Control method for improving stability of power grid by applying photovoltaic synchronous machine pile device

Abstract

The invention relates to the technical field of photovoltaic equipment operation and maintenance, and provides a control method for improving the stability of a power grid by using a photovoltaic synchronous machine pile device, and the method comprises the steps: building a photovoltaic output prediction model through obtaining the photovoltaic synchronous machine pile device, the power grid, a meteorological environment and fault data, and predicting the load of the power grid in combination with real-time power grid data. The method comprises the following steps: comparing a predicted load with an actual demand, calculating an error value, adjusting the output power of a photovoltaic synchronous machine pile device, monitoring the operation data of a power grid in real time, and if the data is abnormal, optimizing an output power control model by using a genetic algorithm; according to the invention, the problems of insufficient voltage stability, inaccurate reactive compensation and slow control speed of the photovoltaic power grid are effectively solved, the stability and operation efficiency of the power grid are improved, and the utilization and green low-carbon development of renewable energy sources are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic equipment operation and maintenance, and in particular to a control method for improving power grid stability by using a photovoltaic synchronous machine stack device. Background Art

[0002] The photovoltaic synchronous machine stack device is a device that combines a photovoltaic power generation system with synchronous generator technology. The photovoltaic synchronous machine stack device can not only provide active power and voltage support in the power system like a traditional synchronous generator, but also take advantage of the photovoltaic power generation system to achieve clean and renewable energy utilization. The photovoltaic synchronous machine stack device converts the direct current generated by photovoltaic modules into alternating current synchronized with the power grid through control algorithms and power electronic conversion technology, and has advanced functions such as rapid response to grid changes and participation in grid frequency and voltage regulation. The photovoltaic synchronous machine stack device is of great significance in improving the grid-connected performance of photovoltaic power generation systems, enhancing grid stability, and promoting the large-scale application of renewable energy.

[0003] The steps to improve the stability of the power grid by using the photovoltaic synchronous machine stack device mainly include the following aspects: First, through the accurate photovoltaic output prediction and weather forecast system, the output power of the photovoltaic synchronous machine stack device is planned in advance, so that the output power matches the power grid demand and reduces the impact of power fluctuations on the power grid. Secondly, the control algorithm built into the photovoltaic synchronous machine stack device is used to monitor the power grid status in real time, and the output power and voltage of the device are quickly adjusted according to the changes in parameters such as the power grid frequency and voltage, providing dynamic reactive power compensation and voltage support for the power grid, and enhancing the stability of the power grid. In addition, the photovoltaic synchronous machine stack device can also participate in the frequency modulation control of the power grid, and assist the power grid in maintaining frequency stability by adjusting its own output. Finally, by optimizing the interface technology between the photovoltaic synchronous machine stack device and the power grid, harmonic pollution can be reduced, the power quality can be improved, and the safe and stable operation of the power grid can be further guaranteed.

[0004] The power grid using the photovoltaic synchronous machine stack device has the following technical pain points in the actual operation process. The output power of photovoltaic power generation is greatly affected by natural factors such as light intensity and temperature, and is intermittent and volatile. This directly leads to insufficient voltage stability of the photovoltaic power grid and the inability to respond to changes in the power grid state in a timely manner, further exacerbating the voltage stability problem and the inaccurate reactive power compensation, which affects the stable operation of the photovoltaic power grid. In order to solve this technical problem, the present invention provides Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device, thereby solving the problems of insufficient voltage stability of the photovoltaic power grid and inaccurate reactive power compensation, which result in slow control speed.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device, comprising:

[0008] Step S101, obtaining operation data of the photovoltaic synchronous machine stack device, operation data of the power grid, meteorological environment data and fault data, wherein the operation data of the photovoltaic synchronous machine stack device includes basic operation parameters of the synchronous machine stack device and status information of the synchronous machine stack device, the operation data of the power grid includes power grid voltage data and power grid current data, and the meteorological environment data includes light intensity, temperature data and weather data, and the operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, meteorological environment data and fault data are obtained and stored in a database;

[0009] Step S102, retrieve the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device from the database, build a photovoltaic output prediction model, obtain the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device, substitute the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device into the photovoltaic output prediction model, output the photovoltaic output prediction result, store the photovoltaic output prediction result, and obtain a photovoltaic output prediction result data set;

[0010] Step S103, obtaining real-time grid operation data and real-time grid demand data, retrieving time information of real-time grid operation data, obtaining time information of data to be retrieved, retrieving time information of data to be retrieved in the photovoltaic output prediction result data set, obtaining a prediction result corresponding to the time information of data to be retrieved, substituting the prediction result corresponding to the time information of data to be retrieved, real-time grid demand data and real-time grid operation data into a preset grid load prediction model, and outputting a real-time grid load prediction result;

[0011] Step S104, obtaining the real-time operation data of the photovoltaic synchronous machine stack device, comparing the real-time grid load prediction result with the real-time grid demand data, obtaining the real-time grid load prediction error value, substituting the real-time grid load prediction error value, the real-time grid operation data and the real-time operation data of the photovoltaic synchronous machine stack device into the output power control model of the photovoltaic synchronous machine stack device, and outputting the output power of the photovoltaic synchronous machine stack device;

[0012] Step S105, after the photovoltaic synchronous machine stack device outputs power to the power grid, the operation data of the power grid is monitored in real time to obtain the real-time operation data of the power grid, and the real-time operation data of the power grid is compared with the preset standard value. If the real-time operation data of the power grid is higher than the preset standard value, the output power control model of the photovoltaic synchronous machine stack device is optimized using a preset genetic algorithm to obtain the optimized output power of the photovoltaic synchronous machine stack device, and the optimized output power of the photovoltaic synchronous machine stack device is transmitted to the power grid.

[0013] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, step S101 is also used for:

[0014] The operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are preprocessed to obtain the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data, the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are integrity checked, the data that pass the integrity check are stored in a database, the data that fail the integrity check are matched in a preset data simulation model knowledge base to obtain a data simulation model corresponding to the data that fail the integrity check, the data simulation model is used to simulate the data that fail the integrity check to obtain simulation data corresponding to the data that fail the integrity check, the simulation data corresponding to the data that fail the integrity check are stored in the database, and the data that fail the integrity check are replaced with the simulation data corresponding to the data that fail the integrity check in the database.

[0015] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, step S102 is also used for:

[0016] The operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are retrieved from the database, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to extract data features to obtain the features of the data to be processed, and the features of the data to be processed are matched in a preset algorithm knowledge base to obtain an algorithm model corresponding to the features of the data to be processed, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to train the algorithm model corresponding to the features of the data to be processed to obtain a photovoltaic output prediction model.

[0017] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S103 is also used for:

[0018] The real-time grid operation data and the data in the photovoltaic output prediction result data set are aligned based on time to obtain aligned data, and the aligned data are tested to obtain aligned data test results, the aligned data test results include aligned successful data and unsuccessful alignment data, and the time information of the unsuccessful alignment data is collected;

[0019] Receiving time information of retrieved real-time power grid operation data, matching the time information of retrieved real-time power grid operation data with the time information of unsuccessful alignment data, and obtaining a time information matching result, wherein the time information matching result includes time matching successful data and time matching unsuccessful data;

[0020] Retrieve the time information corresponding to the data of unsuccessful time matching, and re-perform photovoltaic output prediction through step S102 for the time information corresponding to the data of unsuccessful time matching to obtain the photovoltaic output prediction result of the time information corresponding to the data of unsuccessful time matching.

[0021] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, step S104 is also used for:

[0022] Acquiring the operation data of the photovoltaic synchronous machine stack device in real time through a monitoring system or a sensor, wherein the operation data of the photovoltaic synchronous machine stack device includes the output power, voltage, current, temperature and fault status of the synchronous machine stack device;

[0023] Obtain the real-time grid load forecast result from step S103, and obtain the real-time grid demand data from the grid dispatching system, the real-time grid demand data including the total load demand of the grid and the load distribution of each area;

[0024] The real-time grid load forecast result is compared with the real-time grid demand data, and the deviation degree between the real-time grid load forecast result and the real-time grid demand data is obtained by calculating the error value;

[0025] The calculated real-time grid load forecast error value is stored, and a monitoring mechanism for the error value is established. When the error value exceeds a preset threshold, an alarm is triggered.

[0026] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, step S105 is also used for:

[0027] Obtain the grid operation data from the grid monitoring system in real time, pre-process the real-time grid operation data, and compare the pre-processed real-time grid operation data with the preset standard values ​​one by one. The compared data include the voltage fluctuation range, current over-limit situation, frequency stability and power factor pass rate;

[0028] According to the comparison results, the error value is calculated to obtain the difference between the real-time power grid operation data and the preset standard value, and the result of the error analysis is obtained;

[0029] Based on the results of the error analysis, it is determined whether the operating status of the power grid is within the normal range. If the real-time data exceeds the preset standard value, the alarm mechanism is triggered.

[0030] Furthermore, in the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, step S105 is also used for:

[0031] receiving an optimization target and a population size, wherein the optimization target is to minimize the deviation between the power grid operation data and a preset standard value;

[0032] According to the population size, a set of control model parameter combinations is randomly generated as the initial population. Each control parameter combination of the photovoltaic synchronous machine stack device represents a solution.

[0033] Calculate the deviation between the grid operation data and the preset standard value under each parameter combination, and evaluate the advantages and disadvantages of each parameter combination in the initial population;

[0034] According to the evaluation results, a part of the control parameter combinations of the photovoltaic synchronous machine stack devices are selected as the parent generation for crossover and mutation operations;

[0035] Through the crossover operation, the control parameters of the photovoltaic synchronous machine stack device in the parent generation parameter combination are exchanged to generate a new control parameter combination of the child photovoltaic synchronous machine stack device. Through the mutation operation, the parameters in the control parameter combination of the child photovoltaic synchronous machine stack device are randomly adjusted to increase the diversity of the control parameters of the photovoltaic synchronous machine stack device.

[0036] The deviation between the grid operation data and the preset standard value under each parameter combination is calculated, the advantages and disadvantages of the control combination of the newly generated offspring photovoltaic synchronous machine stack device are evaluated, and sufficient crossover and mutation operations are performed until the set genetic generation is reached or the deviation between the grid operation data and the preset standard value under each parameter combination reaches a threshold. The parameter combination with the smallest deviation between the grid operation data and the preset standard value under each parameter combination is selected from the final population as the optimal solution, that is, the optimized photovoltaic synchronous machine stack device output power control model parameters.

[0037] Beneficial effects of the present invention:

[0038] Through accurate photovoltaic output prediction and grid load prediction, the present invention can plan the output power of the photovoltaic synchronous machine stack device in advance to match it with the grid demand, thereby effectively reducing the impact of power fluctuations on the grid and enhancing the stability of the grid.

[0039] By utilizing the built-in control algorithm of the photovoltaic synchronous machine stack device, the present invention can monitor the state of the power grid in real time, and quickly adjust the output power and voltage of the device according to changes in parameters such as the power grid frequency and voltage, provide dynamic reactive power compensation for the power grid, and further improve the voltage stability of the power grid.

[0040] The present invention monitors the grid operation data in real time and optimizes the output power control model of the photovoltaic synchronous machine stack device using a genetic algorithm when necessary, so as to achieve rapid response and adjustment to the grid state and significantly improve the control response speed. By optimizing the operation of the photovoltaic synchronous machine stack device, the present invention can maximize the use of the output power of the photovoltaic power generation system, reduce dependence on traditional energy, and promote the large-scale application of renewable energy.

[0041] The present invention fully considers the integrity and accuracy of data in the process of data acquisition, processing and model building, and effectively improves the robustness of the system through methods such as data preprocessing, integrity detection and data simulation. By integrating advanced data processing algorithms and control strategies, the present invention realizes intelligent management of photovoltaic synchronous machine stack devices, reduces operation and maintenance costs, and improves management efficiency.

[0042] The application of the present invention is helpful to promote the development of clean energy, reduce greenhouse gas emissions, promote green and low-carbon development, and conform to the trend of global energy transformation and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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 the drawings without paying any creative labor.

[0044] Figure 1 A schematic flow chart of a control method for improving grid stability by using a photovoltaic synchronous machine stack device is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0047] The present invention provides a control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device, comprising:

[0048] Step S101, obtaining operation data of the photovoltaic synchronous machine stack device, operation data of the power grid, meteorological environment data and fault data, wherein the operation data of the photovoltaic synchronous machine stack device includes basic operation parameters of the synchronous machine stack device and status information of the synchronous machine stack device, the operation data of the power grid includes power grid voltage data and power grid current data, and the meteorological environment data includes light intensity, temperature data and weather data, and the operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, meteorological environment data and fault data are obtained and stored in a database;

[0049] In step S101, the present invention provides a comprehensive and systematic data acquisition and storage method, which aims to provide complete data support for subsequent photovoltaic output prediction, grid load prediction and output power control. The specific process is as follows:

[0050] The operating data of the photovoltaic synchronous machine stack device includes the basic operating parameters of the synchronous machine stack device (such as power, voltage, current, frequency, etc.) and the status information of the synchronous machine stack device (such as operating status, fault status, etc.).

[0051] Grid operation data: including grid voltage data and grid current data. Meteorological environment data: including light intensity, temperature data and weather data (such as sunny, cloudy, rainy, snowy, etc.). Since the output power of photovoltaic power generation is greatly affected by meteorological conditions, meteorological environment data is crucial for photovoltaic output prediction.

[0052] Fault data: record any fault information that occurs in the PV synchronous machine stack device or the power grid, including fault type, occurrence time, impact range, etc.

[0053] Data storage: All the data obtained above are stored in the database. The selection and design of the database should take into account the integrity of the data. Through the database, the data can be easily managed and queried, providing convenience for subsequent analysis and prediction.

[0054] Before storing the data in the database, the data needs to be preprocessed to improve its accuracy. Preprocessing operations include data cleaning (such as removing duplicate data, processing missing values, etc.), data format conversion (such as converting data from different sources into a unified format), and data standardization (such as converting data into a dimensionless form for easy comparison and analysis).

[0055] Step S102, retrieve the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device from the database, build a photovoltaic output prediction model, obtain the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device, substitute the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device into the photovoltaic output prediction model, output the photovoltaic output prediction result, store the photovoltaic output prediction result, and obtain a photovoltaic output prediction result data set;

[0056] The operation history data of the photovoltaic synchronous machine pile device and the historical data of the meteorological environment are retrieved from the database. These data are the basis for building a photovoltaic output prediction model, and the historical data can reflect the complex relationship between photovoltaic output and meteorological environment.

[0057] The photovoltaic output prediction model is constructed using the retrieved operation history data of the photovoltaic synchronous machine stack device and the meteorological environment history data. This model can be based on machine learning, deep learning or other statistical methods to predict the output power of the photovoltaic synchronous machine stack device under a given meteorological environment.

[0058] The real-time operation data of the photovoltaic synchronous machine stack device and the real-time meteorological environment data are obtained. The real-time operation data of the photovoltaic synchronous machine stack device and the real-time meteorological environment data are the input of the model prediction and are used to generate real-time photovoltaic output prediction results.

[0059] The real-time operation data of the photovoltaic synchronous machine stack device and the real-time meteorological environment data are substituted into the photovoltaic output prediction model. The photovoltaic output prediction model outputs real-time photovoltaic output prediction results based on the input data and the historical laws it has learned.

[0060] The photovoltaic output prediction results are stored to form a photovoltaic output prediction result data set, which records the photovoltaic output prediction values ​​at different time points, providing an important reference for subsequent grid load prediction and output power control.

[0061] Through step S102, the present invention realizes real-time prediction of the output power of the photovoltaic synchronous machine stack device. The prediction result not only takes into account the operating status of the photovoltaic synchronous machine stack device itself, but also fully considers the influence of meteorological environmental factors, thereby improving the accuracy of the prediction.

[0062] Step S103, obtaining real-time grid operation data and real-time grid demand data, retrieving time information of real-time grid operation data, obtaining time information of data to be retrieved, retrieving time information of data to be retrieved in the photovoltaic output prediction result data set, obtaining a prediction result corresponding to the time information of data to be retrieved, substituting the prediction result corresponding to the time information of data to be retrieved, real-time grid demand data and real-time grid operation data into a preset grid load prediction model, and outputting a real-time grid load prediction result;

[0063] Real-time grid operation data: Obtain grid voltage, current, frequency and other operation data in real time from the grid monitoring system.

[0064] Real-time grid demand data: Obtain real-time grid demand data from the grid dispatching system, including the total load demand of the grid and the load distribution in each area.

[0065] The time information of the real-time grid operation data is retrieved, that is, the timestamp of the grid operation data at the current time point or the most recent time point is obtained. According to the retrieved time information, the photovoltaic output prediction result at the corresponding time point is searched in the photovoltaic output prediction result data set obtained in step S102.

[0066] The photovoltaic output prediction results corresponding to the time information of the data to be retrieved, the real-time grid demand data and the real-time grid operation data are substituted into the preset grid load prediction model. The grid load prediction model is a mathematical model based on historical data, real-time data and predicted data, which is used to predict the load changes of the grid in the future.

[0067] The grid load forecasting model outputs real-time grid load forecasting results based on the input data, combined with the algorithms and parameters within the model. The real-time grid load forecasting results take into account multiple factors such as photovoltaic output, grid demand and current grid operating status, and have high accuracy and reliability.

[0068] Through step S103, the present invention realizes real-time prediction of grid load, provides a reference basis for the subsequent output power control of photovoltaic synchronous machine stack device, helps to achieve grid supply and demand balance, and improves the stability and reliability of the grid. At the same time, through real-time monitoring and prediction of grid load, potential problems in grid operation can be discovered in time, providing decision support for grid dispatching and operation management.

[0069] Step S104, obtaining the real-time operation data of the photovoltaic synchronous machine stack device, comparing the real-time grid load prediction result with the real-time grid demand data, obtaining the real-time grid load prediction error value, substituting the real-time grid load prediction error value, the real-time grid operation data and the real-time operation data of the photovoltaic synchronous machine stack device into the output power control model of the photovoltaic synchronous machine stack device, and outputting the output power of the photovoltaic synchronous machine stack device;

[0070] Real-time operation data acquisition: The operation data of the photovoltaic synchronous machine stack device is acquired in real time through the monitoring system or sensor, including the output power, voltage, current, temperature and fault status of the synchronous machine stack device. These data reflect the current operation status of the photovoltaic synchronous machine stack device.

[0071] Comparison of load forecast and demand: Compare the real-time grid load forecast result obtained in step S103 with the real-time grid demand data. The real-time grid demand data is obtained from the grid dispatching system, including the total load demand of the grid and the load distribution of each area.

[0072] By comparison, the real-time grid load prediction error value is calculated. This error value reflects the deviation between the predicted load and the actual demand, and is an important indicator for measuring the accuracy of load prediction.

[0073] The real-time grid load prediction error value, the real-time grid operation data (such as voltage, current, etc.) and the real-time operation data of the photovoltaic synchronous machine stack device are substituted into the output power control model of the photovoltaic synchronous machine stack device. The output power control model is a model based on physical principles, mathematical algorithms and experimental data, which is used to calculate and output the optimal output power of the photovoltaic synchronous machine stack device according to the input data.

[0074] The output power control model calculates the optimal output power of the photovoltaic synchronous machine stack device based on the input data, combined with the algorithm and parameters within the model, and outputs this output power to the actuator of the photovoltaic synchronous machine stack device.

[0075] Through step S104, the present invention realizes real-time control of the output power of the photovoltaic synchronous machine stack device. The real-time control process of the output power of the photovoltaic synchronous machine stack device fully considers the grid load prediction results, the actual grid demand and the operation status of the photovoltaic synchronous machine stack device itself, which helps to achieve the supply and demand balance of the photovoltaic grid and improve the stability and reliability of the grid. At the same time, by adjusting the output power of the photovoltaic synchronous machine stack device in real time, it is also possible to maximize the use of renewable energy, reduce dependence on traditional energy, and promote green and low-carbon development.

[0076] Step S105, after the photovoltaic synchronous machine stack device outputs power to the power grid, the operation data of the power grid is monitored in real time to obtain the real-time operation data of the power grid, and the real-time operation data of the power grid is compared with the preset standard value. If the real-time operation data of the power grid is higher than the preset standard value, the output power control model of the photovoltaic synchronous machine stack device is optimized using a preset genetic algorithm to obtain the optimized output power of the photovoltaic synchronous machine stack device, and the optimized output power of the photovoltaic synchronous machine stack device is transmitted to the power grid.

[0077] Step S105 is an important step in the present invention for real-time monitoring of the grid operation status, evaluating and optimizing the output power control model of the photovoltaic synchronous machine stack device. The following is a detailed interpretation of this step:

[0078] After the photovoltaic synchronous machine stack device transmits the output power to the power grid, the power grid operation data is obtained in real time through the power grid monitoring system, including parameters such as voltage, current, frequency, and power factor.

[0079] Comparison of operating data with preset standard values: Comparison of real-time monitored grid operating data with preset standard values. These preset standard values ​​are formulated based on grid operation safety specifications, performance indicators and historical experience, and are used to evaluate the stability and safety of the current grid operation status.

[0080] Judgment and optimization conditions: If the real-time grid operation data is higher than the preset standard value, it indicates that the current grid operation state is unstable or has safety risks and needs to be adjusted and optimized. At this time, the genetic algorithm is triggered to optimize the output power control model of the photovoltaic synchronous machine stack device.

[0081] Genetic algorithm optimization,Genetic algorithm is an optimization algorithm based on natural selection and genetics principles, which searches for the optimal solution by simulating the natural evolution process. In this step, the genetic algorithm is used to optimize the output power control model of the photovoltaic synchronous machine pile device to improve its adaptability and stability to the grid operation state.

[0082] The specific optimization process includes determining the optimization goal (such as minimizing the deviation between the power grid operation data and the preset standard value), preparing the parameters required for the genetic algorithm (such as population size, genetic generation, mutation rate, crossover rate, etc.), initializing the population, evaluating fitness, selecting the parent generation, performing crossover and mutation operations, iterative optimization, and determining the optimal solution.

[0083] The optimized output power is outputted, and after being optimized by genetic algorithm, the optimized output power control model parameters of the photovoltaic synchronous machine stack device are obtained. These parameters are applied to the output power control of the photovoltaic synchronous machine stack device, the optimized output power is calculated, and it is transmitted to the power grid.

[0084] Through step S105, the present invention realizes real-time monitoring and evaluation of the operation status of the power grid, and dynamic optimization of the output power control model of the photovoltaic synchronous machine stack device. This step helps to improve the stability and safety of the power grid, enables the photovoltaic synchronous machine stack device to operate efficiently and stably in a complex and changeable power grid environment, and provides strong support for the large-scale application of renewable energy.

[0085] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S101 is also used for:

[0086] The operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are preprocessed to obtain the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data, the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are integrity checked, the data that pass the integrity check are stored in a database, the data that fail the integrity check are matched in a preset data simulation model knowledge base to obtain a data simulation model corresponding to the data that fail the integrity check, the data simulation model is used to simulate the data that fail the integrity check to obtain simulation data corresponding to the data that fail the integrity check, the simulation data corresponding to the data that fail the integrity check are stored in the database, and the data that fail the integrity check are replaced with the simulation data corresponding to the data that fail the integrity check in the database.

[0087] The operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are obtained from various data sources. The operation data of the photovoltaic synchronous machine stack device includes basic operation parameters and status information; the operation data of the power grid covers voltage and current data, and the meteorological environment data includes light intensity, temperature and weather data.

[0088] Preprocess all types of data obtained. Preprocessing steps include data cleaning (such as removing outliers and filling missing values), data conversion (such as converting data in different units into unified units), data reduction (such as reducing the complexity of the data set through dimensionality reduction technology), etc., to ensure the accuracy and consistency of the data.

[0089] Perform integrity check on the preprocessed data. Integrity check aims to verify whether the data is complete, that is, to check whether the data is missing or inconsistent. By setting certain integrity check standards, it is possible to identify which data records are complete and which are incomplete. The data that pass the integrity check is stored in the database for subsequent analysis and model building.

[0090] For data that fails the integrity test, the present invention proposes an innovative solution. First, a match is performed in a preset data simulation model knowledge base to find a data simulation model corresponding to the incomplete data. Then, the incomplete data is simulated using these data simulation models to generate simulated data. Finally, the simulated data is stored in a database, and the simulated data is used to replace the incomplete data in the database.

[0091] Through continuous data acquisition and preprocessing, the data in the database is continuously updated. At the same time, the data in the database is regularly tested for integrity to ensure the accuracy and completeness of the data. For newly discovered incomplete data, the above data simulation and replacement process is repeated.

[0092] Through this series of operations in step S101, the present invention effectively solves the problem of incomplete data and provides a reliable data basis for subsequent photovoltaic output prediction, grid load prediction and photovoltaic synchronous machine stack device output power control. At the same time, this data preprocessing and integrity detection method also improves the robustness and accuracy of the entire control method.

[0093] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S102 is also used for:

[0094] The operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are retrieved from the database, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to extract data features to obtain the features of the data to be processed, and the features of the data to be processed are matched in a preset algorithm knowledge base to obtain an algorithm model corresponding to the features of the data to be processed, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to train the algorithm model corresponding to the features of the data to be processed to obtain a photovoltaic output prediction model.

[0095] In step S102, the present invention describes in detail the process of constructing a photovoltaic output prediction model, which makes full use of the operation history data of the photovoltaic synchronous machine stack device and the meteorological environment history data. The following is a specific interpretation of this step:

[0096] The operation history data of the photovoltaic synchronous machine pile device and the historical data of the meteorological environment are retrieved from the database. These data are the basis for building the photovoltaic output prediction model because they contain the historical relationship between photovoltaic output and various factors (such as light intensity, temperature, etc.).

[0097] Data feature extraction is performed on the retrieved historical data. This step aims to extract feature information useful for photovoltaic output prediction from the original data. For example, features such as light intensity, temperature, and humidity can be extracted from historical meteorological environment data; features such as output power, voltage, and current can be extracted from historical operation data of photovoltaic synchronous machine stack devices.

[0098] The extracted data features to be processed are matched in the preset algorithm knowledge base. The algorithm knowledge base is a database containing various algorithm models, which are designed to process different types of data features and output prediction results. Through matching, the algorithm model that best suits the current data features can be found.

[0099] The algorithm model corresponding to the characteristics of the data to be processed is trained using the operation history data of the photovoltaic synchronous machine pile device and the historical data of the meteorological environment. The training process is to continuously adjust the parameters of the algorithm model so that it can better fit the rules in the historical data, thereby improving the prediction accuracy of future photovoltaic output.

[0100] After training, a photovoltaic output prediction model is obtained. The photovoltaic output prediction model can receive real-time photovoltaic synchronous machine stack device operation data and meteorological environment data as input, and output predicted photovoltaic output results.

[0101] The constructed photovoltaic output prediction model is stored in the database for subsequent use and call. At the same time, as new data continues to accumulate, the model can be regularly updated and retrained to improve its prediction accuracy and adaptability.

[0102] The photovoltaic output prediction model of the present invention is based on historical data. The photovoltaic output prediction model can predict the output power of the photovoltaic synchronous machine stack device in real time, providing an important basis for subsequent grid load prediction and control of the output power of the photovoltaic synchronous machine stack device, which not only helps to improve the stability of the grid, but also promotes the efficient use of renewable energy.

[0103] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S103 is also used for:

[0104] The real-time grid operation data and the data in the photovoltaic output prediction result data set are aligned based on time to obtain aligned data, and the aligned data are tested to obtain aligned data test results, the aligned data test results include aligned successful data and unsuccessful alignment data, and the time information of the unsuccessful alignment data is collected;

[0105] Receiving time information of retrieved real-time power grid operation data, matching the time information of retrieved real-time power grid operation data with the time information of unsuccessful alignment data, and obtaining a time information matching result, wherein the time information matching result includes time matching successful data and time matching unsuccessful data;

[0106] Retrieve the time information corresponding to the data of unsuccessful time matching, and re-perform photovoltaic output prediction through step S102 for the time information corresponding to the data of unsuccessful time matching to obtain the photovoltaic output prediction result of the time information corresponding to the data of unsuccessful time matching.

[0107] In step S103, the present invention describes in detail how to handle the time alignment problem between the real-time grid operation data and the photovoltaic output prediction result data set, and provides a solution for unsuccessful alignment of data. The following is a specific interpretation of this step:

[0108] The real-time grid operation data and the data in the photovoltaic output forecast result data set are aligned based on time. This step is to ensure that the grid operation data and the corresponding photovoltaic output forecast results are matched in time, so that the grid load forecast can be accurately performed later.

[0109] The aligned data is tested to obtain the aligned data test results. The test results include successfully aligned data and unsuccessfully aligned data. Successfully aligned data refers to those grid operation data that are successfully matched to the corresponding photovoltaic output forecast results, while unsuccessfully aligned data refers to those data that cannot be matched to the corresponding forecast results.

[0110] For data that failed to align, its time information is collected, the time information of the real-time grid operation data is received and retrieved, and this time information is matched with the time information of the data that failed to align. The matching results include time-matched data and time-matched data. Time-matched data refers to data that failed to match successfully in the alignment phase, but successfully found the corresponding photovoltaic output forecast results in the subsequent time information matching phase; while time-matched data refers to data that could not find the corresponding forecast results in either the alignment phase or the time information matching phase.

[0111] For data that fail to time match, the corresponding time information is retrieved and the photovoltaic output forecast is performed again through step S102. This step is to fill the data gaps and ensure that the grid operation data at each time point has corresponding photovoltaic output forecast results, thereby improving the accuracy and reliability of grid load forecasting.

[0112] Through this series of operations in step S103, the present invention effectively solves the time alignment problem between the real-time grid operation data and the photovoltaic output prediction result data set, and provides a solution for unsuccessful alignment data, which not only improves the accuracy of grid load prediction, but also provides more accurate data support for subsequent photovoltaic synchronous machine stack device output power control.

[0113] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S104 is also used for:

[0114] Acquiring the operation data of the photovoltaic synchronous machine stack device in real time through a monitoring system or a sensor, wherein the operation data of the photovoltaic synchronous machine stack device includes the output power, voltage, current, temperature and fault status of the synchronous machine stack device;

[0115] Obtain the real-time grid load forecast result from step S103, and obtain the real-time grid demand data from the grid dispatching system, the real-time grid demand data including the total load demand of the grid and the load distribution of each area;

[0116] The real-time grid load forecast result is compared with the real-time grid demand data, and the deviation degree between the real-time grid load forecast result and the real-time grid demand data is obtained by calculating the error value;

[0117] The calculated real-time grid load forecast error value is stored, and a monitoring mechanism for the error value is established. When the error value exceeds a preset threshold, an alarm is triggered.

[0118] The operating data of the photovoltaic synchronous machine stack device is obtained in real time through a monitoring system or sensor. The operating data of the photovoltaic synchronous machine stack device includes parameters such as the output power, voltage, current, temperature and fault status of the synchronous machine stack device. The operating data of the photovoltaic synchronous machine stack device reflects the current operating status of the photovoltaic synchronous machine stack device.

[0119] The real-time grid load forecast result is obtained from step S103. The real-time grid load forecast result is calculated based on multiple factors such as photovoltaic output forecast and grid historical data. The real-time grid load forecast result represents the load demand forecast of the grid in the future.

[0120] At the same time, real-time grid demand data is obtained from the grid dispatching system. The real-time grid demand data includes information such as the total load demand of the grid and the load distribution in each area. The real-time grid demand data reflects the actual current load demand of the grid.

[0121] The real-time grid load forecast results are compared with the real-time grid demand data, and the accuracy of the forecast results is evaluated by calculating the error value. The error value reflects the degree of deviation between the forecast results and the actual demand. The error value is an important indicator for evaluating the performance of the forecast model.

[0122] The calculated real-time grid load forecast error value is stored, and an error value monitoring mechanism is established. The error value monitoring mechanism can track the change of the error value in real time. When the error value exceeds the preset threshold, an alarm mechanism will be triggered to remind the operator to pay attention to the accuracy of the forecast result.

[0123] When the error value exceeds the preset threshold, the alarm mechanism will send a warning signal to remind the operator to take appropriate countermeasures, including re-forecasting photovoltaic output, adjusting the grid load distribution plan, strengthening the operation and maintenance management of photovoltaic synchronous machine stack devices, etc., to ensure the stable operation of the power grid and the balance of supply and demand.

[0124] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S105 is also used for:

[0125] Obtain the grid operation data from the grid monitoring system in real time, pre-process the real-time grid operation data, and compare the pre-processed real-time grid operation data with the preset standard values ​​one by one. The compared data include the voltage fluctuation range, current over-limit situation, frequency stability and power factor pass rate;

[0126] According to the comparison results, the error value is calculated to obtain the difference between the real-time power grid operation data and the preset standard value, and the result of the error analysis is obtained;

[0127] Based on the results of the error analysis, it is determined whether the operating status of the power grid is within the normal range. If the real-time data exceeds the preset standard value, the alarm mechanism is triggered.

[0128] The real-time acquisition of grid operation data from the grid monitoring system is a direct basis for reflecting the current operation status of the grid, including but not limited to parameters such as voltage, current, frequency and power factor.

[0129] The acquired real-time power grid operation data is preprocessed. The preprocessing steps include data cleaning, denoising, formatting, etc., to improve the accuracy of the data and provide a reliable basis for subsequent analysis.

[0130] The preprocessed real-time grid operation data is compared one by one with the preset standard values. The preset standard values ​​are determined based on factors such as safety specifications, technical requirements and historical experience of grid operation. The preset standard values ​​represent the threshold range for normal operation of the grid.

[0131] The compared data include indicators such as voltage fluctuation range, current over-limit situation, frequency stability and power factor qualification rate.

[0132] According to the comparison results, the error value is calculated to obtain the difference between the real-time grid operation data and the preset standard value. The size of the error value reflects the degree of deviation between the current operating state of the grid and the normal state.

[0133] Based on the results of the error analysis, it is determined whether the operating status of the power grid is within the normal range. If the real-time data fluctuates within the allowable range of the preset standard value, the power grid is considered to be operating normally. If the real-time data exceeds the preset standard value, it is determined that the power grid operation is abnormal.

[0134] If the real-time data exceeds the preset standard value, the alarm mechanism will be triggered. The alarm mechanism includes sound alarm, light signal alarm, sending alarm information to the mobile devices of relevant personnel, etc., so that operators can obtain information on abnormal power grid operation in a timely manner.

[0135] After the alarm is triggered, the operator needs to take corresponding response measures based on the alarm information, including adjusting the output power of the photovoltaic synchronous machine stack device, switching the backup power supply, and performing emergency maintenance on the power grid, so as to ensure the safe and stable operation of the power grid.

[0136] At the same time, it is also necessary to conduct in-depth analysis of abnormal data to find out the root causes of abnormal power grid operation and take targeted improvement measures to prevent similar problems from happening again.

[0137] Specifically, the control method for improving grid stability by using a photovoltaic synchronous machine stack device according to the present invention, the step S105 is also used for:

[0138] receiving an optimization target and a population size, wherein the optimization target is to minimize the deviation between the power grid operation data and a preset standard value;

[0139] According to the population size, a set of control model parameter combinations is randomly generated as the initial population. Each control parameter combination of the photovoltaic synchronous machine stack device represents a solution.

[0140] Calculate the deviation between the grid operation data and the preset standard value under each parameter combination, and evaluate the advantages and disadvantages of each parameter combination in the initial population;

[0141] According to the evaluation results, a part of the control parameter combinations of the photovoltaic synchronous machine stack devices are selected as the parent generation for crossover and mutation operations;

[0142] Through the crossover operation, the control parameters of the photovoltaic synchronous machine stack device in the parent generation parameter combination are exchanged to generate a new control parameter combination of the child photovoltaic synchronous machine stack device. Through the mutation operation, the parameters in the control parameter combination of the child photovoltaic synchronous machine stack device are randomly adjusted to increase the diversity of the control parameters of the photovoltaic synchronous machine stack device.

[0143] The deviation between the grid operation data and the preset standard value under each parameter combination is calculated, the advantages and disadvantages of the control combination of the newly generated offspring photovoltaic synchronous machine stack device are evaluated, and sufficient crossover and mutation operations are performed until the set genetic generation is reached or the deviation between the grid operation data and the preset standard value under each parameter combination reaches a threshold. The parameter combination with the smallest deviation between the grid operation data and the preset standard value under each parameter combination is selected from the final population as the optimal solution, that is, the optimized photovoltaic synchronous machine stack device output power control model parameters.

[0144] Receive the optimization target and population size. First, clarify the optimization target, which is to minimize the deviation between the power grid operation data and the preset standard value. This is the core pursuit of the genetic algorithm optimization process, which aims to make the power grid operation data closer to the preset ideal state by adjusting the control model parameters.

[0145] According to the set population size, a set of control model parameter combinations is randomly generated as the initial population. Each control parameter combination of the photovoltaic synchronous machine pile device represents a solution and constitutes the initial set of the genetic algorithm search space.

[0146] Initial population evaluation calculates the deviation between the grid operation data and the preset standard value under each parameter combination, which is used as the basis for evaluating the quality of each parameter combination in the initial population. The smaller the deviation, the closer the grid operation data under this parameter combination is to the preset standard value, so the performance of this parameter combination is better.

[0147] Select the parent generation. According to the evaluation results, select a portion of the photovoltaic synchronous machine device control parameter combinations with excellent performance as the parent generation. These parent generation parameter combinations will play an important role in the subsequent crossover and mutation operations, providing a basis for generating new and better child generation parameter combinations.

[0148] Crossover and mutation operations, through the crossover operation, the control parameters in the parent parameter combination are exchanged to generate a new offspring parameter combination. This process simulates the phenomenon of gene recombination in nature and helps to explore new parameter combination space. At the same time, some parameters in the offspring parameter combination are randomly adjusted through the mutation operation to increase the diversity of the population. The mutation operation helps to break away from the limitations of the local optimal solution and explore a wider solution space.

[0149] Iterative optimization evaluates the newly generated offspring parameter combinations and calculates the deviation between the corresponding power grid operation data and the preset standard value. According to the evaluation results, the offspring parameter combinations with excellent performance are selected as the new population, and the crossover, mutation and evaluation operations are repeated until the set genetic generation is reached or the deviation value under each parameter combination reaches the preset threshold.

[0150] Determine the optimal solution, and after the iterative optimization process is completed, select the best performing parameter combination from the final population as the optimal solution. This optimal solution represents the optimized output power control model parameters of the photovoltaic synchronous machine stack device, which can minimize the deviation between the grid operation data and the preset standard value.

[0151] Through the optimization process of genetic algorithm, the present invention can automatically adjust the output power control model parameters of the photovoltaic synchronous machine stack device to adapt to the real-time changes in the operation status of the power grid, thereby improving the stability and operation efficiency of the power grid. This optimization method has the advantages of strong adaptability and strong global search ability, and provides strong technical support for the application of photovoltaic synchronous machine stack devices in the power grid.

[0152] The technical solution of the present invention effectively solves the problems of insufficient voltage stability, inaccurate reactive power compensation, and slow control speed of the photovoltaic power grid through a control method for improving the stability of the power grid by using a photovoltaic synchronous machine pile device. The specific solutions are as follows:

[0153] The operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are obtained, and the data are stored in the database. The obtained data is preprocessed, including integrity detection. For data that fails the integrity test, simulation is performed through the preset data simulation model knowledge base to obtain simulated data and replace the original unqualified data to improve the integrity of the data. A photovoltaic output prediction model is constructed using the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device. The model can predict photovoltaic output based on real-time operation data and meteorological environment data, substitute the real-time operation data of the photovoltaic synchronous machine stack device and the real-time meteorological environment data into the prediction model, output the photovoltaic output prediction result, and store it to form a data set.

[0154] Obtain the real-time grid operation data and real-time grid demand data, combine them with the photovoltaic output forecast results, substitute them into the preset grid load forecast model, and output the real-time grid load forecast results. Perform time alignment processing on the real-time grid operation data and the photovoltaic output forecast results. For data that fail to align, re-predict the photovoltaic output to ensure the synchronization and integrity of the data. Compare the real-time grid load forecast results with the real-time grid demand data, and calculate the real-time grid load estimated error value. Substitute the error value, real-time grid operation data, and real-time operation data of the photovoltaic synchronous machine stack device into the output power control model, and output the output power of the photovoltaic synchronous machine stack device.

[0155] After the output power of the photovoltaic synchronous machine stack device is transmitted to the power grid, the operation data of the power grid is monitored in real time. The real-time power grid operation data is compared with the preset standard value. If the data is higher than the standard value, the output power control model is optimized using a genetic algorithm. The optimization process includes setting the optimization goal, initializing the population, evaluating the fitness, selecting the parent generation, performing crossover and mutation operations, iterative optimization, and finally determining the optimal solution, that is, the optimized output power control model parameters. An error value monitoring mechanism is established, and an alarm is triggered when the error value exceeds the preset threshold, so as to detect problems in time and take corresponding measures.

[0156] Through the above technical scheme, the present invention can achieve precise control of the output power of the photovoltaic synchronous machine stack device, improve the voltage stability of the photovoltaic power grid, enhance the accuracy of reactive power compensation, and accelerate the control response speed, thereby effectively solving key problems in the operation of the photovoltaic power grid.

Claims

1. A control method for improving power grid stability using a photovoltaic synchronous machine stack device, characterized in that: include: Step S101, obtaining operation data of the photovoltaic synchronous machine stack device, operation data of the power grid, meteorological environment data and fault data, wherein the operation data of the photovoltaic synchronous machine stack device includes basic operation parameters of the synchronous machine stack device and status information of the synchronous machine stack device, the operation data of the power grid includes power grid voltage data and power grid current data, and the meteorological environment data includes light intensity, temperature data and weather data, and the operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, meteorological environment data and fault data are obtained and stored in a database; Step S102, retrieve the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device from the database, build a photovoltaic output prediction model, obtain the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device, substitute the real-time operation data and real-time meteorological environment data of the photovoltaic synchronous machine stack device into the photovoltaic output prediction model, output the photovoltaic output prediction result, store the photovoltaic output prediction result, and obtain a photovoltaic output prediction result data set; Step S103, obtaining real-time grid operation data and real-time grid demand data, retrieving time information of real-time grid operation data, obtaining time information of data to be retrieved, retrieving time information of data to be retrieved in the photovoltaic output prediction result data set, obtaining a prediction result corresponding to the time information of data to be retrieved, substituting the prediction result corresponding to the time information of data to be retrieved, real-time grid demand data and real-time grid operation data into a preset grid load prediction model, and outputting a real-time grid load prediction result; Step S104, obtaining the real-time operation data of the photovoltaic synchronous machine stack device, comparing the real-time grid load prediction result with the real-time grid demand data, obtaining the real-time grid load prediction error value, substituting the real-time grid load prediction error value, the real-time grid operation data and the real-time operation data of the photovoltaic synchronous machine stack device into the output power control model of the photovoltaic synchronous machine stack device, and outputting the output power of the photovoltaic synchronous machine stack device; Step S105, after the photovoltaic synchronous machine stack device outputs power to the power grid, the operation data of the power grid is monitored in real time to obtain the real-time operation data of the power grid, and the real-time operation data of the power grid is compared with the preset standard value. If the real-time operation data of the power grid is higher than the preset standard value, the output power control model of the photovoltaic synchronous machine stack device is optimized using a preset genetic algorithm to obtain the optimized output power of the photovoltaic synchronous machine stack device, and the optimized output power of the photovoltaic synchronous machine stack device is transmitted to the power grid.

2. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 1, characterized in that: The step S101 is also used to: The operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are preprocessed to obtain the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data, the preprocessed operation data of the photovoltaic synchronous machine stack device, the operation data of the power grid, the meteorological environment data and the fault data are integrity checked, the data that pass the integrity check are stored in a database, the data that fail the integrity check are matched in a preset data simulation model knowledge base to obtain a data simulation model corresponding to the data that fail the integrity check, the data simulation model is used to simulate the data that fail the integrity check to obtain simulation data corresponding to the data that fail the integrity check, the simulation data corresponding to the data that fail the integrity check are stored in the database, and the data that fail the integrity check are replaced with the simulation data corresponding to the data that fail the integrity check in the database.

3. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 1, characterized in that: The step S102 is also used to: The operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are retrieved from the database, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to extract data features to obtain the features of the data to be processed, and the features of the data to be processed are matched in a preset algorithm knowledge base to obtain an algorithm model corresponding to the features of the data to be processed, and the operation history data and meteorological environment history data of the photovoltaic synchronous machine stack device are used to train the algorithm model corresponding to the features of the data to be processed to obtain a photovoltaic output prediction model.

4. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 1, characterized in that: The step S103 is also used to: The real-time grid operation data and the data in the photovoltaic output prediction result data set are aligned based on time to obtain aligned data, and the aligned data are tested to obtain aligned data test results, the aligned data test results include aligned successful data and unsuccessful alignment data, and the time information of the unsuccessful alignment data is collected; Receiving time information of retrieved real-time power grid operation data, matching the time information of retrieved real-time power grid operation data with the time information of unsuccessful alignment data, and obtaining a time information matching result, wherein the time information matching result includes time matching successful data and time matching unsuccessful data; Retrieve the time information corresponding to the data of unsuccessful time matching, and re-perform photovoltaic output prediction through step S102 for the time information corresponding to the data of unsuccessful time matching to obtain the photovoltaic output prediction result of the time information corresponding to the data of unsuccessful time matching.

5. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 1, characterized in that: The step S104 is also used to: Acquiring the operation data of the photovoltaic synchronous machine stack device in real time through a monitoring system or a sensor, wherein the operation data of the photovoltaic synchronous machine stack device includes the output power, voltage, current, temperature and fault status of the synchronous machine stack device; Obtain the real-time grid load forecast result from step S103, and obtain the real-time grid demand data from the grid dispatching system, the real-time grid demand data including the total load demand of the grid and the load distribution of each area; The real-time grid load forecast result is compared with the real-time grid demand data, and the deviation degree between the real-time grid load forecast result and the real-time grid demand data is obtained by calculating the error value; The calculated real-time grid load forecast error value is stored, and an error value monitoring mechanism is established. When the error value exceeds a preset threshold, an alarm is triggered.

6. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 1, characterized in that: The step S105 is also used to: Obtain the grid operation data from the grid monitoring system in real time, pre-process the real-time grid operation data, and compare the pre-processed real-time grid operation data with the preset standard values ​​one by one. The compared data include the voltage fluctuation range, current over-limit situation, frequency stability and power factor pass rate; According to the comparison results, the error value is calculated to obtain the difference between the real-time power grid operation data and the preset standard value, and the result of the error analysis is obtained; Based on the results of the error analysis, it is determined whether the operating status of the power grid is within the normal range. If the real-time data exceeds the preset standard value, the alarm mechanism is triggered.

7. The control method for improving the stability of a power grid by using a photovoltaic synchronous machine stack device as claimed in claim 6, characterized in that: The step S105 is also used to: receiving an optimization target and a population size, wherein the optimization target is to minimize the deviation between the power grid operation data and a preset standard value; According to the population size, a set of control model parameter combinations is randomly generated as the initial population. Each control parameter combination of the photovoltaic synchronous machine stack device represents a solution. Calculate the deviation between the grid operation data and the preset standard value under each parameter combination, and evaluate the advantages and disadvantages of each parameter combination in the initial population; According to the evaluation results, a part of the control parameter combinations of the photovoltaic synchronous machine stack devices are selected as the parent generation for crossover and mutation operations; Through the crossover operation, the control parameters of the photovoltaic synchronous machine stack device in the parent generation parameter combination are exchanged to generate a new control parameter combination of the child photovoltaic synchronous machine stack device. Through the mutation operation, the parameters in the control parameter combination of the child photovoltaic synchronous machine stack device are randomly adjusted to increase the diversity of the control parameters of the photovoltaic synchronous machine stack device. The deviation between the grid operation data and the preset standard value under each parameter combination is calculated, the advantages and disadvantages of the control combination of the newly generated offspring photovoltaic synchronous machine stack device are evaluated, and sufficient crossover and mutation operations are performed until the set genetic generation is reached or the deviation between the grid operation data and the preset standard value under each parameter combination reaches a threshold. The parameter combination with the smallest deviation between the grid operation data and the preset standard value under each parameter combination is selected from the final population as the optimal solution, that is, the optimized photovoltaic synchronous machine stack device output power control model parameters.

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