of the current environment, while the predicted value helps to understand the possible changes in environmental parameters in advance, providing foresight for the operation and maintenance of the equipment.

[0081] The received PV power plant warning parameters will be substituted into the PV power plant warning model to conduct warning analysis of the equipment operation status. The PV power plant warning model determines whether the equipment operation status is close to or exceeds the safety range based on preset thresholds (such as temperature threshold, humidity threshold, equipment failure warning threshold, etc.). When a potential equipment failure risk is detected, the PV power plant warning model immediately issues a warning signal so that preventive measures can be taken in time to avoid the occurrence or expansion of the failure.

[0082] Environmental monitoring results and early warning results are integrated together to form comprehensive PV power station environmental data monitoring data, which not only includes the real-time operating status of the equipment and the real-time values ​​of environmental parameters, but also integrates early warning information to provide the PV power station operators with the operating status of the PV power station. Operators can identify problem areas more quickly and accurately, optimize equipment operation and maintenance strategies, and thus improve the overall performance and reliability of the PV power station.

[0083] In summary, step S102 realizes comprehensive monitoring and early warning analysis of the operating environment of photovoltaic power station equipment by comprehensively using the environmental monitoring model and the early warning model, providing key data support and decision-making basis for subsequent power conversion control.

[0084] Specifically, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S103, comprises: Integrate the photovoltaic power station environmental data monitoring results and early warning results obtained in step S102. The integrated data set includes real-time monitoring values ​​of environmental parameters such as temperature, humidity, and light intensity, the operating status of the equipment, and early warning information. The early warning information includes that the equipment is about to overheat and the power generation is lower than expected; The integrated data set is calculated through the maximum power point tracking algorithm, which calculates the optimal operating voltage and current based on environmental data and the real-time status of the equipment, and maximizes the power output of the photovoltaic cell; Based on the output of the maximum power point tracking algorithm, the control adjustment parameters of the equipment in the photovoltaic power station are calculated. The control adjustment parameters of the equipment in the photovoltaic power station include the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, and the charging and discharging rate of the battery pack.

[0085] The PV power station environmental data monitoring results and early warning results obtained in step S102 are integrated into a data set. The data set not only includes real-time monitoring values ​​of environmental parameters such as temperature, humidity, and light intensity, but also integrates the operating status of the equipment (such as normal, abnormal, warning, etc.) and specific early warning information (for example, the equipment is about to overheat, the power generation is lower than expected, etc.).

[0086] Before integrating the data, data cleaning and preprocessing are performed to ensure data accuracy and consistency and exclude outliers or noise data. The integrated data set is input into the maximum power point tracking (MPPT) algorithm.

[0087] The maximum power point tracking algorithm is an optimization technique used to maximize the power output of photovoltaic cells under given environmental conditions. It continuously adjusts the operating point of the photovoltaic system to find the voltage and current combination that can produce the maximum power output. The maximum power point tracking algorithm uses different strategies such as the perturbation observation method and the incremental conductance method to achieve maximum power point tracking. The strategy dynamically adjusts the operating voltage and current based on environmental data and the real-time status of the device.

[0088] Based on the output of the MPPT algorithm, the system calculates the control adjustment parameters of the equipment in the photovoltaic power station. The parameters are designed to optimize the operating status of the equipment to improve the power generation efficiency and reliability of the photovoltaic power station. Specific control adjustment parameters include the adjustment values ​​of the inverter's output voltage and frequency to improve power quality and reduce losses.

[0089] For photovoltaic panels, the optimal tilt angle adjustment value may be calculated to better receive sunlight and improve photovoltaic conversion efficiency. For battery packs, the appropriate charge and discharge rates are calculated to balance the battery's life and performance.

[0090] The calculated control adjustment parameters are applied to the equipment of the photovoltaic power station in real time. The operating status of the equipment and changes in environmental conditions are continuously monitored, and dynamic adjustments are made according to actual conditions. Through this real-time feedback and adjustment mechanism, the system can continuously optimize the power generation performance of the photovoltaic power station and cope with various environmental and operational challenges.

[0091] In summary, step S103 realizes precise control and optimization adjustment of photovoltaic power station equipment by integrating environmental data monitoring results and early warning results and applying the maximum power point tracking algorithm, which not only improves the power generation efficiency and reliability of the photovoltaic power station, but also provides operators with more intelligent and efficient management means.

[0092] Specifically, the photovoltaic power generation high efficiency and high reliability power conversion control method provided by the present invention, step S103, comprises: The calculated control adjustment parameters are converted into device control commands, which are sent to the inverter, tracking system, and battery management system in the photovoltaic power station; After receiving the control command, the equipment in the photovoltaic power station performs corresponding operations, including adjusting the output voltage and changing the tilt angle of the photovoltaic panel; Collect data information after the device executes the control command, including the actual working status, output power, current and voltage parameters of the device.

[0093] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S104, includes: The data information collected after the equipment in the photovoltaic power station executes the control command includes the output power of the inverter, the voltage and current of the photovoltaic panel, and the charge and discharge status of the battery pack.

[0094] Pre-deploy and load the photovoltaic power station power generation calculation model to predict or calculate its power generation according to the operating status of the photovoltaic power station; The preprocessed data is input into a preset photovoltaic power station power generation calculation model, and the photovoltaic power station power generation calculation model calculates the actual power generation of the photovoltaic power station based on the input data light intensity, temperature, equipment status and the logic of the photovoltaic power station power generation calculation model.

[0095] After executing the control command, relevant data information of the equipment in the photovoltaic power station is collected, including but not limited to the output power of the inverter, the voltage and current of the photovoltaic panel, and the charging and discharging status of the battery pack.

[0096] In order to accurately predict and calculate the power generation of the photovoltaic power station, the photovoltaic power station power generation calculation model is deployed and loaded in advance. The photovoltaic power station power generation calculation model predicts or calculates its power generation according to the operating status of the photovoltaic power station, providing data support for subsequent control and adjustment.

[0097] The collected raw data undergoes a preprocessing step, which includes data cleaning, outlier detection and processing, etc., to provide a reliable data basis for subsequent power calculations.

[0098] The preprocessed data is input into the loaded photovoltaic power station power generation calculation model. The photovoltaic power station power generation calculation model calculates the actual power generation of the photovoltaic power station based on the input data (such as light intensity, temperature, equipment status, etc.) and internal logic algorithms. The actual power generation of the photovoltaic power station is used to evaluate the operating efficiency and performance of the photovoltaic power station.

[0099] Compare the calculated actual power generation with the expected photovoltaic power generation. If the two are consistent or the difference is within an acceptable range, it is considered that the photovoltaic power station is in good operating condition and the control command is executed effectively. If the difference is large, it may indicate that the operating status of the photovoltaic power station is abnormal and further investigation is needed.

[0100] When the actual power is found to be inconsistent with the expected power, corresponding abnormal handling measures are taken, such as further inspection and maintenance of photovoltaic power station equipment to ensure its normal operation. At the same time, this inconsistency may also trigger subsequent model optimization processes, such as using genetic algorithms to optimize the photovoltaic power generation power calculation model to improve the accuracy of prediction and the efficiency of control.

[0101] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S104, includes: The calculated actual power generation is compared with the expected photovoltaic power generation, which is set based on historical data, weather forecasts or user demand.

[0102] Analyze the comparison results of actual power generation and expected photovoltaic power generation. If the comparison results of actual power generation and expected photovoltaic power generation are consistent or the difference between the comparison results of actual power generation and expected photovoltaic power generation is within the preset error range, the operation status of the photovoltaic power station is good and the control command execution is effective. If the difference between the comparison results of actual power generation and expected photovoltaic power generation is not within the preset error range, the operation status of the photovoltaic power station is abnormal. The abnormal operation status of the photovoltaic power station includes equipment failure, environmental condition change or control strategy error.

[0103] The expected photovoltaic power generation is set based on historical data, weather forecasts or user needs. These data are processed and analyzed by special algorithms to obtain a reasonable expected power generation value. The actual power generation calculated in real time is compared with this expected value. In order to evaluate the comparison results, a preset error range is set, which is determined based on the historical operation data of the photovoltaic power station, equipment performance and industry standards. The setting of the error range takes into account a variety of factors, including normal fluctuations of equipment, slight changes in weather conditions, etc., to ensure the rationality and accuracy of the comparison results.

[0104] If the actual power generation is consistent with the expected photovoltaic power generation, or the difference is within the preset error range, then it can be considered that the operation status of the photovoltaic power station is good and the control command execution is effective.

[0105] If the difference between the actual power generation and the expected photovoltaic power generation exceeds the preset error range, the system will determine that the operating status of the photovoltaic power station is abnormal.

[0106] When an abnormal operating status is found, further analysis is conducted on possible causes, which may include equipment failure, changes in environmental conditions (such as sudden clouds blocking the sun), or improper control strategies.

[0107] In order to accurately identify the cause of the abnormality, an alarm is initiated so that staff can intervene and deal with it in time.

[0108] Specifically, the photovoltaic power generation high-efficiency and high-reliability power conversion control method provided by the present invention, step S105, comprises: Extract features from the data generated during environmental data monitoring using a preset photovoltaic power station environmental monitoring model. The features to be extracted include real-time values, historical change trends, extreme values, and other features of environmental parameters such as temperature, humidity, and light intensity; The data generated in the process of using the photovoltaic power station early warning model to warn about the equipment operation status is also subjected to feature extraction, including equipment fault warning signals, abnormal status identification, and maintenance history features; Integrate the data features extracted from the environmental monitoring and early warning process to form a set of environmental data and early warning data features; Use genetic algorithms to optimize the photovoltaic power generation calculation model. Take environmental data and early warning data feature sets, photovoltaic power plant environmental monitoring parameters and early warning parameters as inputs to the genetic algorithm. Genetic algorithms generate new candidate solutions for the power generation calculation model and evaluate the new candidate solutions based on the deviation between actual power and expected power, the prediction accuracy of the model, and the stability index of the model. The optimal power generation calculation model is converged to by iterative genetic algorithm, and the optimized power generation calculation model is verified. When the optimized power generation calculation model is verified, the optimized power generation calculation model is deployed on the server and replaces the original power generation calculation model. Use the optimized power generation calculation model to calculate the data information after the equipment in the photovoltaic power station executes the control command, and calculate the power generation power of the photovoltaic power station; According to the calculation results of the optimized model, the control commands of the photovoltaic power station are corrected. The correction includes adjusting the output voltage and frequency of the inverter, changing the tilt angle of the photovoltaic panel, optimizing the charging and discharging strategy of the battery pack, and sending the corrected control commands to the equipment in the photovoltaic power station.

[0109] Key features are extracted from the data generated during the environmental monitoring process. The features include real-time values ​​of environmental parameters such as temperature, humidity, and light intensity, as well as important information such as their historical change trends and extreme values.

[0110] It also performs feature extraction on the data generated during the equipment operation status warning process, mainly extracting features such as equipment fault warning signals, abnormal status identification, and maintenance history.

[0111] The extracted environmental monitoring and early warning data features will be integrated to form a comprehensive set of environmental data and early warning data features, providing a rich data foundation for the subsequent optimization process.

[0112] Genetic algorithms are used to optimize the photovoltaic power generation calculation model. In this process, the previously constructed feature set, environmental monitoring parameters, and early warning parameters are used as inputs to the genetic algorithm. The genetic algorithm generates new candidate solutions for the power generation calculation model and comprehensively evaluates these candidate solutions based on the deviation between actual power and expected power, the prediction accuracy of the model, and the stability index of the model.

[0113] Through continuous iteration, the genetic algorithm will gradually converge and eventually find the optimal power generation calculation model. This optimized model will undergo a rigorous verification process to ensure its effectiveness and stability in practical applications. Only models that pass the verification will be adopted.

[0114] When the optimized power generation calculation model is verified, it is deployed on the server and replaces the original power generation calculation model so that it can play a role in actual operation.

[0115] After the deployment is completed, the new model is used to perform real-time calculations on the data information after the equipment in the photovoltaic power station executes control commands to obtain the current power generation power of the photovoltaic power station.

[0116] Based on these calculation results, necessary corrections are made to the control commands of the photovoltaic power station, including adjusting the output voltage and frequency of the inverter, changing the tilt angle of the photovoltaic panels, or optimizing the charging and discharging strategies of the battery pack.

[0117] The corrected control commands will be immediately sent to the equipment within the PV power station to ensure that the system can respond quickly to environmental changes and maintain stable operation.

[0118] The present invention effectively solves the stability and reliability problems of the power conversion system under extreme environmental conditions. The present invention monitors the operating environment and equipment status of the photovoltaic power station in real time, obtains power generation operation data, temperature data, humidity data and electromagnetic field strength data, and provides comprehensive information for subsequent monitoring and early warning.

[0119] The preset photovoltaic power station environmental monitoring model is used to monitor real-time environmental data, and the photovoltaic power station early warning model is combined to issue early warnings on the equipment operation status. This can detect potential problems in a timely manner and improve the stability and reliability of the system.

[0120] The environmental monitoring and early warning results are substituted into the maximum power point tracking algorithm to dynamically adjust the working status of the equipment (such as the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, etc.) to ensure that the photovoltaic cells operate at the maximum power point and improve the energy conversion efficiency.

[0121] The actual power generation is calculated using a preset photovoltaic power plant power generation calculation model and compared with the expected photovoltaic power generation. This helps evaluate the execution effect of the control command, identify problems in a timely manner and make optimizations.

[0122] If the actual power is inconsistent with the expected power, the genetic algorithm is used to optimize the photovoltaic power station power calculation model. By extracting the data features in the environmental monitoring and early warning process, new candidate solutions for the power calculation model are generated and evaluated, and finally the optimal model that adapts to environmental changes is obtained.

[0123] During the entire control process, data information after the device executes the control command is collected in real time and feedback control is performed. This mechanism ensures that the system can respond quickly to environmental changes, adjust the control strategy in time, and maintain stable operation of the system.

[0124] In summary, the present invention effectively improves the stability and reliability of photovoltaic power stations under extreme environmental conditions through comprehensive data collection and monitoring, real-time monitoring and early warning, maximum power point tracking and dynamic adjustment, real-time power calculation and comparison, model adaptive optimization and real-time feedback control.

[0125] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A photovoltaic power generation high efficiency and high reliability power conversion control method, characterized in that: include: Step S101, obtaining photovoltaic power station data information, determining photovoltaic power station equipment and photovoltaic power station equipment location information according to the photovoltaic power station data information, matching corresponding data information to be obtained in a preset knowledge base according to the photovoltaic power station equipment and photovoltaic power station equipment location information, and obtaining power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment according to the data information to be obtained; Step S102, receiving the environmental monitoring parameters of the photovoltaic power station, the early warning parameters of the photovoltaic power station, and the expected photovoltaic power generation power, substituting the environmental monitoring parameters of the photovoltaic power station into a preset environmental monitoring model of the photovoltaic power station, using the preset environmental monitoring model of the photovoltaic power station to monitor the operating environmental data of the equipment in the photovoltaic power station, and obtaining the monitoring results of the environmental data of the photovoltaic power station, substituting the early warning parameters of the photovoltaic power station into the early warning model of the photovoltaic power station, and using the early warning model of the photovoltaic power station to warn the operating status of the equipment in the photovoltaic power station, and obtaining the early warning results of the photovoltaic power station; Step S103, substituting the environmental data monitoring results of the photovoltaic power station and the early warning results of the photovoltaic power station into the maximum power point tracking algorithm, calculating the control adjustment parameters of the equipment in the photovoltaic power station, converting the control adjustment parameters of the equipment in the photovoltaic power station into the control commands of the equipment in the photovoltaic power station, and collecting data information after the equipment in the photovoltaic power station executes the control commands of the equipment in the photovoltaic power station; Step S104, calculating the data information after the equipment in the photovoltaic power station executes the control command of the equipment in the photovoltaic power station through a preset photovoltaic power station power generation calculation model, obtaining the photovoltaic power station power generation after the control command is executed, and comparing the photovoltaic power station power generation after the control command is executed with the expected photovoltaic power generation to obtain a comparison result between the actual power and the expected power; Step S105, if the actual power is inconsistent with the expected power in the comparison result between the actual power and the expected power, feature extraction is performed on the data generated in the process of monitoring the operating environment data of the equipment in the photovoltaic power station, and feature extraction is performed on the data generated in the process of using the photovoltaic power station early warning model to warn the operating status of the equipment in the photovoltaic power station to obtain environmental data and early warning data feature sets, and the environmental data and early warning data feature sets, the photovoltaic power station environmental monitoring parameters and the photovoltaic power station early warning parameters are used to optimize the photovoltaic power station power generation calculation model through a genetic algorithm to obtain an optimized photovoltaic power station power generation calculation model, and the optimized photovoltaic power station power generation calculation model is used to calculate the data information after the equipment in the photovoltaic power station executes the photovoltaic power station equipment control command to obtain a corrected photovoltaic power station equipment control command.

2. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 1, characterized in that: Step S101 includes: Determine the interface and communication protocol for data communication with the photovoltaic power station. The communication protocols include HTTP and MQTT. The data of the equipment in the photovoltaic power station is obtained through the interface. The communication protocol data format adopts XML format, and a corresponding encryption model and decryption model are set for the communication protocol data in XML format; Receive data sent by the photovoltaic power station in real time, analyze the received data, and extract the data information of the photovoltaic power station. The data information of the photovoltaic power station includes equipment data and environmental monitoring parameters; The extracted equipment data is classified according to the data type to obtain power generation operation data, temperature data, humidity data and electromagnetic field strength data.

3. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 2, characterized in that: Step S101 includes: The server identifies the photovoltaic power station equipment corresponding to each data item through the equipment identifier, and the equipment identifier includes the equipment ID and the equipment serial number; The device location information is obtained through pre-set location coordinates or a real-time positioning system, and the corresponding location information is queried in the database according to the device identifier; According to the equipment and location information of the photovoltaic power station, the server matches the corresponding data information to be obtained in the preset knowledge base, which includes the performance parameters, historical operation data and environmental data of the equipment in the photovoltaic power station; After acquiring the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment, the server preprocesses the power generation operation data, temperature data, humidity data and electromagnetic field strength data of the photovoltaic power station equipment to obtain preprocessed operation data.

4. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 1, characterized in that: Step S102 includes: Receive environmental monitoring parameters from the photovoltaic power station in real time. The environmental monitoring parameters include the temperature of the equipment in the photovoltaic power station, the humidity of the equipment in the photovoltaic power station, the wind speed of the equipment in the photovoltaic power station, and the light intensity of the equipment in the photovoltaic power station. The early warning parameters include the temperature threshold, the humidity threshold, the equipment failure early warning threshold, and the expected photovoltaic power generation power; Pre-deploy and load the PV power station environmental monitoring model, which is built by training machine learning algorithms based on historical data and historical technical solutions, and is used to evaluate the operating environment status of equipment in the PV power station; The received environmental monitoring parameters are substituted into the loaded PV power station environmental monitoring model.

5. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 4, characterized in that: Step S102 includes: Use the preset PV power station environmental monitoring model to calculate the input environmental monitoring parameters to monitor the operating environment data of the equipment in the PV power station: After the calculation of the photovoltaic power station environmental monitoring model is completed, the photovoltaic power station environmental data monitoring results are output. The photovoltaic power station environmental data monitoring results include the operating status of the equipment. The operating status of the equipment includes normal, abnormal, warning, real-time values ​​of environmental parameters, and predicted values ​​of environmental parameters; Substitute the received PV power station warning parameters into the PV power station warning model to conduct warning analysis of the equipment operation status. The PV power station warning model determines the equipment operation status based on the preset threshold value to detect potential equipment failures; The environmental monitoring results and early warning results are integrated to form the environmental data monitoring data of the photovoltaic power station. The environmental data monitoring data of the photovoltaic power station includes the equipment operation status, environmental parameters and early warning information.

6. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 1, characterized in that: Step S103 includes: Integrate the photovoltaic power station environmental data monitoring results and early warning results obtained in step S102, the integrated data set includes real-time monitoring values ​​of temperature, humidity, light intensity environmental parameters, equipment operating status and early warning information, the early warning information includes equipment about to overheat and power generation is lower than expected; The integrated data set is calculated through the maximum power point tracking algorithm, which calculates the optimal operating voltage and current based on environmental data and the real-time status of the equipment, and maximizes the power output of the photovoltaic cell; Based on the output of the maximum power point tracking algorithm, the control adjustment parameters of the equipment in the photovoltaic power station are calculated. The control adjustment parameters of the equipment in the photovoltaic power station include the output voltage and frequency of the inverter, the tilt angle of the photovoltaic panel, and the charging and discharging rate of the battery pack.

7. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 6, characterized in that: Step S103 includes: The calculated control adjustment parameters are converted into device control commands, which are sent to the inverter, tracking system, and battery management system in the photovoltaic power station; After receiving the control command, the equipment in the photovoltaic power station performs corresponding operations, including adjusting the output voltage and changing the tilt angle of the photovoltaic panel; Collect data information after the device executes the control command, including the actual working status, output power, current and voltage parameters of the device.

8. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 1, characterized in that: Step S104 includes: Collect data information after the equipment in the photovoltaic power station executes the control command, including the output power of the inverter, the voltage and current of the photovoltaic panel, and the charge and discharge status of the battery pack; Pre-deploy and load the photovoltaic power station power generation calculation model to predict or calculate its power generation according to the operating status of the photovoltaic power station; The preprocessed data is input into a preset photovoltaic power station power generation calculation model, and the photovoltaic power station power generation calculation model calculates the actual power generation of the photovoltaic power station based on the input data light intensity, temperature, equipment status and the logic of the photovoltaic power station power generation calculation model.

9. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 8, characterized in that: Step S104 includes: Compare the calculated actual power generation with the expected photovoltaic power generation, which is based on historical data, weather forecasts or user demand settings; Analyze the comparison results of actual power generation and expected photovoltaic power generation. If the comparison results of actual power generation and expected photovoltaic power generation are consistent or the difference between the comparison results of actual power generation and expected photovoltaic power generation is within the preset error range, the operation status of the photovoltaic power station is good and the control command execution is effective. If the difference between the comparison results of actual power generation and expected photovoltaic power generation is not within the preset error range, the operation status of the photovoltaic power station is abnormal. The abnormal operation status of the photovoltaic power station includes equipment failure, environmental condition change or control strategy error.

10. The photovoltaic power generation high efficiency and high reliability power conversion control method according to claim 1, characterized in that: Step S105 includes: Extract features from the data generated during environmental data monitoring using a preset photovoltaic power station environmental monitoring model. The features to be extracted include real-time values, historical change trends, and extreme value features of environmental parameters such as temperature, humidity, and light intensity. The data generated in the process of using the photovoltaic power station early warning model to warn about the equipment operation status is also subjected to feature extraction, including equipment fault warning signals, abnormal status identification, and maintenance history features; Integrate the data features extracted from the environmental monitoring and early warning process to form a set of environmental data and early warning data features; Use genetic algorithms to optimize the photovoltaic power generation calculation model. Take environmental data and early warning data feature sets, photovoltaic power plant environmental monitoring parameters and early warning parameters as inputs to the genetic algorithm. Genetic algorithms generate new candidate solutions for the power generation calculation model and evaluate the new candidate solutions based on the deviation between actual power and expected power, the prediction accuracy of the model, and the stability index of the model. The optimal power generation calculation model is converged to by iterative genetic algorithm, and the optimized power generation calculation model is verified. When the optimized power generation calculation model is verified, the optimized power generation calculation model is deployed on the server and replaces the original power generation calculation model. Use the optimized power generation calculation model to calculate the data information after the equipment in the photovoltaic power station executes the control command, and calculate the power generation power of the photovoltaic power station; According to the calculation results of the optimized model, the control commands of the photovoltaic power station are corrected. The correction includes adjusting the output voltage and frequency of the inverter, changing the tilt angle of the photovoltaic panel, optimizing the charging and discharging strategy of the battery pack, and sending the corrected control commands to the equipment in the photovoltaic power station.

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