A DC output access variable-speed hydropower control method and system for a photovoltaic module

Abstract

The present invention discloses a method and system for connecting the DC output of a photovoltaic module to variable-speed hydropower control, including: collecting the light intensity information, stored energy, energy consumption of the controller, water volume and water flow information of the photovoltaic module; obtaining historical control data and analyzing the correlation relationship between the historical data; evaluating the water flow, light intensity, and energy storage according to the collected information and historical data, and predicting the water flow state; and performing control on the controller according to the operation requirements for the controller. By combining the data fluctuation analysis of multiple time periods, a flexible and efficient control strategy is provided. The coordinated control of photovoltaic and hydropower is realized, giving full play to the advantages of both, improving the resource utilization rate, and achieving energy conservation and environmental protection. Generally, the present invention improves the control efficiency and provides a solid foundation for a green and sustainable energy supply.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower control, and specifically provides a method and system for connecting the DC output of a photovoltaic module to variable-speed hydropower control. Background Art

[0002] With the development of renewable energy technologies, photovoltaic power generation has become an increasingly popular green energy solution. However, due to the influence of various factors such as light intensity and weather conditions on the production capacity of photovoltaic power generation, the output power has certain instability. To ensure the stable operation of the power grid, it is necessary to effectively control and regulate the output of photovoltaic power generation.

[0003] In addition, as another important renewable energy, hydropower generation is affected by factors such as water flow and reservoir water storage. Therefore, how to effectively control hydropower according to real-time water flow information and water storage to ensure stable power supply to the power grid is also a current technical challenge.

[0004] To solve the above problems, the present method proposes to collect the light information, water volume and water flow information of the photovoltaic module, combine historical control data, comprehensively evaluate the water flow, light and energy storage, and predict the water flow state based on this. Based on this information, the controller can be accurately controlled in real time, so as to realize the coordinated power generation of photovoltaic and hydropower and ensure stable power supply to the power grid.

[0005] To further optimize this control method, researchers have also defined the light information and water flow information in detail, and established various linear relationships based on historical data to accurately predict parameters such as light intensity, water storage volume, and energy storage capacity in different situations. In addition, a data fluctuation analysis method based on quarterly, monthly and 7-day cycles is proposed to realize the long-term prediction of photovoltaic and hydropower generation.

[0006] Generally speaking, the present invention provides a new and efficient control strategy for the coordinated power generation of photovoltaic and hydropower, and is expected to make an important contribution to realizing green and stable power supply to the power grid. Summary of the Invention

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the existing variable-speed hydropower control methods have problems such as slow response speed, insufficient data utilization, lack of flexibility, strong dependence on operators, and single control strategy.

[0009] To solve the above technical problems, the present invention provides the following technical solution: A method for connecting the DC output of a photovoltaic module to variable-speed hydropower control, comprising:

[0010] Collect the light information, energy storage capacity, energy consumption of the controller, and water volume and water flow information of the photovoltaic module;

[0011] Obtain historical control data and analyze the correlation relationships between the historical data;

[0012] Evaluate the water flow, light, and energy storage based on the collected information and historical data, and predict the water flow state;

[0013] Execute the control of the controller according to the operation requirements for the controller.

[0014] As a preferred solution of the variable-speed hydroelectric control method for the DC output access of the photovoltaic module described in the present invention, wherein: the light information includes the curve of light obtained by accessing the variable-speed hydroelectric device through the DC output of the photovoltaic module, which is used to record the light intensity and light duration information;

[0015] The water flow information includes the flow velocity information and flow rate information before and after the water flow passes through the controller.

[0016] As a preferred solution of the variable-speed hydroelectric control method for the DC output access of the photovoltaic module described in the present invention, wherein: the evaluation includes evaluating the relationship according to the light intensity, water storage volume, and evaporation rate in the historical record; evaluating the relationship according to the energy storage capacity, water flow information, light intensity, light energy acquisition amount, and water energy acquisition amount in the historical record; evaluating the relationship between the energy storage capacity, energy consumption of the controller, and natural loss in the historical record.

[0017] As a preferred solution of the variable-speed hydroelectric control method for the DC output access of the photovoltaic module described in the present invention, wherein: the evaluation further includes, through the variable analysis of the light intensity and water storage volume in multiple groups of historical records, and through the fitting of the historical data, obtaining the relationship between the water storage volume and evaporation rate at each light intensity and establishing a linear relationship:

[0018] C i →Z i

[0019] And the relationship between the light intensity and evaporation rate at each water storage volume and establishing a linear relationship:

[0020] I c →Z c

[0021] Through the variable analysis of the energy storage capacity, water flow information, and light intensity in multiple groups of historical records, obtaining the relationship between the light intensity and light energy acquisition amount under each energy storage capacity and water flow information and establishing a linear relationship:

[0022] I qv →E qv

[0023] Under each energy storage capacity and light intensity, establish the relationship between water flow information and water energy acquisition amount and establish a linear relationship:

[0024] V qi →E qi

[0025] Through the variable analysis of the energy storage capacity and the energy consumption of the controller in multiple groups of historical records, obtain the relationship between the energy consumption of the controller and the natural loss under each energy storage capacity and establish a linear relationship:

[0026] H q →σ q

[0027] Under each energy consumption of the controller, establish the relationship between the energy storage capacity and the natural loss and establish a linear relationship:

[0028] Q h →σ h

[0029] Among them, C i represents the data set of water storage when the light intensity is i; Z i represents the fitting result of the evaporation rate when the light intensity is i; I c represents the data set of light intensity when the water storage is c; Z c represents the fitting result of the evaporation rate when the water storage is c; I qv represents the data set of light intensity when the energy storage capacity is q and the water flow information is v; E qv represents the fitting result of the light energy acquisition amount when the energy storage capacity is q and the water flow information is v; V qi represents the data set of water flow information when the energy storage capacity is q and the light intensity is i; E qi The fitting result of the water energy acquisition amount when the energy storage capacity is q and the light intensity is i; H q represents the data set of the energy consumption of the controller when the energy storage capacity is q; σ q represents the fitting result of the natural loss when the energy storage capacity is q; Q h represents the data set of the energy storage capacity when the energy consumption of the controller is h; σ h represents the fitting result of the natural loss when the energy consumption of the controller is h;

[0030] According to each linear relationship, the light intensity, water storage, energy storage capacity, water flow information, and energy consumption of the controller can be randomly combined to obtain the results of the evaporation rate, light energy acquisition amount, water energy acquisition amount, and natural loss under different combination conditions.

[0031] As a preferred solution of the method for connecting the DC output of the photovoltaic module to the variable-speed hydropower control according to the present invention, wherein: the prediction includes obtaining the current information, obtaining the combination identical to the current information in the random combination, and predicting the data change at the next moment according to the change rates of the current water volume and the energy storage volume;

[0032] After the prediction is completed, the prediction results are obtained and calculated to obtain the prediction results of the energy storage volume and the water volume;

[0033] The predicted energy storage volume = the original energy storage volume + the light energy acquisition amount + the water energy acquisition amount - the natural loss - the energy consumption of the controller;

[0034] The predicted water volume = the stored water volume - the evaporation rate × time - the water flow rate;

[0035] Set the step size t, and represent the actual data in each time step with the average value of the data information in each time step; and re-predict the prediction results after the next step with each time step t as the node; thus, the prediction results from t to nt are obtained, where n is the number of prediction step sizes;

[0036] Set the initial value of n to 100. If the difference between the prediction result at 100t and the actual data after 100t is less than 10%, then increase the initial value n of one step size and perform the next prediction until the difference between the prediction result and the actual data exceeds 10% and stop increasing the number of prediction step sizes.

[0037] As a preferred solution of the method for connecting the DC output of the photovoltaic module to the variable-speed hydropower control according to the present invention, wherein: according to the data fluctuations in the seasonal cycle, monthly cycle, and 7-day cycle in the historical records, obtain the maximum and minimum values of the energy storage volume and the maximum and minimum values of the water volume in the seasonal cycle and the monthly cycle;

[0038] If the prediction results exceed the maximum and minimum values of the energy storage volume and the maximum and minimum values of the water volume, an abnormal warning is issued and different warning signals are set for the warning information in the seasonal cycle and the monthly cycle for distinction;

[0039] When the operator's demand is mainly for energy storage, the maximum fluctuation value of the monthly cycle in the historical record of the energy storage change rate is used as the target limit for adjustment, and the operator can select the required energy storage change rate; when the operator's demand is mainly for water storage or drainage, the maximum fluctuation value of the monthly cycle in the historical record of the water volume change rate is used as the target limit for adjustment, and the operator can select the required water volume change rate; the control module simulates the variable speed control of the controller according to the demand selected by the operator, and predicts the data after the variable speed control based on the simulation of the controller. If the demand is met, the control data of the simulated transmission is used as an instruction to perform variable speed control on the controller; if the demand cannot be met, the control data with the change rate closest to the demand is used as an instruction to perform variable speed control on the controller;

[0040] If the operator does not select a demand, the operation behavior in the historical record that is closest to the current data information condition is retrieved as a control instruction to perform variable speed control on the controller;

[0041] If the operator adjusts the variable speed control, the adjustment result is recorded, and when the control instruction before adjustment is output for the same data information within the same quarterly cycle, monthly cycle, and 7-day cycle, the variable speed control is directly adjusted;

[0042] The data information includes the light intensity, the water storage volume, the energy storage amount, the water flow information, and the energy consumption of the controller.

[0043] As a preferred scheme of the photovoltaic module DC output access variable speed hydropower control method of the present invention, wherein: the quarterly cycle includes taking every three adjacent months as a cycle, a total of 12 cycles, and the quarterly cycle with the middle month of the quarterly cycle as the current month as the current quarterly cycle;

[0044] The 7-day cycle includes selecting the date in the current quarterly cycle with the highest matching degree of the data information of 4 consecutive days and the information of the previous 3 days plus the data information of the current day in the historical record as the middle date of the current 7-day cycle, and the time period 3 days before and 3 days after the middle date as the current 7-day cycle;

[0045] The monthly cycle includes taking every 30 adjacent dates as a monthly cycle, and taking the 12th - 18th dates of the monthly cycle as the current 7-day cycle as the current monthly cycle.

[0046] A photovoltaic module DC output access variable speed hydropower control system adopting the method according to any one of the present invention, characterized in that:

[0047] An acquisition unit, which acquires the light information, water volume and water flow information of the photovoltaic module; obtains historical control data and analyzes the correlation relationship between historical data;

[0048] An analysis unit that evaluates water flow, light, and energy storage based on the collected information and historical data, and predicts the water flow state.

[0049] A control unit that executes the control of the controller according to the operation requirements of the controller.

[0050] A computer device, comprising: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method described in any one of the present inventions are implemented.

[0051] A computer-readable storage medium, on which a computer program is stored, characterized in that: when the computer program is executed by a processor, the steps of the method described in any one of the present inventions are implemented.

[0052] Advantages of the present invention: The photovoltaic module DC output access variable-speed hydropower control method provided by the present invention realizes fast and accurate response through real-time collection and historical data analysis, ensuring stable power grid supply. Combining the data fluctuation analysis of multiple time periods, it provides flexible and efficient control strategies. When the prediction result exceeds the expectation, it can issue an early warning in time and automatically adjust, reducing the dependence on operators. In addition, the present invention realizes the coordinated control of photovoltaic and hydropower, gives full play to the advantages of both, improves the resource utilization rate, and realizes energy conservation and environmental protection. Generally, the present invention improves the control efficiency and provides a solid foundation for green and sustainable energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0054] Figure 1 It is the overall flowchart of a photovoltaic module DC output access variable-speed hydropower control method provided by the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Referring to Figure 1 , an embodiment of the present invention provides a method for connecting a DC output of a photovoltaic module to a variable-speed hydropower control, including:

[0058] S1: Collect the light information, stored energy, energy consumption of the controller, water volume and water flow information of the photovoltaic module; obtain historical control data and analyze the correlation between historical data.

[0059] Further, the light information includes a curve of light obtained by connecting the DC output of the photovoltaic module to a variable-speed hydropower device, which is used to record the light intensity and light duration information; the water flow information includes the flow velocity information and flow rate information before and after the water flow passes through the controller.

[0060] Evaluate the relationship according to the light intensity, water storage volume and evaporation rate in the historical record; evaluate the relationship according to the stored energy, water flow information, light intensity, light energy acquisition amount and water energy acquisition amount in the historical record; evaluate the relationship according to the stored energy, energy consumption of the controller and natural loss in the historical record.

[0061] Through the variable analysis of the light intensity and water storage volume in multiple groups of historical records, and through the fitting of historical data, obtain the relationship between the water storage volume and evaporation rate at each light intensity and establish a linear relationship:

[0062] C i →Z i

[0063] And the relationship between the light intensity and evaporation rate at each water storage volume and establish a linear relationship:

[0064] I c →Z c

[0065] Through the variable analysis of the stored energy, water flow information and light intensity in multiple groups of historical records, obtain the relationship between the light intensity and light energy acquisition amount at each stored energy and water flow information and establish a linear relationship:

[0066] I qv →E qv

[0067] And the relationship between the water flow information and water energy acquisition amount at each stored energy and light intensity and establish a linear relationship:

[0068] V qi →E qi

[0069] By analyzing the variables of the energy storage capacity and the energy consumption of the controller in multiple groups of historical records, the relationship between the energy consumption of the controller and the natural loss under each energy storage capacity is obtained and a linear relationship is established:

[0070] H q →σ q

[0071] And the relationship between the energy storage capacity and the natural loss under the energy consumption of each controller is obtained and a linear relationship is established:

[0072] Q h →σ h

[0073] Among them, C i represents the data set of the water storage volume when the light intensity is i; Z i represents the fitting result of the evaporation rate when the light intensity is i; I c represents the data set of the light intensity when the water storage volume is c; Z c represents the fitting result of the evaporation rate when the water storage volume is c; I qv represents the data set of the light intensity when the energy storage capacity is q and the water flow information is v; E qv represents the fitting result of the light energy acquisition amount when the energy storage capacity is q and the water flow information is v; V qi represents the data set of the water flow information when the energy storage capacity is q and the light intensity is i; E qi The fitting result of the water energy acquisition amount when the energy storage capacity is q and the light intensity is i; H q represents the data set of the energy consumption of the controller when the energy storage capacity is q; σ q represents the fitting result of the natural loss when the energy storage capacity is q; Q h represents the data set of the energy storage capacity when the energy consumption of the controller is h; σ h represents the fitting result of the natural loss when the energy consumption of the controller is h; According to each linear relationship, the light intensity, water storage volume, energy storage capacity, water flow information, and energy consumption of the controller can be randomly combined, so as to obtain the results of the evaporation rate, light energy acquisition amount, water energy acquisition amount, and natural loss under different combination conditions.

[0074] It should be noted that based on these data, the present invention has conducted various evaluations. First, it evaluates the relationship between light intensity, water storage volume, and evaporation rate according to historical records. Then, it also evaluates the relationship between energy storage capacity, water flow information, light intensity, light energy acquisition amount, and water energy acquisition amount. In addition, the relationship between energy storage capacity, energy consumption of the controller, and natural loss is also considered. To understand these relationships more deeply, the present invention conducts variable analysis on multiple groups of historical records, thereby establishing a series of linear relationships. These linear relationships cover multiple variables such as light intensity, water storage volume, energy storage capacity, water flow information, and controller energy consumption. Finally, according to these linear relationships, the present invention randomly combines various data to predict the evaporation rate, light energy acquisition amount, water energy acquisition amount, and natural loss under different combination conditions. Generally speaking, the present invention provides a more accurate and efficient method for variable-speed hydropower control by comprehensively analyzing and utilizing various data of photovoltaic modules, which helps to improve the working efficiency and stability of photovoltaic modules. At the same time, directly using the linear relationship eliminates the delay and calculation errors that occur in the complex calculation process.

[0075] S2: According to the collected information and historical data, evaluate the water flow, light, and energy storage, and predict the water flow state.

[0076] Obtain the current information, obtain the combination identical to the current information in the random combination, and predict the data change at the next moment according to the change rates of the current water volume and energy storage capacity; after completing the prediction, obtain and calculate the prediction result to obtain the prediction results of the energy storage capacity and water volume; the predicted energy storage capacity = original energy storage capacity + light energy acquisition amount + water energy acquisition amount - natural loss - energy consumption of the controller; the predicted water volume = water storage volume - evaporation rate × time - water flow rate; set the step size t, and represent the actual data in each time step with the average value of the data information in each time step; and re-predict the prediction result after the next step with each time step t as a node; thus, obtain the prediction results from t to nt, where n is the number of prediction step sizes; set the initial value of n to 100, if the difference between the prediction result at 100t and the actual data after 100t is less than 10%, then increase the initial value n of one step size and conduct the next prediction until the difference between the prediction result and the actual data exceeds 10% and stop increasing the number of prediction step sizes.

[0077] Furthermore, data such as 10% and 100 can be adjusted according to requirements, and only the initial values are given in the present invention. If the difference between the prediction result at 100t and the actual data after 100t is less than 10%, then increase one step size, which can ensure that the predicted result is accurate. However, if it is greater than 10%, it means that the prediction result is not very accurate, and continuously predicting inaccurate data will cause subsequent data to deviate more, so the number of n is not increased.

[0078] S3: Execute the control of the controller according to the operation requirements for the controller.

[0079] Obtain the maximum and minimum values of the energy storage capacity and the maximum and minimum values of the water volume in the quarterly cycle and the monthly cycle according to the data fluctuations in the quarterly cycle, monthly cycle, and 7-day cycle in the historical record; if the prediction result exceeds the maximum and minimum values of the energy storage capacity and the maximum and minimum values of the water volume, an abnormal warning is issued and different warning signals are set for the warning information in the quarterly cycle and the monthly cycle for differentiation.

[0080] It should be noted that according to the data fluctuations in the quarterly cycle, monthly cycle, and 7-day cycle in the historical record, the maximum and minimum values of the energy storage capacity and the maximum and minimum values of the water volume in the quarterly cycle and the monthly cycle are obtained; if the prediction result exceeds the maximum and minimum values of the energy storage capacity and the maximum and minimum values of the water volume, an abnormal warning is issued and different warning signals are set for the warning information in the quarterly cycle and the monthly cycle for differentiation. Based on these historical data, the system will determine the maximum and minimum values of the energy storage capacity and the water volume in the quarterly and monthly cycles. These extreme values provide a reference range for the system to judge whether the current energy storage and water volume are normal. When the real-time monitored data exceeds these predetermined maximum and minimum values, the system will immediately issue an abnormal warning. This is a key function because data outside the normal range may indicate some problems or abnormal situations in the system. To enable operators or managers to quickly identify and respond to different types of abnormalities, the system sets different warning signals for the warnings in the quarterly cycle and the monthly cycle. In this way, when a warning is received, it can be quickly determined which cycle's data has an abnormality, and corresponding measures can be taken.

[0081] When the operator's demand is mainly for energy storage, the maximum fluctuation value in the monthly cycle of the historical record of the energy storage capacity change rate is used as the target limit for adjustment, and the operator can select the required energy storage capacity change rate; when the operator's demand is mainly for water storage or drainage, the maximum fluctuation value in the monthly cycle of the historical record of the water volume change rate is used as the target limit for adjustment, and the operator can select the required water volume change rate; the control module simulates the variable speed control of the controller according to the demand selected by the operator, and predicts the data after the variable speed control based on the simulation of the controller. If the demand is met, the control data of the simulated transmission is used as an instruction to perform variable speed control on the controller; if the demand cannot be met, the control data with the change rate closest to the demand is used as an instruction to perform variable speed control on the controller.

[0082] If the operator does not select a demand, the operation behavior in the historical record that is closest to the current data information conditions is retrieved as a control instruction to perform variable speed control on the controller.

[0083] If the operator adjusts the variable speed control, the adjustment result is recorded. When the control command before adjustment is output for the same data information within the same quarterly cycle, monthly cycle, and 7-day cycle, the variable speed control is directly adjusted.

[0084] It should be noted that when the main demand of the operator is energy storage, the system will refer to the maximum fluctuation value in the monthly cycle of the historical record of the energy storage change rate as the target limit for adjustment. This means that the system will try to ensure that the change in energy storage does not exceed this limit. The operator can choose the energy storage change rate they desire. When the operator's demand is water storage or drainage, the system will refer to the maximum fluctuation value in the monthly cycle of the historical record of the water volume change rate as the target limit for adjustment. The operator can choose the water volume change rate they desire. The control module will simulate the variable speed control of the controller and predict the simulated data. If the simulated data meets the operator's demand, the system will use the simulated control data as an instruction to perform actual variable speed control on the controller. If the simulated data fails to meet the demand, the system will select a control data closest to the demand as an instruction. If the operator does not clearly select the demand, the system will automatically select the operation behavior in the historical record closest to the current data conditions as a control instruction to perform variable speed control on the controller. This makes the operation behavior closer to human judgment. Additionally, the monthly cycle is selected because, first, the data change within a month is not too large. Therefore, it is more practical to select the monthly cycle in the historical data that is most similar to the current scenario as a reference, and the adjustment behavior is closer to the demand.

[0085] The data information includes the light intensity, the water storage volume, the energy storage, the water flow information, and the energy consumption of the controller.

[0086] The quarterly cycle includes taking every three adjacent months as a cycle, a total of 12 cycles, and using the quarterly cycle with the middle month of the quarterly cycle as the current month as the current quarterly cycle; the 7-day cycle includes selecting the date in the current quarterly cycle where the data information of 4 consecutive days has the highest matching degree with the information of the previous 3 days plus the data information of the current day in the historical record as the middle date of the current 7-day cycle, and the time period 3 days before and 3 days after the middle date as the current 7-day cycle; the monthly cycle includes taking every 30 adjacent dates as a monthly cycle, and using the 12th - 18th dates of the monthly cycle as the current 7-day cycle as the current monthly cycle.

[0087] It should be noted that the quarterly cycle is defined based on three consecutive months. In a year, there will be 12 such quarterly cycles. To determine the current quarterly cycle, the system considers the current month and regards it as the middle month of the quarterly cycle. For example, if it is February now, then January, February, and March will form the current quarterly cycle. The determination of the 7-day cycle and the monthly cycle is to make the prediction behavior and control behavior closer to the control purpose. Because in the same physical environment, the month and week may not correspond to the previous year, and can only be said to have a small difference. Therefore, it is intended to lock the range with the quarterly cycle and then use the 7-day cycle and the monthly cycle for locking to effectively locate the most similar time period in the historical data. And for similar physical environments, the control results we need are roughly the same. Therefore, the design of the present invention can effectively avoid the influence of seasonal fluctuations and annual fluctuations on the prediction.

[0088] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium provided in the various embodiments of the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memories, magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random-access memories, magnetic random-access memories, ferroelectric memories, phase-change memories, graphene memories, etc. Volatile memories can include random-access memories or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random-access memory or dynamic random-access memory, etc. The databases involved in the various embodiments of the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation.

[0089] The processors involved in the various embodiments of the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0090] Embodiment 2

[0091] Hereinafter, an embodiment of the present invention provides a method for connecting the DC output of a photovoltaic module to variable-speed hydropower control. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0092] Experimental conditions:

[0093] Use the same photovoltaic modules and controllers.

[0094] Conduct tests under the same weather conditions and water flow conditions.

[0095] Conduct three repeated experiments for each method to ensure the accuracy of the data.

[0096] Experimental steps:

[0097] Use the traditional method to connect the DC output of the photovoltaic module to the variable-speed hydropower control and record the relevant data.

[0098] Use the method of the present invention to connect the DC output of the photovoltaic module to the variable-speed hydropower control and record the relevant data.

[0099] Compare the data of the two methods and analyze their efficiency and accuracy as shown in Table 1.

[0100] Table 1 Efficiency and Accuracy Table

[0101]

[0102] By comparing the total efficiency of the two methods, we can intuitively see which method is more efficient. In the above example table, the total efficiency of the method of the present invention is 90%, while that of the traditional method is 85%. This means that the method of the present invention can more efficiently connect the DC output of the photovoltaic module to the variable-speed hydropower control under the same conditions. By comparing the light energy acquisition amount and water energy acquisition amount of the two methods, we can determine which method is more superior in terms of energy acquisition. In the example data, both the light energy acquisition amount and water energy acquisition amount of the method of the present invention are higher than those of the traditional method, indicating that the method of the present invention is more efficient in energy acquisition. The energy consumption of the controller is a key factor affecting the system efficiency. Lower controller energy consumption means higher energy utilization rate. In the example data, the controller energy consumption of the method of the present invention is lower than that of the traditional method, which further proves its high efficiency.

[0103] Table 2 shows the performance evaluation results of the present invention and the traditional method

[0104]

[0105]

[0106] First, in terms of response time, the method of the present invention only takes 2 seconds, while the traditional method takes 5 seconds. This means that the response speed of the method of the present invention is 2.5 times that of the traditional method, providing users with a faster feedback and operation experience. Secondly, considering the number of system adjustments, the method of the present invention only needs to be adjusted 3 times per month, while the traditional method needs to be adjusted 10 times. This shows the advantage of the method of the present invention in adaptive and intelligent adjustment, reducing frequent manual intervention, thereby reducing maintenance costs and improving system stability. In terms of prediction accuracy, the method of the present invention reaches 95%, while the traditional method is only 85%. This 10% gap means that the method of the present invention is more accurate in predicting future states and demands, and thus can better meet user needs. System failure rate is also a key indicator. The method of the present invention only has 1 failure per year, while the traditional method has 5 failures. This shows that the stability and reliability of the method of the present invention have been significantly improved. From the perspective of user satisfaction, the method of the present invention has obtained a high score of 9 points, while the traditional method is only 7 points. This means that users prefer to use the method of the present invention because it provides them with a better experience and higher efficiency. Finally, long-term stability is a key indicator for evaluating whether a system will experience performance degradation after long-term operation. The performance degradation rate of the method of the present invention is only 2%, while that of the traditional method is 8%. This further proves the stability and durability of the method of the present invention in long-term use.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for controlling the DC output of a photovoltaic module connected to variable speed hydropower, characterized in that: include: Collect the illumination information, energy storage, energy consumption of the controller, and water volume and flow information of the photovoltaic components; Obtain historical control data and analyze the correlation between historical data; Based on the collected information and historical data, water flow, light, and energy storage are evaluated, and the water flow status is predicted; Execute control of the controller according to the operation requirements of the controller; According to the data fluctuations of seasonal cycle, monthly cycle and 7-day cycle in historical records, the maximum and minimum values ​​of energy storage and water volume of seasonal cycle and monthly cycle are obtained; When the prediction result exceeds the maximum and minimum values ​​of the energy storage capacity and the maximum and minimum values ​​of the water volume, an abnormal warning is issued and different warning signals are set for the warning information of the seasonal cycle and the monthly cycle to distinguish; When the operator's demand is mainly for energy storage, the maximum fluctuation value of the monthly cycle in the historical record of the energy storage change rate is used as the target limit for adjustment, and the operator can select the required energy storage change rate; when the operator's demand is mainly for water storage or drainage, the maximum fluctuation value of the monthly cycle in the historical record of the water volume change rate is used as the target limit for adjustment, and the operator can select the required water volume change rate; the control module simulates the speed control of the controller according to the demand selected by the operator, and predicts the data after the speed control based on the simulation of the controller. If the demand is met, the control data of the simulated transmission is used as an instruction to perform speed control on the controller; if the demand cannot be met, the control data with a change rate closest to the demand is used as an instruction to perform speed control on the controller; If the operator does not select a requirement, the operation behavior in the historical record that is closest to the current data information condition is retrieved as a control instruction to control the speed of the controller; If the operator adjusts the speed control, the adjustment result is recorded, and when the control instruction before the adjustment is output for the same data information in the same seasonal cycle, monthly cycle, and 7-day cycle, the speed control is directly adjusted; The data information includes the light intensity, water storage volume, energy storage capacity, water flow information, and energy consumption of the controller.

2. The photovoltaic module DC output access variable speed hydropower control method according to claim 1, characterized in that: The illumination information includes a curve of illumination obtained by connecting the DC output of the photovoltaic module to the variable speed hydroelectric device, which is used to record the illumination intensity and illumination duration information; The water flow information includes: Flow velocity information and flow rate information before and after the water flows through the controller.

3. The photovoltaic module DC output access variable speed hydropower control method as claimed in claim 2, characterized in that: The analysis of the correlation between historical data includes evaluating the relationship based on the light intensity, water storage and evaporation rate in the historical records; evaluating the relationship based on the energy storage, water flow information, light intensity, light energy acquisition and water energy acquisition in the historical records; and evaluating the relationship between the energy storage, controller energy consumption and natural loss in the historical records.

4. The photovoltaic module DC output access variable speed hydropower control method as claimed in claim 3, characterized in that: The analysis of the correlation between the historical data also includes analyzing the variables of light intensity and water storage in multiple sets of historical records, and fitting the historical data to obtain the relationship between water storage and evaporation rate under each light intensity and establish a linear relationship: C i →Z i The relationship between light intensity and evaporation rate under each amount of water storage and establish a linear relationship: I c →Z c According to each linear relationship, the light intensity, water storage capacity, energy storage, water flow information, and energy consumption of the controller can be randomly combined to obtain the results of evaporation rate, light energy acquisition, water energy acquisition, and natural loss under different combination conditions.

5. The photovoltaic module DC output access variable speed hydropower control method as claimed in claim 4, characterized in that: The prediction includes obtaining current information, obtaining a combination identical to the current information in the random combination, and predicting data changes at the next moment according to the current water volume and storage energy change rate; After the prediction is completed, the prediction results are acquired and calculated to obtain the prediction results of the energy storage and water volume; The predicted energy storage = original energy storage + light energy acquisition + water energy acquisition - natural loss - energy consumption of the controller; The predicted water volume = water storage volume - evaporation rate × time - water flow rate; Set the step length t, and use the average value of the data information of each time step to represent the actual data in each time step; and use each time step t as a node to re-predict the prediction result after the next step; thus, the prediction results from t to nt are obtained, where n is the number of predicted steps; Set the initial value of n to 100. If the difference between the predicted result at 100t and the actual data after 100t is less than 10%, increase the initial value n by 1 step and make the next prediction. Stop increasing the number of prediction steps when the difference between the predicted result and the actual data exceeds 10%.

6. The photovoltaic module DC output access variable speed hydropower control method according to claim 1, characterized in that: The seasonal cycle includes taking every three adjacent months as a cycle, a total of 12 cycles, and the seasonal cycle with the middle month of the seasonal cycle as the current month as the current seasonal cycle; The 7-day cycle includes selecting the date with the highest matching degree between the data information of 4 consecutive days and the information of the previous 3 days in the historical records plus the data information of the current day in the current season cycle as the middle date of the current 7-day cycle, and the time period of 3 days before and 3 days after the middle date as the current 7-day cycle; The monthly cycle includes taking every 30 adjacent dates as a monthly cycle, and taking the 12th to 18th dates of the monthly cycle as the current 7-day cycle as the monthly cycle as the current monthly cycle.

7. A photovoltaic module DC output connected to a variable speed hydropower control system using the method according to any one of claims 1 to 6, characterized in that: The collection unit collects the illumination information, water volume and water flow information of the photovoltaic modules; obtains the historical control data and analyzes the correlation between the historical data; The analysis unit evaluates water flow, light, and energy storage based on the collected information and historical data, and predicts the water flow status; The control unit controls the controller according to the operation requirements of the controller.

8. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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