Photovoltaic power generation control method and system

Abstract

The invention discloses a photovoltaic power generation control method and system, and the method comprises the steps: obtaining and analyzing the historical power output data of a photovoltaic power generation system and local historical meteorological data, and determining meteorological characteristic parameters which affect the power output; constructing a power output prediction model of the photovoltaic power generation system based on the meteorological characteristic parameters and a preset neural network model to predict power output prediction data; obtaining a target power output value, analyzing the power output prediction data based on the target power output value, and determining power output difference data; drawing and analyzing a time sequence curve graph of the power output difference data, and dividing the time sequence curve graph into a plurality of fluctuation stages; analyzing and evaluating the power output difference data of each fluctuation stage to obtain a fluctuation evaluation value; and determining a working correction coefficient of the inverter according to the fluctuation evaluation value, and adjusting working parameters of the inverter according to the working correction coefficient. The method not only helps the system to realize stable power output, but also can improve the response speed and adaptability of the system.

Description

Technical Field

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

[0002] Photovoltaic power generation system, as a clean and renewable energy power generation system, has been widely used around the world. However, the power output of photovoltaic power generation system is affected by various meteorological factors such as sunshine, temperature, and shadow effect, resulting in certain volatility and uncertainty in the power output of the system, which poses a challenge to the stability and operation efficiency of the system. In order to solve the problem of power output fluctuation of photovoltaic power generation system, power output control method has become a key technology.

[0003] However, traditional photovoltaic power generation control methods are usually based on fixed parameters and cannot fully consider the real-time meteorological conditions and operating status changes of the system, resulting in inflexible and inaccurate control effects. Traditional methods mostly rely on manual experience and simple control strategies, lack intelligence and adaptability, and cannot make timely adjustments based on real-time data and system status. In addition, the efficiency and stability of traditional methods in power output control need to be improved, and the potential performance of photovoltaic power generation systems cannot be fully utilized, affecting the economy and reliability of the system. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a photovoltaic power generation control method and system, including: Obtain the historical power output data of the photovoltaic power generation system and the local historical meteorological data, and analyze the historical power output data and historical meteorological data to determine the meteorological characteristic parameters that affect the power output; A power output prediction model of a photovoltaic power generation system is constructed based on meteorological characteristic parameters and a preset neural network model, and power output prediction data of the photovoltaic power generation system is predicted based on the power output prediction model; Obtaining a target power output value of the photovoltaic power generation system, and analyzing the power output prediction data based on the target power output value to determine power output difference data of the photovoltaic power generation system; Draw a time series curve of the power output difference data of the photovoltaic power generation system, analyze the time series curve, and divide the time series curve into several fluctuation stages; Analyze and evaluate the power output difference data at each fluctuation stage to obtain the fluctuation evaluation value at each fluctuation stage; The working correction coefficient of the inverter is determined according to the fluctuation evaluation value, and the working parameters of the inverter are adjusted according to the working correction coefficient to control the power output of the photovoltaic power generation system.

[0005] Furthermore, the acquisition of historical power output data of the photovoltaic power generation system and local historical meteorological data, and analysis of the historical power output data and historical meteorological data to determine meteorological characteristic parameters affecting power output include: Obtain historical power output data of the photovoltaic power generation system and local historical meteorological data, and divide the historical meteorological data into several meteorological parameter data groups according to parameter types; The correlation between each meteorological parameter data group and the historical power output data is calculated, and the meteorological parameter data groups with correlations higher than a preset value are screened out, and the meteorological parameters corresponding to the screened meteorological parameter data groups are determined as meteorological characteristic parameters that affect power output.

[0006] Furthermore, the power output prediction model of the photovoltaic power generation system is constructed based on the meteorological characteristic parameters and the preset neural network model, and the power output prediction data of the photovoltaic power generation system is predicted based on the power output prediction model, including: Construct a data set based on meteorological characteristic parameters and corresponding data, and input the data set into a preset neural network model to construct an initial performance prediction model; Divide the data set into a training set and a test set according to a certain ratio, and input the training set and the test set into the initial performance prediction model; The initial performance prediction model is trained and tested until the initial performance prediction model meets a preset convergence condition, thereby obtaining a power output prediction model of the photovoltaic power generation system; Obtain local weather forecast data, and input real-time operation data into the power output prediction model. The power output prediction model performs prediction to obtain power output prediction data of the photovoltaic power generation system.

[0007] Furthermore, the obtaining of the target power output value of the photovoltaic power generation system, and analyzing the power output prediction data based on the target power output value to determine the power output difference data of the photovoltaic power generation system includes: The target power output value and power output prediction data of the photovoltaic power generation system are obtained, and the difference between each power output prediction data and the target power output value is calculated to obtain the power output difference data of the photovoltaic power generation system.

[0008] Furthermore, the time series curve diagram of the power output difference data of the photovoltaic power generation system is drawn, and the time series curve diagram is analyzed to divide the time series curve diagram into several fluctuation stages, including: Draw a time series curve graph of power output difference data of the photovoltaic power generation system, and determine the point in the time series curve graph where the vertical coordinate is zero; The time series curve graph is divided into several curve segments according to the point with zero vertical coordinate, and each curve segment is regarded as the corresponding fluctuation stage.

[0009] Furthermore, the power output difference data of each fluctuation stage is analyzed and evaluated to obtain the fluctuation evaluation value of each fluctuation stage, including: Obtaining power output difference data at each fluctuation stage, calculating an average value of the power output difference data at each fluctuation stage, and evaluating the average value to obtain an average evaluation value corresponding to each fluctuation stage; Obtain the total time length of the time series curve graph and the time length of each fluctuation stage, and calculate the ratio of the time length of each fluctuation stage to the total time length of the time series curve graph, and use the ratio as the time length coefficient corresponding to each fluctuation stage; The average evaluation value corresponding to each fluctuation stage is multiplied by the duration coefficient corresponding to each fluctuation stage to obtain the fluctuation evaluation value of each fluctuation stage.

[0010] Further, the method of determining the working correction coefficient of the inverter according to the fluctuation evaluation value, and adjusting the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system includes: Presetting a corresponding relationship between a work correction coefficient and a fluctuation assessment value interval, wherein the corresponding relationship between a work correction coefficient and a fluctuation assessment value interval is associated with a corresponding work correction coefficient for each fluctuation assessment value interval; Obtaining the fluctuation assessment value of each fluctuation stage, and based on the mapping relationship between the fluctuation assessment value interval to which the fluctuation assessment value belongs and the corresponding relationship between the difference coefficient and the fluctuation assessment value interval, selecting the working correction coefficient corresponding to the fluctuation assessment value interval as the working correction coefficient of the inverter; The working parameters of the inverter are adjusted according to the working correction coefficients in each fluctuation stage, and the power output of the photovoltaic power generation system is controlled according to the adjusted working parameters.

[0011] The present invention also provides a photovoltaic power generation control system, comprising: An acquisition module is used to acquire historical power output data of the photovoltaic power generation system and local historical meteorological data, and analyze the historical power output data and historical meteorological data to determine meteorological characteristic parameters that affect power output; A prediction module is used to construct a power output prediction model of the photovoltaic power generation system based on meteorological characteristic parameters and a preset neural network model, and predict power output prediction data of the photovoltaic power generation system based on the power output prediction model; A determination module is used to obtain a target power output value of the photovoltaic power generation system, and analyze the power output prediction data based on the target power output value to determine the power output difference data of the photovoltaic power generation system; A division module is used to draw a time series curve diagram of the power output difference data of the photovoltaic power generation system, analyze the time series curve diagram, and divide the time series curve diagram into several fluctuation stages; An evaluation module is used to analyze and evaluate the power output difference data of each fluctuation stage to obtain a fluctuation evaluation value of each fluctuation stage; The control module is used to determine the working correction coefficient of the inverter according to the fluctuation evaluation value, and adjust the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system.

[0012] Compared with the prior art, the photovoltaic power generation control method and system according to the embodiment of the present invention have the following beneficial effects: The present invention can achieve accurate prediction of the power output of the photovoltaic power generation system by establishing a neural network model based on historical data and meteorological characteristic parameters, and help optimize system operation and power dispatch; By analyzing the difference between the power output prediction data and the target value and evaluating the fluctuation stage, the present invention can determine the working correction coefficient of the inverter and adjust the inverter parameters to control the power output of the photovoltaic power generation system and achieve more stable and efficient operation; The present invention draws a time series curve graph of power output difference data and analyzes it, which can intuitively display the fluctuation of power output, help users better understand the system operation status, discover potential problems and formulate corresponding optimization measures; The present invention can realize intelligent operation and maintenance management of photovoltaic power generation system by establishing power output prediction model and control algorithm, improve the automation degree of the system, reduce manual intervention, and improve the operation efficiency and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a schematic diagram of the structure of the process of the photovoltaic power generation control method in an embodiment of the present invention; Figure 2 It is a schematic diagram of the composition of the photovoltaic power generation control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0016] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0017] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0018] like Figure 1 As shown, in an embodiment of the present application, a photovoltaic power generation control method is provided, including: S100: acquiring historical power output data of a photovoltaic power generation system and local historical meteorological data, and analyzing the historical power output data and the historical meteorological data to determine meteorological characteristic parameters affecting power output; S200: constructing a power output prediction model of a photovoltaic power generation system based on meteorological characteristic parameters and a preset neural network model, and predicting power output prediction data of the photovoltaic power generation system based on the power output prediction model; S300: acquiring a target power output value of the photovoltaic power generation system, and analyzing the power output prediction data based on the target power output value to determine power output difference data of the photovoltaic power generation system; S400: drawing a time series curve diagram of the power output difference data of the photovoltaic power generation system, and analyzing the time series curve diagram to divide the time series curve diagram into several fluctuation stages; S500: analyzing and evaluating the power output difference data of each fluctuation stage to obtain a fluctuation evaluation value of each fluctuation stage; S600: determining a working correction coefficient of an inverter according to the fluctuation evaluation value, and adjusting the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system.

[0019] Furthermore, the present invention can achieve accurate prediction of the power output of the photovoltaic power generation system by establishing a neural network model based on historical data and meteorological characteristic parameters, thereby helping to optimize system operation and power dispatching; the present invention can determine the working correction coefficient of the inverter and adjust the inverter parameters by analyzing the difference between the power output prediction data and the target value, as well as the evaluation of the fluctuation stage, so as to control the power output of the photovoltaic power generation system and achieve more stable and efficient operation; the present invention draws a time series curve graph of the power output difference data and analyzes it, which can intuitively display the fluctuation of the power output, help users better understand the system operation status, discover potential problems and formulate corresponding optimization measures; the present invention can achieve intelligent operation and maintenance management of the photovoltaic power generation system by establishing a power output prediction model and a control algorithm, improve the system's degree of automation, reduce manual intervention, and improve the system's operating efficiency and stability.

[0020] In an embodiment of the present application, a photovoltaic power generation control method is provided, wherein historical power output data of the photovoltaic power generation system and local historical meteorological data are obtained, and the historical power output data and historical meteorological data are analyzed to determine meteorological characteristic parameters that affect power output, including: obtaining historical power output data of the photovoltaic power generation system and local historical meteorological data, and dividing the historical meteorological data into a number of meteorological parameter data groups according to parameter type; calculating the correlation between each meteorological parameter data group and the historical power output data, and filtering out meteorological parameter data groups with correlations higher than preset values, and determining the meteorological parameters corresponding to the filtered meteorological parameter data groups as meteorological characteristic parameters that affect power output.

[0021] Specifically, the power output data of the photovoltaic power generation system over the past period of time and the local historical meteorological data, including meteorological parameters such as sunshine intensity, temperature, and wind speed, are obtained; the collected historical meteorological data are divided according to parameter types, for example, sunshine intensity data, temperature data, wind speed data, etc. are respectively formed into different meteorological parameter data groups; for each meteorological parameter data group, the correlation between the group of data and the historical power output data is calculated, and statistical methods such as Pearson correlation coefficient or Spearman are used to measure the correlation between the two; a preset correlation threshold is set, and meteorological parameter data groups with correlations higher than the threshold are screened out, and the meteorological parameters corresponding to these data groups are determined as key meteorological characteristic parameters affecting the power output of the photovoltaic power generation system. This step can determine which meteorological factors have the most significant impact on the power output of the system by analyzing historical data and screening out meteorological parameters that are highly correlated with power output, providing an important basis for subsequent power output prediction and control. In summary, by analyzing historical power output data and meteorological data, determining the key meteorological characteristic parameters that affect power output can help optimize the operation and management of the photovoltaic power generation system and improve the power output efficiency and stability of the system.

[0022] In an embodiment of the present application, a photovoltaic power generation control method is provided, which constructs a power output prediction model of a photovoltaic power generation system based on meteorological characteristic parameters and a preset neural network model, and predicts power output prediction data of the photovoltaic power generation system based on the power output prediction model, including: constructing a data set based on meteorological characteristic parameters and corresponding data, and inputting the data set into a preset neural network model to construct a performance prediction initial model; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the performance prediction initial model; training and testing the performance prediction initial model until the performance prediction initial model meets the preset convergence conditions to obtain a power output prediction model of the photovoltaic power generation system; obtaining local meteorological forecast data, and inputting real-time operation data into the power output prediction model, and predicting by the power output prediction model to obtain power output prediction data of the photovoltaic power generation system.

[0023] Specifically, based on the previously determined meteorological characteristic parameters affecting power output and their corresponding historical data, a data set is constructed, which includes meteorological characteristic parameter data and corresponding power output data; the constructed data set is input into a preset neural network model to construct an initial performance prediction model; the constructed data set is divided into a training set and a test set according to a certain ratio, usually the training set is used for model training, and the test set is used to evaluate the performance of the model; the training set and the test set are respectively input into the initial performance prediction model to train and test the model, and the model parameters are continuously adjusted and the model structure is optimized until the initial performance prediction model meets the preset convergence conditions, that is, the performance of the model reaches a certain standard; after training and testing, a power output prediction model of the photovoltaic power generation system is obtained, and the model can predict the power output of the system according to the input meteorological characteristic parameter data; local meteorological forecast data are obtained as real-time operation data, and these data are input into the trained power output prediction model, and the model will predict the power output of the photovoltaic power generation system according to the current meteorological conditions. This step can more accurately predict the power output of the photovoltaic power generation system by building and training a neural network model, improving the accuracy and reliability of the prediction; inputting real-time meteorological data into the prediction model can achieve real-time prediction of the power output of the photovoltaic power generation system, helping the system to adjust its operation strategy in time to cope with meteorological changes. In summary, using a neural network model to build a power output prediction model can effectively improve the power output prediction accuracy of the photovoltaic power generation system, realize real-time monitoring and regulation of the system operation, and promote the efficient operation and management of the photovoltaic power generation system.

[0024] In an embodiment of the present application, a photovoltaic power generation control method is provided, which obtains a target power output value of a photovoltaic power generation system, and analyzes power output prediction data based on the target power output value to determine power output difference data of the photovoltaic power generation system, including: obtaining the target power output value and power output prediction data of the photovoltaic power generation system, and calculating the difference between each power output prediction data and the target power output value to obtain the power output difference data of the photovoltaic power generation system.

[0025] Specifically, the target power output value of the photovoltaic power generation system is obtained, that is, the power value that the system is expected to output at a specific moment. At the same time, the power output prediction data of the system at the same moment is also obtained through the previously established power output prediction model; for the power output prediction data at each moment and the corresponding target power output value, the difference between them is calculated, that is, the difference between the predicted power output value and the target power output value. These difference data reflect the deviation between the predicted value and the target value. According to the changing trend of the power output difference data, this step can timely adjust the system parameters and optimize the system operation management strategy to improve the overall performance and efficiency of the system.

[0026] In an embodiment of the present application, a photovoltaic power generation control method is provided, which draws a timing curve graph of the power output difference data of the photovoltaic power generation system, analyzes the timing curve graph, and divides the timing curve graph into a number of fluctuation stages, including: drawing a timing curve graph of the power output difference data of the photovoltaic power generation system, and determining the point with a vertical coordinate of zero in the timing curve graph; dividing the timing curve graph into a number of curve segments according to the point with a vertical coordinate of zero, and taking each curve segment as a corresponding fluctuation stage.

[0027] Specifically, according to the calculated power output difference data of the photovoltaic power generation system, a time series curve is drawn, the horizontal axis represents time, and the vertical axis represents the power output difference data, that is, the difference between the predicted value and the target value; in the time series curve, find the point with zero vertical axis, that is, the time point when the power output predicted value is completely consistent with the target value; according to the point with zero vertical axis, the time series curve is divided into several curve segments, each curve segment represents a fluctuation stage, and the fluctuation stage indicates that there is a certain deviation between the system predicted value and the target value, that is, the system output power has a certain volatility. In this step, by drawing the time series curve and dividing the fluctuation stage, the fluctuation characteristics of the power output of the photovoltaic power generation system can be clearly identified, and the prediction accuracy and stability of the system in different time periods can be understood; according to the analysis of the fluctuation stage, targeted operation management strategies can be formulated, and system parameters and control strategies can be adjusted to reduce system volatility and improve the stability and accuracy of the system power output. In summary, by drawing the time series curve and dividing the fluctuation stage, the power output fluctuation of the photovoltaic power generation system can be deeply understood, the problems existing in the system can be identified, and the system operation strategy can be optimized, thereby improving the operation efficiency and performance of the system.

[0028] In an embodiment of the present application, a photovoltaic power generation control method is provided, in which the power output difference data of each fluctuation stage is analyzed and evaluated to obtain a fluctuation evaluation value of each fluctuation stage, including: obtaining the power output difference data of each fluctuation stage, calculating the average value of the power output difference data of each fluctuation stage, and evaluating the average value to obtain an average evaluation value corresponding to each fluctuation stage; obtaining the total time length of a timing curve and the time length of each fluctuation stage, and calculating the ratio of the time length of each fluctuation stage to the total time length of the timing curve, and using the ratio as the time length coefficient corresponding to each fluctuation stage; multiplying the average evaluation value corresponding to each fluctuation stage by the time length coefficient corresponding to each fluctuation stage to obtain a fluctuation evaluation value for each fluctuation stage.

[0029] Specifically, by calculating the average value of the power output difference data of each fluctuation stage, a measure of the overall prediction accuracy of that stage can be obtained. The size of the average value reflects the average deviation between the predicted value and the target value in that stage. The smaller the deviation, the more accurate the prediction. The evaluation value can map the average value to a more understandable range, such as converting it into a score range of 0-10, so as to more intuitively understand the prediction performance of each fluctuation stage. The high or low evaluation value can indicate the prediction accuracy of the system at different stages, which helps to guide subsequent optimization work. The duration coefficient reflects the importance and duration of each fluctuation stage in the overall timing curve. By calculating the duration coefficient, the contribution of each fluctuation stage to the overall fluctuation of the system can be understood, providing a basis for the formulation of subsequent optimization strategies. The fluctuation evaluation value is obtained by multiplying the average evaluation value by the duration coefficient. This value comprehensively considers the factors of prediction accuracy and duration, and can more comprehensively evaluate the performance of the system at different fluctuation stages. The size of the fluctuation evaluation value can indicate the degree and quality of the overall fluctuation of the system. This step can comprehensively evaluate the fluctuation characteristics of the system at different stages, including factors such as prediction accuracy and duration, by calculating the fluctuation evaluation value, helping system managers to fully understand the performance of the system; the fluctuation evaluation value provides guidance for system managers, who can formulate targeted adjustment strategies based on the evaluation results of each fluctuation stage to improve the power output stability and accuracy of the system. In summary, by calculating the fluctuation evaluation value, the fluctuation characteristics of the system can be comprehensively evaluated, the system optimization strategy can be guided, the system operation efficiency can be improved, and a more stable and efficient photovoltaic power generation system can be achieved.

[0030] In an embodiment of the present application, a photovoltaic power generation control method is provided, wherein an operating correction coefficient of an inverter is determined according to a fluctuation evaluation value, and the operating parameters of the inverter are adjusted according to the operating correction coefficient to control the power output of the photovoltaic power generation system, including: presetting a corresponding relationship between an operating correction coefficient and a fluctuation evaluation value interval, wherein the corresponding relationship between an operating correction coefficient and a fluctuation evaluation value interval is associated with a corresponding operating correction coefficient for each fluctuation evaluation value interval; obtaining fluctuation evaluation values ​​of each fluctuation stage, and selecting an operating correction coefficient corresponding to the fluctuation evaluation value interval as the operating correction coefficient of the inverter based on a mapping relationship between the fluctuation evaluation value interval to which the fluctuation evaluation value belongs within the corresponding relationship between a difference coefficient and a fluctuation evaluation value interval; adjusting the operating parameters of the inverter according to the operating correction coefficients of each fluctuation stage, and controlling the power output of the photovoltaic power generation system according to the adjusted operating parameters.

[0031] Specifically, a working correction coefficient-fluctuation assessment value interval correspondence table is pre-set, wherein each fluctuation assessment value interval corresponds to a working correction coefficient, and this relationship table is used to guide the adjustment of the inverter working parameters according to the system performance; according to the fluctuation assessment values ​​of each fluctuation stage calculated previously, the assessment value of each stage is matched with the pre-set fluctuation assessment value interval correspondence; according to the mapping relationship of the fluctuation assessment value interval to which the fluctuation assessment value belongs in the relationship table, the working correction coefficient corresponding to the fluctuation assessment value interval is selected as the working correction coefficient of the inverter, and this working correction coefficient is used to adjust the inverter working parameters to improve the stability and efficiency of the system; according to the selected working correction coefficient, the inverter working parameters are adjusted, and these parameters include output voltage, frequency, power factor, etc. By adjusting these parameters, the performance of the inverter can be optimized and the power output quality of the system can be improved; according to the adjusted working parameters, the power output of the photovoltaic power generation system is controlled, and by adjusting the inverter parameters according to the working correction coefficients of different fluctuation stages, the system can maintain a more stable power output under different fluctuation conditions, thereby improving the overall performance of the photovoltaic power generation system. This step selects the corresponding working correction coefficient according to different intervals of the fluctuation evaluation value to realize the intelligent adjustment of the inverter parameters, so that the system parameters can be adjusted in real time according to the system fluctuation situation, and the adaptability and stability of the system can be improved; by adjusting the inverter parameters according to the fluctuation evaluation value, the power output of the system can be optimized, the power generation efficiency of the system can be improved, energy loss can be reduced, and the overall benefit of the system can be improved; by adjusting the inverter parameters according to the working correction coefficients in different fluctuation stages, the system can have better stability when facing different fluctuation situations, reduce power fluctuations, and improve the reliability and stability of the system. In summary, this step can realize the intelligent adjustment of the inverter parameters according to the system fluctuation situation, thereby optimizing the performance of the photovoltaic power generation system, improving efficiency and stability, and realizing a more intelligent and efficient photovoltaic power generation system operation.

[0032] like Figure 2As shown, in an embodiment of the present application, a photovoltaic power generation control system is provided, including: an acquisition module, which is used to acquire historical power output data of the photovoltaic power generation system and local historical meteorological data, and analyze the historical power output data and historical meteorological data to determine the meteorological characteristic parameters that affect the power output; a prediction module, which is used to construct a power output prediction model of the photovoltaic power generation system based on the meteorological characteristic parameters and a preset neural network model, and predict the power output prediction data of the photovoltaic power generation system based on the power output prediction model; a determination module, which is used to acquire the target power output value of the photovoltaic power generation system, and analyze the power output prediction data based on the target power output value to determine the power output difference data of the photovoltaic power generation system; a division module, which is used to draw a time series curve diagram of the power output difference data of the photovoltaic power generation system, and analyze the time series curve diagram to divide the time series curve diagram into several fluctuation stages; an evaluation module, which is used to analyze and evaluate the power output difference data of each fluctuation stage to obtain the fluctuation evaluation value of each fluctuation stage; a control module, which is used to determine the working correction coefficient of the inverter according to the fluctuation evaluation value, and adjust the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system.

[0033] In summary, the embodiment of the present invention provides a photovoltaic power generation control method and system, which includes: obtaining and analyzing the historical power output data of the photovoltaic power generation system and the local historical meteorological data, determining the meteorological characteristic parameters that affect the power output; constructing a power output prediction model of the photovoltaic power generation system based on the meteorological characteristic parameters and a preset neural network model to predict the power output prediction data; obtaining the target power output value, and analyzing the power output prediction data based on the target power output value to determine the power output difference data; drawing and analyzing the time series curve of the power output difference data, dividing it into several fluctuation stages; analyzing and evaluating the power output difference data of each fluctuation stage to obtain a fluctuation evaluation value; determining the working correction coefficient of the inverter according to the fluctuation evaluation value, and adjusting the working parameters of the inverter according to it. The present invention not only helps the system to achieve stable power output, but also can improve the response speed and adaptability of the system.

[0034] Finally, it should be noted that: Obviously, a person skilled in the art may 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 present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0035] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0036] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0037] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0038] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A photovoltaic power generation control method, characterized in that: include: Obtain the historical power output data of the photovoltaic power generation system and the local historical meteorological data, and analyze the historical power output data and historical meteorological data to determine the meteorological characteristic parameters that affect the power output; A power output prediction model of a photovoltaic power generation system is constructed based on meteorological characteristic parameters and a preset neural network model, and power output prediction data of the photovoltaic power generation system is predicted based on the power output prediction model; Obtaining a target power output value of the photovoltaic power generation system, and analyzing the power output prediction data based on the target power output value to determine power output difference data of the photovoltaic power generation system; Draw a time series curve of the power output difference data of the photovoltaic power generation system, analyze the time series curve, and divide the time series curve into several fluctuation stages; Analyze and evaluate the power output difference data at each fluctuation stage to obtain the fluctuation evaluation value at each fluctuation stage; The working correction coefficient of the inverter is determined according to the fluctuation evaluation value, and the working parameters of the inverter are adjusted according to the working correction coefficient to control the power output of the photovoltaic power generation system.

2. A photovoltaic power generation control method according to claim 1, characterized in that: The obtaining of historical power output data of the photovoltaic power generation system and local historical meteorological data, and analyzing the historical power output data and historical meteorological data to determine meteorological characteristic parameters affecting power output include: Obtain historical power output data of the photovoltaic power generation system and local historical meteorological data, and divide the historical meteorological data into several meteorological parameter data groups according to parameter types; The correlation between each meteorological parameter data group and the historical power output data is calculated, and the meteorological parameter data groups with correlations higher than a preset value are screened out, and the meteorological parameters corresponding to the screened meteorological parameter data groups are determined as meteorological characteristic parameters that affect power output.

3. A photovoltaic power generation control method according to claim 2, characterized in that: The power output prediction model of the photovoltaic power generation system is constructed based on the meteorological characteristic parameters and the preset neural network model, and the power output prediction data of the photovoltaic power generation system is predicted based on the power output prediction model, including: Construct a data set based on meteorological characteristic parameters and corresponding data, and input the data set into a preset neural network model to construct an initial performance prediction model; Divide the data set into a training set and a test set according to a certain ratio, and input the training set and the test set into the initial performance prediction model; The initial performance prediction model is trained and tested until the initial performance prediction model meets a preset convergence condition, thereby obtaining a power output prediction model of the photovoltaic power generation system; Obtain local weather forecast data, and input real-time operation data into the power output prediction model. The power output prediction model performs prediction to obtain power output prediction data of the photovoltaic power generation system.

4. A photovoltaic power generation control method according to claim 3, characterized in that: The step of obtaining a target power output value of the photovoltaic power generation system and analyzing the power output prediction data based on the target power output value to determine the power output difference data of the photovoltaic power generation system includes: The target power output value and power output prediction data of the photovoltaic power generation system are obtained, and the difference between each power output prediction data and the target power output value is calculated to obtain the power output difference data of the photovoltaic power generation system.

5. A photovoltaic power generation control method according to claim 4, characterized in that: The method of drawing a time series curve diagram of the power output difference data of the photovoltaic power generation system and analyzing the time series curve diagram to divide the time series curve diagram into several fluctuation stages includes: Draw a time series curve graph of power output difference data of the photovoltaic power generation system, and determine the point in the time series curve graph where the vertical coordinate is zero; The time series curve graph is divided into several curve segments according to the point with zero vertical coordinate, and each curve segment is regarded as the corresponding fluctuation stage.

6. A photovoltaic power generation control method according to claim 5, characterized in that: The power output difference data of each fluctuation stage is analyzed and evaluated to obtain the fluctuation evaluation value of each fluctuation stage, including: Obtaining power output difference data at each fluctuation stage, calculating an average value of the power output difference data at each fluctuation stage, and evaluating the average value to obtain an average evaluation value corresponding to each fluctuation stage; Obtain the total time length of the time series curve graph and the time length of each fluctuation stage, and calculate the ratio of the time length of each fluctuation stage to the total time length of the time series curve graph, and use the ratio as the time length coefficient corresponding to each fluctuation stage; The average evaluation value corresponding to each fluctuation stage is multiplied by the duration coefficient corresponding to each fluctuation stage to obtain the fluctuation evaluation value of each fluctuation stage.

7. A photovoltaic power generation control method according to claim 6, characterized in that: The method of determining the working correction coefficient of the inverter according to the fluctuation evaluation value and adjusting the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system includes: Presetting a corresponding relationship between a work correction coefficient and a fluctuation assessment value interval, wherein the corresponding relationship between a work correction coefficient and a fluctuation assessment value interval is associated with a corresponding work correction coefficient for each fluctuation assessment value interval; Obtaining the fluctuation assessment value of each fluctuation stage, and based on the mapping relationship between the fluctuation assessment value interval to which the fluctuation assessment value belongs and the corresponding relationship between the difference coefficient and the fluctuation assessment value interval, selecting the working correction coefficient corresponding to the fluctuation assessment value interval as the working correction coefficient of the inverter; The working parameters of the inverter are adjusted according to the working correction coefficients in each fluctuation stage, and the power output of the photovoltaic power generation system is controlled according to the adjusted working parameters.

8. A photovoltaic power generation control system, characterized in that: include: An acquisition module is used to acquire historical power output data of the photovoltaic power generation system and local historical meteorological data, and analyze the historical power output data and historical meteorological data to determine meteorological characteristic parameters that affect power output; A prediction module, used to construct a power output prediction model of the photovoltaic power generation system based on meteorological characteristic parameters and a preset neural network model, and predict power output prediction data of the photovoltaic power generation system based on the power output prediction model; A determination module is used to obtain a target power output value of the photovoltaic power generation system, and analyze the power output prediction data based on the target power output value to determine the power output difference data of the photovoltaic power generation system; A division module is used to draw a time series curve diagram of the power output difference data of the photovoltaic power generation system, analyze the time series curve diagram, and divide the time series curve diagram into several fluctuation stages; An evaluation module is used to analyze and evaluate the power output difference data of each fluctuation stage to obtain a fluctuation evaluation value of each fluctuation stage; The control module is used to determine the working correction coefficient of the inverter according to the fluctuation evaluation value, and adjust the working parameters of the inverter according to the working correction coefficient to control the power output of the photovoltaic power generation system.

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