Output power adjustment method and apparatus, and computer device and storage medium

Abstract

The present application relates to an output power adjustment method and apparatus, and a computer device and a storage medium. The method comprises: acquiring sampling data corresponding to sampling parameters, and determining a fuzzy set to which the sampling data belongs, wherein the sampling parameters are related to the maximum power point of a photovoltaic power generation system; on the basis of a fuzzy rule corresponding to the fuzzy set, performing fuzzy reasoning on the sampling data, and determining fuzzy control information matching the sampling data, wherein the fuzzy control information comprises a parameter value adjustment direction of working parameters and a step-size adjustment direction of parameter value change amounts; performing defuzzification processing on the step-size adjustment direction to obtain a step-size adjustment value; and outputting a control signal matching the parameter value adjustment direction and the step-size adjustment value, wherein the control signal is used for changing an output power of the photovoltaic power generation system by adjusting parameter values of the working parameters. By using the method, an output power adjustment effect can be improved.

Description

Output power regulation method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the field of photovoltaic power generation technology, and in particular to an output power regulation method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] Solar photovoltaic power generation, as a representative of renewable energy generation, has the potential to become a major energy source in the future and has already been officially implemented in production. However, the output power of photovoltaic power sources can fluctuate dramatically with environmental factors such as light intensity and temperature, making it uncontrollable. To improve photovoltaic power generation efficiency, it is often necessary to adjust the output power of photovoltaic power generation systems to achieve maximum power point tracking.

[0003] Traditional output power regulation methods monitor the output voltage of the photovoltaic power supply in real time, determine the difference between the output voltage and the set voltage, and then adjust the output voltage based on this difference to maintain a constant output voltage, thereby achieving constant output power. However, the solar panels of a photovoltaic power generation system are affected by changes in sunlight, which may cause fluctuations in the maximum power point or even multiple power point extremes. Therefore, using traditional output power regulation methods may not track the maximum power point of the photovoltaic power generation system, resulting in poor regulation effectiveness. Technical issues

[0004] Based on this, it is necessary to provide an output power regulation method, device, computer equipment, computer-readable storage medium and computer program product that can improve the output power regulation effect in response to the above technical problems. Technical Solutions

[0005] In a first aspect, the present application provides an output power adjustment method, the method comprising:

[0006] Acquire sampling data corresponding to a sampling parameter, and determine the fuzzy set to which the sampling data belongs; the sampling parameter is related to the maximum power point of the photovoltaic power generation system;

[0007] Based on the fuzzy rules corresponding to the fuzzy set, fuzzy reasoning is performed on the sampled data to determine fuzzy control information matching the sampled data; the fuzzy control information includes a parameter value adjustment direction of the working parameter and a step size adjustment direction of the parameter value change;

[0008] Performing defuzzification processing on the step length adjustment direction to obtain a step length adjustment value;

[0009] A control signal matching the parameter value adjustment direction and the step adjustment value is output; the control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter.

[0010] In one embodiment, the sampling parameter is an operating parameter of a photovoltaic power generation system; and obtaining sampling data corresponding to the sampling parameter and determining the fuzzy set to which the sampling data belongs includes:

[0011] Acquire sampling data of operating parameters of the photovoltaic power generation system, determine a data change rate of the operating parameters based on the sampling data; and determine the fuzzy set to which the data change rate belongs as the fuzzy set to which the sampling data belongs.

[0012] In one embodiment, the method further comprises:

[0013] Determine multiple candidate fuzzy sets corresponding to the working parameters; map the data change rate to the membership function of each candidate fuzzy set respectively to obtain the candidate membership degree of the data change rate belonging to each candidate fuzzy set; determine the candidate fuzzy set corresponding to the maximum membership degree among the candidate membership degrees as the fuzzy set to which the data change rate belongs.

[0014] In one embodiment, performing fuzzy reasoning on the sampled data based on the fuzzy rules corresponding to the fuzzy set to determine fuzzy control information matching the sampled data includes:

[0015] From the multiple fuzzy rules corresponding to the fuzzy set, determine a first fuzzy rule that matches the parameter value adjustment direction of the working parameter, and a second fuzzy rule that matches the step adjustment direction of the parameter value change; based on the first fuzzy rule, perform fuzzy reasoning on the sampling data to obtain the parameter value adjustment direction of the working parameter; based on the second fuzzy rule, perform fuzzy reasoning on the sampling data to obtain the step adjustment direction of the parameter value change; and combine the parameter value adjustment direction and the step adjustment direction to obtain fuzzy control information.

[0016] In one embodiment, the method further comprises:

[0017] Obtaining the original output power and the updated output power of the photovoltaic power generation system before and after the output power changes; calculating the power change of the updated output power relative to the original output power; and performing fuzzy control on the output power based on the power change when the power change does not meet the maximum power condition, and returning to the step of obtaining the original output power and the updated output power until the new power change meets the maximum power condition.

[0018] In one embodiment, the fuzzy controlling the output power based on the power variation includes:

[0019] If the power change is positive, the parameter value adjustment direction is determined as the updated parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired updated sampling data to determine the updated step adjustment direction; if the power change is negative, the negative direction of the parameter adjustment direction is determined as the updated parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired updated sampling data to determine the updated step adjustment direction; and an update control signal that matches the updated parameter value adjustment direction and the updated step adjustment direction is determined.

[0020] In a second aspect, the present application further provides an output power adjustment device. The device comprises:

[0021] An information acquisition module, configured to acquire sampling data corresponding to sampling parameters and determine the fuzzy set to which the sampling data belongs; the sampling parameters are related to the maximum power point of the photovoltaic power generation system;

[0022] a fuzzy reasoning module, configured to perform fuzzy reasoning on the sampled data based on fuzzy rules corresponding to the fuzzy set, and determine fuzzy control information matching the sampled data; the fuzzy control information includes a parameter value adjustment direction of an operating parameter and a step size adjustment direction of a parameter value variation;

[0023] a defuzzification module, configured to perform a defuzzification process on the step adjustment direction to obtain a step adjustment value;

[0024] A power regulation module is used to output a control signal that matches the parameter value adjustment direction and the step adjustment value; the control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter.

[0025] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0026] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-described method when executed by a processor.

[0027] In a fifth aspect, the present application further provides a computer program product, which includes a computer program that implements the steps of the above method when executed by a processor. Beneficial effects

[0028] The above-mentioned output power regulation method, apparatus, computer device, computer-readable storage medium, and computer program product use fuzzy rules to associate sampling parameters related to the maximum power point of a photovoltaic power generation system with operating parameters capable of changing the output power of the photovoltaic power generation system. Fuzzy reasoning is performed based on the acquired sampling data to obtain matching fuzzy control information. The output power of the photovoltaic power generation system is then adjusted based on the control signal corresponding to the fuzzy control information. When the sampling data changes, the operating parameters can be promptly changed accordingly, thereby converging the output power near the maximum power point, which is beneficial for improving the control effect of the output power. Furthermore, the fuzzy control information includes the parameter value adjustment direction of the operating parameter and the step adjustment direction of the parameter value change. By defuzzifying the step adjustment direction to obtain the step adjustment value, the output power can be adjusted with a non-fixed step size, which is equivalent to achieving adaptive adjustment of the adjustment step size during the output power adjustment process, thereby ensuring that the output power does not fluctuate significantly, thereby more flexibly responding to changes in the maximum power point and improving the tracking efficiency of the maximum power point. Therefore, the above-mentioned method can improve the output power adjustment effect while improving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] FIG1 is a diagram illustrating an application environment of an output power regulation method according to an embodiment;

[0030] FIG2 is a schematic flow chart of an output power regulation method according to an embodiment;

[0031] FIG3 is a schematic flow chart of an output power adjustment method according to another embodiment;

[0032] FIG4 is a structural block diagram of an output power regulating device in one embodiment;

[0033] FIG5 is a diagram showing the internal structure of a computer device in one embodiment. Modes for Carrying Out the Invention

[0034] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0035] The output power regulation method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. The control terminal 102 communicates with the sampling terminal 104 through a network to obtain the sampling data collected by the sampling terminal 104. The control terminal 102 can be a control chip or a controller including a control chip. The control terminal 102 can be set separately, integrated into the controller of the photovoltaic power generation system, or integrated into a terminal, which includes but is not limited to various personal computers, laptops, smart phones and tablet computers. The sampling terminal 104 can be implemented as an independent sampling terminal or a sampling terminal cluster composed of multiple sampling terminals. During output power regulation, the control terminal 102 obtains sampled data corresponding to sampled parameters associated with the maximum power point of the photovoltaic power generation system from the sampling terminal 104 and determines the fuzzy set to which the sampled data belongs. The control terminal 102 then performs fuzzy inference on the sampled data based on the fuzzy rules corresponding to the fuzzy set to determine fuzzy control information that matches the sampled data. This fuzzy control information includes the parameter value adjustment direction of the operating parameter and the step adjustment direction of the parameter value change. The control terminal 102 then defuzzifies the step adjustment direction to obtain a step adjustment value and outputs a control signal that matches the parameter adjustment direction and step adjustment value. This control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter. It can be understood that because the parameters used are associated with the maximum power point of the photovoltaic power generation system, repeating the above output power regulation process in actual applications can achieve tracking of the maximum power point of the photovoltaic power generation system.

[0036] In one embodiment, as shown in FIG2 , an output power adjustment method is provided. This embodiment takes the method applied to the control terminal 102 in FIG1 as an example for explanation. The method includes the following steps:

[0037] Step S100: Acquire sampling data corresponding to sampling parameters, and determine the fuzzy set to which the sampling data belongs.

[0038] The sampling parameters refer to various parameters related to the maximum power point of the photovoltaic power generation system. The sampling parameters may include, for example, at least one of the parameters such as the light that can be received by the solar panels in the photovoltaic power generation system, the operating voltage of the solar panels, or the ambient temperature of the environment in which the solar panels are located. The sampling data is data obtained by collecting data on the sampling parameters, and the sampling data may include multiple sampling values ​​at different sampling times. It can be understood that different sampling parameters may correspond to different sampling data. For example, collecting data on light can obtain light values ​​at different sampling times; collecting data on temperature can obtain temperature values ​​at different sampling times. Furthermore, in some embodiments, the sampling data may also include change data of the current sampling value relative to the previous sampling value, such as light change data, voltage change data, and temperature change data.

[0039] A fuzzy set is a set used to express ambiguous concepts. Fuzzy concepts describe object attributes that cannot be answered simply with "yes" or "no," such as "young," "large," "warm," and "evening." A fuzzy set refers to the entire set of objects that possess the attributes described by a fuzzy concept. Specifically, in this application, a fuzzy set can include the entire set of objects described by a fuzzy concept of a sampling parameter. For example, a fuzzy set for voltage can include "increase," "decrease," and "maintain," while a fuzzy set for temperature can include "high," "low," and "moderate."

[0040] In some specific embodiments, the fuzzy set may also include all objects described by a fuzzy concept of the working parameter association information. The working parameter refers to a parameter that can change the output power of the photovoltaic power generation system. The working parameter may be, for example, the output voltage, output current or output power. The working parameter association information may include the parameter value adjustment direction of the working parameter or the step adjustment direction of the parameter value change. The parameter value adjustment direction of the working parameter refers to the adjustment direction of the parameter value of the working parameter. For example, when the working parameter is the output voltage, the corresponding parameter value adjustment direction may be "voltage increase", "voltage decrease" or "voltage unchanged". The step adjustment direction of the parameter value change is the direction of change of the changed value when the parameter value of the working parameter changes, for example, "step increase", "step decrease" or "step unchanged".

[0041] A photovoltaic power generation system uses the photovoltaic effect of photovoltaic cells to directly convert solar radiation into electrical energy. The maximum power point (MPP) is the power point at which a photovoltaic power generation system achieves optimal efficiency. As previously mentioned, the MPP of a photovoltaic power generation system changes in real time. Therefore, to improve photovoltaic power generation efficiency, it is typically necessary to adjust the output power of the photovoltaic power generation system to track the MPP. In other words, the purpose of output power regulation in this application is to track the MPP of the photovoltaic power generation system to improve its power generation efficiency.

[0042] Specifically, the sampling end can collect data on sampling parameters related to the maximum power point of the photovoltaic power generation system to obtain sampled values ​​corresponding to the sampling parameters. The control end can then retrieve each sampled value from the sampling end to obtain sampled data corresponding to the sampling parameters. Alternatively, the sampling end can be equipped with a certain computing function. After collecting data on sampling parameters related to the maximum power point of the photovoltaic power generation system and obtaining sampled values ​​corresponding to the sampling parameters, the sampling end can calculate the data change rate of the sampling parameters based on the change data of the current sampling value relative to the previous sampling value, and then transmit the data change rate of the sampling parameters to the control end. In other words, the sampled data corresponding to the parameters can be the sampling values ​​or the processed results obtained by processing the sampling values, such as the data change rate.

[0043] After acquiring the sampled data, the control end can perform fuzzification on the sampled data to determine the fuzzy set to which the sampled data belongs. For example, if the sampling parameter is temperature, the control end can perform fuzzification on the temperature value to determine a fuzzy expression for the temperature value, and then determine the fuzzy set corresponding to the fuzzy expression. This fuzzy set is the fuzzy set to which the sampled data belongs. For example, this fuzzy set may include "high temperature," "moderate temperature," or "low temperature." The control end can also perform fuzzification on the temperature change rate to determine a fuzzy expression for the temperature change rate, and then determine the fuzzy set corresponding to the fuzzy expression. This fuzzy set is the fuzzy set to which the sampled data belongs. For example, this fuzzy set may include "large temperature change," "moderate temperature change," or "small temperature change."

[0044] Furthermore, the type of sampling parameters may be one or more. In the case of multiple types of sampling parameters, the control end may obtain sampling data corresponding to each sampling parameter and determine the fuzzy set to which each sampling data belongs.

[0045] Step S200: performing fuzzy reasoning on the sampled data based on the fuzzy rules corresponding to the fuzzy set, and determining fuzzy control information matching the sampled data.

[0046] Among them, the fuzzy rules are used to define the reasoning relationship between the sampling parameters and the working parameters, and are specifically used to define the fuzzy relationship between the fuzzy expression of the sampling parameters and the fuzzy control information of the working parameters. The fuzzy control information includes the fuzzy relationship between the parameter value adjustment direction of the working parameters and the step adjustment direction of the parameter value change. For the specific definitions of the working parameters, parameter value adjustment direction, and step value adjustment direction, please refer to the above and will not be repeated here. For example, the initial step value is set to 1, and the step adjustment direction output is increased. Then, when the working parameters are adjusted for the next time, the adjusted step value is greater than 1. In this way, the step adjustment value of the working parameters can change with the change of the sampling data, which is beneficial to improving the output power adjustment efficiency.

[0047] In a specific embodiment, the fuzzy rule may be "IF X is A THEN Y is B," where X represents a sampling parameter, A represents the fuzzy set corresponding to the sampling parameter, Y represents an operating parameter or a change in the operating parameter's value, and B represents fuzzy control information, corresponding to the parameter value adjustment direction of the operating parameter or the step size adjustment direction of the parameter value change. Furthermore, in practical applications, there may be more than one sampling parameter. When there are two sampling parameters, the two sampling parameters can be connected using "AND" to form a parallel condition, for example, "IF X1 is A1 AND X2 is A2 THEN Y is B." For more information on sampling parameters, refer to the above rules and are not further described here.

[0048] Exemplarily, the fuzzy rule may include at least a portion of the following rules: “IF the illumination change rate is 'small' AND the voltage change rate is 'small' THEN the step adjustment direction is 'increase'”, “IF the light change rate is ‘small’ AND the voltage change rate is ‘moderate’ THEN the step size adjustment direction is ‘hold’,” “IF the light change rate is ‘small’ AND the voltage change rate is ‘large’ THEN the step size adjustment direction is ‘decrease’,” “IF the light change rate is ‘moderate’ AND the voltage change rate is ‘small’ THEN the step size adjustment direction is ‘increase’,” “IF the light change rate is ‘moderate’ AND the voltage change rate is ‘moderate’ THEN the step size adjustment direction is ‘hold’,” “IF the light change rate is ‘moderate’ AND the voltage change rate is ‘large’ THEN the step size adjustment direction is ‘decrease’,” “IF the light change rate is ‘large’ AND the voltage change rate is ‘small’ THEN the step size adjustment direction is ‘increase’,” “IF the light change rate is ‘large’ AND the voltage change rate is ‘moderate’ THEN the step size adjustment direction is ‘hold’,” or “IF the light change rate is ‘large’ AND the voltage change rate is ‘large’ THEN the step size adjustment direction is ‘decrease’.”

[0049] Fuzzy reasoning refers to substituting the input into the corresponding fuzzy rule after the fuzzy rule is known to determine the output. The input here refers to the sampling parameters, and the output refers to the parameter value adjustment direction of the operating parameter and the step adjustment direction of the parameter value change. For example, the fuzzy reasoning process can be: if the illumination change rate is "small", then the step adjustment direction of the output voltage is "increasing" and the parameter value adjustment direction of the output voltage is "decreasing". For another example, the fuzzy reasoning process can be: if the illumination change rate is "small" and the voltage change rate is "small", then the step adjustment direction of the output voltage is "increasing" and the parameter value adjustment direction of the output voltage is "decreasing".

[0050] Specifically, the control end can extract fuzzy rules related to the fuzzy set to which the sampled data belongs from a fuzzy rule library, and perform fuzzy reasoning on the sampled data according to the fuzzy rules to obtain corresponding fuzzy control information. This can be performed on a single sampled data to obtain fuzzy control information corresponding to a single operating parameter. Similarly, fuzzy reasoning can be performed on multiple sampled data to obtain fuzzy control information corresponding to a single operating parameter, or on a single sampled data to obtain fuzzy control information corresponding to multiple operating parameters. Fuzzy reasoning can also be performed on multiple sampled data to obtain fuzzy control information corresponding to multiple operating parameters.

[0051] Furthermore, in some embodiments, a single sampled data may correspond to multiple fuzzy rules, and the control end may perform fuzzy reasoning on the sampled data according to the multiple fuzzy rules to obtain corresponding multiple fuzzy control information.

[0052] Step S300: performing defuzzification processing on the step length adjustment direction to obtain a step length adjustment value.

[0053] Defuzzification is the process of converting fuzzy control information into clear control information. Specifically for this application, this control information refers to the step adjustment value. For example, defuzzifying "increase" to "+0.5" can be understood as assuming that the initial step value is "1". Then, the step value for the next change in the operating parameter will be "1.5". The resulting operating parameter value after adjustment is the value obtained by adding "1.5" to the original value.

[0054] Specifically, within a period of time, the control terminal can perform defuzzification processing on the step length adjustment direction to obtain the step length adjustment value. The specific algorithm used in the defuzzification processing can be, for example, the maximum membership method, the center of gravity method, or the average method.

[0055] In a specific embodiment, the control end uses the maximum membership method to defuzzify the step length adjustment direction. The process is as follows: obtaining the step length adjustment directions obtained through fuzzy reasoning within a set time period, substituting these step length adjustment directions into the corresponding membership functions to obtain the corresponding membership degrees, taking the step length adjustment direction corresponding to the maximum membership degree as the final step length adjustment direction, and then obtaining the step length adjustment value based on the membership function mapping of the step length adjustment direction. The set time period can be, for example, 5 minutes, 10 minutes, etc.

[0056] In a specific embodiment, the control end uses the center of gravity method to perform defuzzification processing on the step length adjustment direction. The process is as follows: obtaining the step length adjustment direction obtained by fuzzy reasoning within a set time period, substituting these step length adjustment directions into the corresponding membership function to obtain the corresponding membership, obtaining the membership function center point according to the corresponding membership function image, multiplying the membership corresponding to each step length adjustment direction with the corresponding membership function center point, and summing all the products, dividing the value obtained by the summation by the sum of the membership values ​​to obtain a center of gravity membership value. According to the comparison between the membership value and the collected membership values ​​of the step length adjustment direction, the step length adjustment direction corresponding to the membership value closer to the center of gravity membership value among the collected membership values ​​of the step length adjustment direction is the final step length adjustment direction, and the step length adjustment value is obtained according to the membership function mapping of the step length adjustment direction.

[0057] In a specific embodiment, the control end uses the average value method to defuzzify the step adjustment direction. The process is as follows: obtain the step adjustment direction obtained by fuzzy reasoning for a set time, substitute these step adjustment directions into the corresponding membership function to obtain the corresponding membership, sum the corresponding membership values, and divide them by the number of collected step adjustment directions. The obtained value is the average membership value. The membership value is compared with the membership values ​​of the collected step adjustment directions. Among the membership values ​​of the collected step adjustment directions, the step adjustment direction corresponding to the membership value that is closer to the average membership value is the final step adjustment direction, and the step adjustment value is obtained according to the membership function mapping of the step adjustment direction.

[0058] Step S400: outputting a control signal that matches the parameter value adjustment direction and the step size adjustment value.

[0059] The control signal is used to change the output power of the photovoltaic power generation system by adjusting the operating parameter values. For example, if the initial output voltage is "10V" and the initial step size is "1V", and the control signal increases the voltage with a voltage step size of "+0.5", the output voltage at the next moment after adjustment will be "11.5V".

[0060] Specifically, the control terminal 102 outputs a control signal that matches the parameter value adjustment direction and the step adjustment value, so as to change the output power of the photovoltaic power generation system by adjusting the parameter value of the working parameter, thereby realizing the output power regulation function.

[0061] The above-mentioned output power regulation method uses fuzzy rules to associate sampling parameters related to the maximum power point of the photovoltaic power generation system with operating parameters that can change the output power of the photovoltaic power generation system. Fuzzy reasoning is performed based on the acquired sampling data to obtain matching fuzzy control information. Then, the output power of the photovoltaic power generation system is adjusted according to the control signal corresponding to the fuzzy control information. When the sampling data changes, the operating parameters can be changed in a timely manner, so that the output power converges near the maximum power point, which is conducive to improving the control effect of the output power. Furthermore, the fuzzy control information includes the parameter value adjustment direction of the operating parameter and the step adjustment direction of the parameter value change. By defuzzifying the step adjustment direction to obtain the step adjustment value, the output power can be adjusted with a non-fixed step size. This is equivalent to achieving adaptive adjustment of the adjustment step size during the output power adjustment process, thereby ensuring that the output power does not fluctuate significantly, thereby more flexibly responding to changes in the maximum power point and improving the tracking efficiency of the maximum power point. Therefore, the above-mentioned method can improve the output power adjustment effect while improving efficiency.

[0062] In one embodiment, the sampled parameters are operating parameters of a photovoltaic power generation system. In this embodiment, step S100 includes: obtaining sampled data of the operating parameters of the photovoltaic power generation system, determining a data change rate of the operating parameters based on the sampled data; and determining the fuzzy set to which the data change rate belongs as the fuzzy set to which the sampled data belongs.

[0063] Specifically, the control terminal 102 can obtain sampled data of the operating parameters of the photovoltaic power generation system collected by the sampling terminal 104. This sampled data may include, for example, illumination data, voltage data, and temperature data. The control terminal then calculates the data change rate of the sampled parameter based on the change data of the current sampled value relative to the previous sampled value. The obtained data change rate of the sampled parameter determines which fuzzy set the corresponding sampled parameter belongs to. In other words, the data change rate is fuzzified to determine the fuzzy set to which the data change rate belongs, and this fuzzy set is determined as the fuzzy set to which the sampled data belongs. For example, suppose the current illumination sampling value is "100lux" and the illumination sampling value at the previous moment is "50lux", then the calculation process of the illumination data change rate is "(100-50) / 50*100%=100%". According to the current illumination data change rate of "100%", it corresponds to the candidate fuzzy set of illumination: "large illumination change rate", "small illumination change rate" or "moderate illumination change rate", and the fuzzy set corresponding to the current illumination sampling data is selected as "large illumination change rate".

[0064] In this embodiment, after the control end receives the working parameter sampling data collected by the sampling end, it calculates the data change rate of the working parameter, and determines the fuzzy set of the sampling data based on the data change rate, so that the determined fuzzy set is highly consistent with the fluctuation of the sampling parameters, thereby ensuring that the output power regulation process can follow the fluctuation of the sampling parameters, which is conducive to further improving the power regulation effect.

[0065] In one embodiment, the output power regulation method also includes: determining multiple candidate fuzzy sets corresponding to the working parameters; mapping the data change rate to the membership function of each candidate fuzzy set respectively, and obtaining the candidate membership degree of the data change rate belonging to each candidate fuzzy set respectively; and determining the candidate fuzzy set corresponding to the maximum membership degree among the candidate membership degrees as the fuzzy set to which the data change rate belongs.

[0066] The candidate fuzzy set is used to define the fuzzy set corresponding to each operating parameter in the fuzzy set. For example, if the operating parameter is the illumination change rate, the corresponding candidate fuzzy set may include "large illumination change rate", "small illumination change rate", or "moderate illumination change rate".

[0067] A membership function is used to determine the true extent to which an element belongs to a fuzzy set. The membership degree represents the true extent to which an element belongs to a fuzzy set. For example, if "fat person" is a fuzzy set, the membership function corresponding to this fuzzy set indicates that a person weighing 80 kg belongs to the "fat" category with a membership degree of 0.9, while a person weighing 70 kg belongs to the "fat" category with a membership degree of 0.8. For another example, for the fuzzy set "young," if the age is under 20, the corresponding membership degree could be "1," indicating that anyone under 20 is definitely "young." If a person is 30, it's unclear whether they are "young," and the corresponding membership degree could be "0.5," indicating that a 30-year-old is 50% "young" and 50% "not" young.

[0068] Specifically, in this application, the degree of membership is used to represent the relationship between the data change rate of an operating parameter and a fuzzy set, specifically, to represent the degree to which the data change rate of an operating parameter belongs to the fuzzy set. For example, in a specific application, when the illumination change rate is 10%, the degree of membership for "large illumination change rate" is 0.2, the degree of membership for "small illumination change rate" is 0.5, and the degree of membership for "moderate illumination change rate" is 0.8.

[0069] Furthermore, the specific type of membership function is not unique. For example, it can be a triangular function, a trapezoidal function, a rectangular function, a Gaussian function, and a sawtooth function. By substituting the data change rate into the membership function of each candidate fuzzy set, the membership degree of the corresponding candidate fuzzy set can be obtained.

[0070] In a specific embodiment, a triangular function is used as the membership function of the candidate fuzzy set. Assuming that the data change rate ranges from "0" to "1", the peak position of the triangular function can be selected as "0.5", and the steepness is appropriately adjusted to adapt to the actual situation to obtain a membership function similar to a triangle. The obtained data change rate is substituted into the image of the membership function to obtain the corresponding membership value.

[0071] In a specific embodiment, a trapezoidal function is used as the membership function of the candidate fuzzy set. Assuming that the data change rate ranges from "0" to "1", the rising and falling slope points of the trapezoidal function can be selected to adapt to the actual situation, and a membership function similar to a trapezoid can be obtained. The obtained data change rate is substituted into the image of the membership function to obtain the corresponding membership value.

[0072] In a specific embodiment, a Gaussian function is used as the membership function of the candidate fuzzy set. The Gaussian function is a membership function in the shape of a bell curve, which is often used to describe continuously changing input variables. It has a center position and a standard deviation, which are used to control the width and steepness of the curve. The membership of the Gaussian function is higher near the center position and gradually decreases with increasing distance from the center position. Assuming that the data change rate ranges from "-1" to "1", the center position of the Gaussian function can be selected as "0", and the standard deviation can be appropriately adjusted to adapt to the actual situation. In this way, a bell-shaped membership function can be obtained. Substituting the obtained data change rate into the image of the membership function, the corresponding membership value can be obtained.

[0073] In a specific embodiment, a sawtooth function is used as the membership function of the candidate fuzzy set. A sawtooth function is a membership function with a sawtooth shape, which increases or decreases linearly within a certain range. A sawtooth function is usually used to describe discrete input variables, where the membership changes gradually along a straight line segment. Assuming that the data change rate ranges from "0" to "1", the starting point and end point of the rising straight line segment of the sawtooth function, as well as the starting point and end point of the descending straight line segment, can be selected to adapt to the actual situation. In this way, a sawtooth membership function can be obtained, and the corresponding membership value can be obtained by substituting the obtained data change rate into the image of the membership function.

[0074] Furthermore, the process of determining the specific function expression of the sawtooth function may include: setting the starting point of the rising straight line segment to 0, the ending point to 1, and the step size (membership step size) to 0.2; setting the starting point of the descending straight line segment to 1, the ending point to 0, and the step size (membership step size) to 0.2; on the rising straight line segment, the membership starts from 0 and increases every 0.2 until it reaches 1 (i.e.: 0, 0.2, 0.4, 0.6, 0.8, 1); on the descending straight line segment, the membership starts from 1 and decreases every 0.2 until it reaches 0 (i.e.: 1, 0.8, 0.6, 0.4, 0.2, 0); and determining the function expression of the above image as the expression of the sawtooth function.

[0075] Specifically, the control end can determine multiple candidate fuzzy sets corresponding to the operating parameter. The operating parameter can be one or multiple. When there is one operating parameter, it can correspond to multiple candidate fuzzy sets. When there are multiple operating parameters, each operating parameter can correspond to multiple candidate fuzzy sets. The control end then substitutes the data change rate into the membership function corresponding to each candidate fuzzy set to obtain the candidate membership degree of each candidate fuzzy set. Finally, the control end determines the candidate fuzzy set corresponding to the maximum membership degree among the candidate membership degrees as the fuzzy set to which the data change rate belongs. For example, if the illumination change rate of "5%" is substituted into the membership function of the fuzzy sets "large illumination change rate," "small illumination change rate," or "moderate illumination change rate," the membership degrees can be obtained as 0.2, 0.5, and 0.8, respectively. The fuzzy set "maintaining illumination change rate" has the largest membership degree. Therefore, when the illumination change rate is "5%," the corresponding fuzzy set is "moderate illumination change rate."

[0076] In this embodiment, the data change rate of the working parameter is substituted into the membership function corresponding to the candidate fuzzy set of the working parameter to obtain the corresponding membership degree, and by comparing the numerical values ​​of the corresponding membership degrees, the candidate fuzzy set with the largest membership degree is selected as the fuzzy set corresponding to the working parameter, which can ensure the accuracy of the determined fuzzy set and thereby improve the accuracy of the entire output power regulation process.

[0077] In one embodiment, step S200 includes: determining a first fuzzy rule that matches the parameter value adjustment direction of the working parameter and a second fuzzy rule that matches the step adjustment direction of the parameter value change from multiple fuzzy rules corresponding to the fuzzy set; performing fuzzy reasoning on the sampling data based on the first fuzzy rule to obtain the parameter value adjustment direction of the working parameter; performing fuzzy reasoning on the sampling data based on the second fuzzy rule to obtain the step adjustment direction of the parameter value change; and obtaining fuzzy control information by combining the parameter value adjustment direction and the step adjustment direction.

[0078] The first fuzzy rule represents a fuzzy rule consisting of the input condition, output condition, and corresponding fuzzy relationship when the output condition is the parameter value adjustment direction of the operating parameter. For example, if the output condition is the parameter value adjustment direction of the operating parameter, and the input condition is the rate of change of illumination, the fuzzy rule is "IF the rate of change of illumination increases, THEN the parameter value adjustment direction of the operating parameter is increasing."

[0079] Similarly, the second fuzzy rule is a fuzzy rule consisting of the input, output, and fuzzy relationship corresponding to the output condition, when the output condition is the step adjustment direction of the parameter value change. For example, if the output condition is the step adjustment direction of the parameter value change, and the input condition is the illumination change rate, the fuzzy rule is "IF the illumination change rate increases, THEN the step adjustment direction of the value change is increasing."

[0080] Specifically, the control end 102 determines the fuzzy rules for the parameter value adjustment direction of the working parameter and the step adjustment direction of the parameter value change from multiple fuzzy rules corresponding to the fuzzy set according to different output conditions. That is to say, after the fuzzy set of the sampling parameter is determined, there are multiple corresponding fuzzy rules. According to the output condition, which is the parameter value adjustment direction of the working parameter or the step adjustment direction of the parameter value change, a certain fuzzy rule is determined as the first fuzzy rule or the second fuzzy rule. By performing fuzzy reasoning based on the two fuzzy rules, fuzzy control information including the parameter value adjustment direction and the step adjustment direction can be obtained.

[0081] This embodiment separates the two parameter value adjustment directions and the step size adjustment direction in the fuzzy control information for fuzzy reasoning, and matches corresponding fuzzy rules for them respectively, making the fuzzy reasoning process clearer and helping to further improve the accuracy of the fuzzy control information determined by the fuzzy reasoning.

[0082] In one embodiment, the output power regulation method further includes: obtaining the original output power and the updated output power of the photovoltaic power generation system before and after the output power changes; calculating the power change of the updated output power relative to the original output power; when the power change does not meet the maximum power condition, performing fuzzy control on the output power based on the power change, and returning to the steps of obtaining the original output power and the updated output power until the new power change meets the maximum power condition.

[0083] The maximum power condition can refer to a power change less than a set threshold or less than or equal to a set threshold. It is understood that if the change in output power after adjustment is very small, the output power can be considered to be near or at the maximum power point. For example, if the threshold is set to "0.1," then when the absolute value of the power change is detected to be less than "0.1" or less than or equal to "0.1," the current power is considered to be at maximum power, and maximum power point tracking is complete.

[0084] Specifically, the sampling end can first collect the original output power before the output power changes, and then collect and update the output power again after the output power changes, and transmit the two collected data to the control end. The control end calculates and updates the power change of the output power relative to the original output power. If the power change meets the maximum power condition, it means that the output power point at this time is near or at the maximum power point. If the power change does not meet the maximum power condition, it is considered that the output power at this time is not around the maximum power point, and the output power needs to be adjusted again based on fuzzy reasoning until the new power change meets the maximum power condition.

[0085] Furthermore, the control end can redetermine the parameter adjustment direction of the output power, and redetermine the corresponding fuzzy set based on the power change at this time, perform fuzzy reasoning on the output power based on the power change to obtain the latest step adjustment direction, and then defuzzify the latest step adjustment direction to obtain the latest step adjustment value, adjust the output power according to the latest determined parameter adjustment direction of the output power and the latest step adjustment value to obtain the latest output power, and re-sample the updated output power and the latest output power by the sampling end and transmit them to the control end, and the control end calculates the power change again and determines whether the maximum power condition is met. If it is met, stop changing the output power and keep the output power unchanged. If it is not met, repeat the above steps until the maximum power point tracking is completed.

[0086] In this embodiment, whether the maximum power condition is met is determined based on the power change before and after adjustment, and the output power is adjusted when the maximum power condition is not met. This is equivalent to determining whether the maximum power condition is met by the contribution of the output power adjustment to the actual output power, which can achieve maximum power point tracking and improve the efficiency of the photovoltaic power generation system.

[0087] In a specific embodiment, fuzzy control of the output power based on the power change refers to repeatedly executing the processing process from step S100 to step S400, performing fuzzy reasoning based on the newly acquired sampling data, determining the parameter value adjustment direction of the working parameter matching the sampling data, and the step adjustment direction of the parameter value change, then defuzzifying the step adjustment direction to obtain the step adjustment value, and outputting a control signal matching the parameter value adjustment direction and the step adjustment value, so as to change the output power of the photovoltaic power generation system by adjusting the parameter value of the working parameter.

[0088] In one embodiment, fuzzy control is performed on the output power based on the power variation, including: if the power variation is positive, determining the parameter value adjustment direction as the updated parameter value adjustment direction, and performing fuzzy reasoning based on the acquired updated sampling data to determine the updated step adjustment direction; if the power variation is negative, determining the negative direction of the parameter adjustment direction as the updated parameter value adjustment direction, and performing fuzzy reasoning based on the acquired updated sampling data to determine the updated step adjustment direction; and determining an update control signal that matches the updated parameter value adjustment direction and the updated step adjustment direction.

[0089] Specifically, this embodiment changes the parameter value adjustment direction according to the positive or negative value of the power change. When the power change is positive, it means that the output power has not yet reached the maximum power point. The output power is adjusted along the current parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired updated sampling data to determine the step adjustment value, so that the output power is close to the maximum power point. If the power change is negative, it means that the output power has exceeded the maximum power point and the output power has a trend of gradually decreasing. The parameter value adjustment direction is changed to the opposite direction to adjust the output power, and fuzzy reasoning is performed based on the acquired updated sampling data to determine the step adjustment value, so that the output power is close to the maximum power point.

[0090] In this embodiment, the parameter value adjustment direction is updated according to the power change, and fuzzy reasoning is performed based on the acquired update sampling data to determine the update step adjustment direction, which can reduce the fuzzy reasoning workload in the subsequent adjustment process, thereby improving efficiency while ensuring the power adjustment effect.

[0091] In one embodiment, as shown in FIG3 , the output power adjustment method includes:

[0092] Step S110: acquiring sampling data of operating parameters of the photovoltaic power generation system, and determining a data change rate of the operating parameters based on the sampling data;

[0093] Step S121: determining multiple candidate fuzzy sets corresponding to the working parameters;

[0094] Step S122: mapping the data change rate to the membership function of each candidate fuzzy set, and obtaining the candidate membership degree of each candidate fuzzy set that the data change rate belongs to;

[0095] Step S123: determining the candidate fuzzy set corresponding to the maximum membership degree among the candidate membership degrees as the fuzzy set to which the data change rate belongs;

[0096] Step S130: determining the fuzzy set to which the data change rate belongs as the fuzzy set to which the sampled data belongs;

[0097] Step S210: determining, from a plurality of fuzzy rules corresponding to the fuzzy set, a first fuzzy rule that matches the parameter value adjustment direction of the working parameter and a second fuzzy rule that matches the step adjustment direction of the parameter value variation;

[0098] Step S220: Based on the first fuzzy rule, perform fuzzy reasoning on the sampled data to obtain the parameter value adjustment direction of the working parameter;

[0099] Step S230: Based on the second fuzzy rule, fuzzy reasoning is performed on the sampled data to obtain the step adjustment direction of the parameter value change;

[0100] Step S240: combining the parameter value adjustment direction and the step size adjustment direction to obtain fuzzy control information;

[0101] Step S300: performing defuzzification processing on the step length adjustment direction to obtain the step length adjustment value;

[0102] Step S400: outputting a control signal that matches the parameter value adjustment direction and the step size adjustment value;

[0103] The control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter;

[0104] Step S500: obtaining the original output power and updated output power of the photovoltaic power generation system before and after the output power is changed;

[0105] Step S600: Calculating the power variation of the updated output power relative to the original output power;

[0106] Step S710: When the power variation does not satisfy the maximum power condition, if the power variation is positive, the parameter value adjustment direction is determined as the update parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired update sampling data to determine the update step size adjustment direction;

[0107] Step S720: If the power variation does not meet the maximum power condition, and the power variation is negative, the negative direction of the parameter adjustment direction is determined as the update parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired update sampling data to determine the update step size adjustment direction;

[0108] Step S800: determining an update control signal that matches the update parameter value adjustment direction and the update step size adjustment direction; returning to step S500;

[0109] Step S730: When the power variation satisfies the maximum power condition, the process ends.

[0110] In a specific embodiment, the output power adjustment process may include the following steps:

[0111] a. Initialization: Set the initial voltage of the operating point, set a suitable step value and an initial power threshold. V0 is the initial voltage, delta_V is the step value, and initialize delta_V as a small positive value.

[0112] b. Measure power: Measure the output power of the current operating point through a sensor, and calculate the corresponding power P0 according to the current voltage V0 and current I0.

[0113] c. Parameter value adjustment direction: Increase or decrease the voltage value of the operating point (according to the step value) according to the fuzzy control signal obtained by the operation of fuzzy inference. For example: Perform fuzzy inference based on P0 and the previous power P_previous to obtain the output control signal delta_V_control.

[0114] d. Measure power: Measure the power at the new operating point.

[0115] e. Judge power change: Compare the current power with the previous power to judge the power change situation.

[0116] 1. If the power increases: Continue to adjust the voltage along the fuzzy control signal obtained by the operation of fuzzy inference, and go to step d.

[0117] 2. If the power decreases: Change the fuzzy control signal (according to the fuzzy control signal obtained by the operation of fuzzy inference), and go to step d.

[0118] 3. If the power change is not obvious or less than the set threshold: It is considered that the maximum power point has been found, stop the perturbation, and go to step f.

[0119] For example:

[0120] Adjust the voltage according to the relationship between delta_V_control and delta_V:

[0121] If delta_V_control > delta_V, increase the voltage: V1 = V0 + delta_V;

[0122] If delta_V_control < delta_V, decrease the voltage: V1 = V-_0 - delta_V;

[0123] If delta_V_control = delta_V, keep the voltage unchanged: V1 = V0.

[0124] f. Output result: Record the voltage and power of the current operating point as the maximum power point.

[0125] g. Optimize step size: Based on actual conditions and needs, the step size can be dynamically adjusted according to the magnitude of power changes to improve the accuracy and response speed of the algorithm.

[0126] h. Loop execution: Repeat steps b to f continuously to maintain the system's maximum power output under different lighting conditions.

[0127] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0128] Based on the same inventive concept, embodiments of the present application further provide an output power regulating device for implementing the aforementioned output power regulating method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the output power regulating device provided below can be found in the above-described limitations of the output power regulating method and are not further elaborated here.

[0129] In one embodiment, as shown in FIG4 , an output power adjustment device 400 is provided, comprising: an information acquisition module 410 , a fuzzy reasoning module 420 , a defuzzification module 430 , and a power adjustment module 440 , wherein:

[0130] An information acquisition module is used to obtain sampling data corresponding to sampling parameters and determine the fuzzy set to which the sampling data belongs; the sampling parameters are related to the maximum power point of the photovoltaic power generation system;

[0131] A fuzzy reasoning module is used to perform fuzzy reasoning on the sampled data based on the fuzzy rules corresponding to the fuzzy set, and determine fuzzy control information that matches the sampled data; the fuzzy control information includes the parameter value adjustment direction of the working parameter and the step size adjustment direction of the parameter value change;

[0132] A defuzzification module is used to perform defuzzification processing on the step adjustment direction to obtain the step adjustment value;

[0133] The power regulation module is used to output a control signal that matches the parameter value adjustment direction and step adjustment value; the control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter.

[0134] In one embodiment, the sampling parameters are operating parameters of the photovoltaic power generation system. In this embodiment, the information acquisition module 410 includes:

[0135] a data change rate determination component, configured to obtain sampled data of operating parameters of the photovoltaic power generation system and determine a data change rate of the operating parameters based on the sampled data;

[0136] The fuzzy set determination component is used to determine the fuzzy set to which the data change rate belongs as the fuzzy set to which the sampled data belongs.

[0137] In one embodiment, the fuzzy set determination component is further used to: determine multiple candidate fuzzy sets corresponding to the working parameters; map the data change rate to the membership function of each candidate fuzzy set respectively to obtain the candidate membership degree of the data change rate belonging to each candidate fuzzy set; and determine the candidate fuzzy set corresponding to the maximum membership degree among the candidate membership degrees as the fuzzy set to which the data change rate belongs.

[0138] In one embodiment, the fuzzy reasoning module 320 is specifically used to: determine a first fuzzy rule that matches the parameter value adjustment direction of the working parameter and a second fuzzy rule that matches the step adjustment direction of the parameter value change from multiple fuzzy rules corresponding to the fuzzy set; based on the first fuzzy rule, perform fuzzy reasoning on the sampling data to obtain the parameter value adjustment direction of the working parameter; based on the second fuzzy rule, perform fuzzy reasoning on the sampling data to obtain the step adjustment direction of the parameter value change; and combine the parameter value adjustment direction and the step adjustment direction to obtain fuzzy control information.

[0139] In one embodiment, the output power regulating device 400 further includes:

[0140] An output power acquisition module is used to obtain the original output power and updated output power of the photovoltaic power generation system before and after the output power changes;

[0141] A power variation calculation module, used to calculate the power variation of the updated output power relative to the original output power;

[0142] The fuzzy control module is used to perform fuzzy control on the output power based on the power variation when the power variation does not meet the maximum power condition, and return to the steps of obtaining the original output power and updating the output power until the new power variation meets the maximum power condition.

[0143] In one embodiment, the fuzzy control module is specifically used to: if the power change is positive, determine the parameter value adjustment direction as the update parameter value adjustment direction, and perform fuzzy reasoning based on the acquired update sampling data to determine the update step adjustment direction; if the power change is negative, determine the negative direction of the parameter adjustment direction as the update parameter value adjustment direction, and perform fuzzy reasoning based on the acquired update sampling data to determine the update step adjustment direction; determine an update control signal that matches the update parameter value adjustment direction and the update step adjustment direction.

[0144] Each module in the output power regulation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0145] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in Figure 5. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store sampled data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an output power regulation method is implemented.

[0146] Those skilled in the art will understand that the structure shown in FIG5 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0147] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0149] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0151] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to a memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0152] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An output power adjustment method, characterized in that: The method comprises: Acquire sampling data corresponding to sampling parameters, and determine the fuzzy set to which the sampling data belongs; the sampling parameters are related to the maximum power point of the photovoltaic power generation system; Based on the fuzzy rules corresponding to the fuzzy set, fuzzy reasoning is performed on the sampled data to determine fuzzy control information matching the sampled data; the fuzzy control information includes a parameter value adjustment direction of a working parameter and a step adjustment direction of a parameter value variation; Performing a defuzzification process on the step length adjustment direction to obtain a step length adjustment value; A control signal matching the parameter value adjustment direction and the step adjustment value is output; the control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter.

2. The method according to claim 1, characterized in that: The sampling parameters are working parameters of the photovoltaic power generation system; The acquiring sampling data corresponding to the sampling parameters and determining the fuzzy set to which the sampling data belongs includes: Acquire sampling data of operating parameters of the photovoltaic power generation system, and determine the data change rate of the operating parameters based on the sampling data; The fuzzy set to which the data change rate belongs is determined as the fuzzy set to which the sampling data belongs.

3. The method according to claim 2, characterized in that The method further comprises: Determine a plurality of candidate fuzzy sets corresponding to the working parameters; Mapping the data change rate to the membership function of each candidate fuzzy set respectively, to obtain the candidate membership degree of each candidate fuzzy set to which the data change rate belongs; The candidate fuzzy set corresponding to the maximum degree of membership among the candidate degrees of membership is determined as the fuzzy set to which the data change rate belongs.

4. The method according to claim 1, characterized in that: The step of performing fuzzy reasoning on the sampled data based on the fuzzy rules corresponding to the fuzzy set to determine fuzzy control information matching the sampled data includes: Determine, from among the plurality of fuzzy rules corresponding to the fuzzy set, a first fuzzy rule that matches the parameter value adjustment direction of the working parameter and a second fuzzy rule that matches the step length adjustment direction of the parameter value variation; Based on the first fuzzy rule, fuzzy reasoning is performed on the sampled data to obtain a parameter value adjustment direction of the working parameter; Based on the second fuzzy rule, fuzzy reasoning is performed on the sampled data to obtain a step adjustment direction of the parameter value change; The parameter value adjustment direction and the step size adjustment direction are combined to obtain fuzzy control information.

5. The output power adjustment method according to claim 1, characterized in that: The method further comprises: Obtaining the original output power and updated output power of the photovoltaic power generation system before and after the output power is changed; Calculating a power change of the updated output power relative to the original output power; When the power variation does not satisfy the maximum power condition, the output power is fuzzily controlled based on the power variation, and the steps of obtaining the original output power and updating the output power are returned until the new power variation satisfies the maximum power condition.

6. The output power adjustment method according to claim 5, characterized in that: The fuzzy control of the output power based on the power variation comprises: If the power variation is positive, the parameter value adjustment direction is determined as the update parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired update sampling data to determine the update step size adjustment direction; If the power variation is negative, the negative direction of the parameter adjustment direction is determined as the update parameter value adjustment direction, and fuzzy reasoning is performed based on the acquired update sampling data to determine the update step adjustment direction; An update control signal matching the update parameter value adjustment direction and the update step size adjustment direction is determined.

7. An output power adjustment device, characterized in that: The device comprises: An information acquisition module, used to acquire sampling data corresponding to sampling parameters and determine the fuzzy set to which the sampling data belongs; the sampling parameters are related to the maximum power point of the photovoltaic power generation system; A fuzzy reasoning module, configured to perform fuzzy reasoning on the sampled data based on the fuzzy rules corresponding to the fuzzy set, and determine fuzzy control information matching the sampled data; the fuzzy control information includes a parameter value adjustment direction of a working parameter and a step size adjustment direction of a parameter value variation; A defuzzification module, used for performing a defuzzification process on the step length adjustment direction to obtain a step length adjustment value; A power regulation module is used to output a control signal that matches the parameter value adjustment direction and the step adjustment value; the control signal is used to change the output power of the photovoltaic power generation system by adjusting the parameter value of the operating parameter.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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.

10. A computer program product, comprising a computer program, 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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