Method and System for Optimizing Energy Efficiency of Photovoltaic System Based on Big Data Analysis Algorithm

Abstract

The present invention relates to the technical field of photovoltaic data processing, and discloses an energy efficiency optimization method and system for a photovoltaic system based on a big data analysis algorithm, including: performing power preprocessing on the discharge power to obtain preprocessed power; calculating the power loss of the photovoltaic system using the preprocessed power, calculating the discharge efficiency of the photovoltaic system using the power loss and the output power, and determining the line optimization target of the photovoltaic system; initializing the total line length of the photovoltaic system, obtaining the line optimization scheme corresponding to the total line length, calculating the initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identifying the dominance relationship corresponding to the total line length based on the initial fitness; classifying the total line length, calculating the crowding distance corresponding to the classified line, and updating the total line length using the crowding distance; and performing line optimization on the photovoltaic system using the target optimization scheme. The present invention can reduce the numerical difference of the photovoltaic system and improve the data search ability.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm, belonging to the technical field of photovoltaic data processing. Background Art

[0002] At present, a photovoltaic system is composed of main components such as solar panels, solar controllers, storage batteries and AC inverters. These different components are connected by lines, and the connecting lines will cause certain losses. The energy efficiency of a photovoltaic system is defined as the degree of output performance or function that an electrical appliance can provide when consuming a certain amount of energy. Thus, it can be seen that if the losses caused by the photovoltaic system during power output are reduced, it is possible to ensure a certain power supply while consuming less energy, thereby improving the energy efficiency of the photovoltaic system.

[0003] Currently, in the process of improving the energy efficiency of a photovoltaic system from the perspective of losses, it is necessary to evaluate the magnitude of the losses of the photovoltaic system and optimize the losses. Since the photovoltaic system is affected by sunlight, and the intensity of sunlight is unstable, resulting in different powers of the photovoltaic system at different times, the losses between different times calculated directly by the calculation formula are quite different, and the regular distribution of losses within continuous time cannot be determined. Furthermore, the rules for reducing losses cannot be found. Secondly, the methods for optimizing the energy efficiency of a photovoltaic system include layout optimization and optimization of the number of components. The former provides changing the layout of the photovoltaic system and adjusting the lines between different components to reduce losses, thereby optimizing the energy efficiency. The latter provides adjusting the number of each component to optimize the energy efficiency. For the former, if only calculating the energy efficiency of the layout formed by each connection line and selecting the optimal energy efficiency from the energy efficiencies calculated in large quantities, the data search ability is insufficient.

[0004] Therefore, the numerical differences of the photovoltaic system are large and the data search ability during energy efficiency optimization is insufficient. Summary of the Invention

[0005] The present invention provides a method and system for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm, and its main purpose is to reduce the numerical differences of the photovoltaic system and improve the data search ability during energy efficiency optimization.

[0006] To achieve the above object, a method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm provided by the present invention includes:

[0007] Determine the photovoltaic system to be optimized for energy efficiency, collect the discharge power of the photovoltaic system within a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power, wherein the photovoltaic system includes solar panels, solar controllers, storage batteries and AC inverters;

[0008] Calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panels, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization objective of the photovoltaic system based on the power loss and the discharge efficiency;

[0009] Initialize the total length of the lines of the photovoltaic system, obtain the line optimization scheme corresponding to the total length of the lines, calculate the initial fitness corresponding to the total length of the lines using the line optimization objective and the line optimization scheme, and identify the domination relationship corresponding to the total length of the lines based on the initial fitness;

[0010] Based on the domination relationship, classify the total length of the lines to obtain the classified lines, calculate the crowding distance corresponding to the classified lines based on the initial fitness, and update the total length of the lines using the crowding distance to obtain the updated lines;

[0011] Obtain the target optimization scheme corresponding to the updated lines, and optimize the lines of the photovoltaic system using the target optimization scheme to achieve the energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

[0012] Optionally, the power preprocessing of the discharge power to obtain the preprocessed power includes:

[0013] Decimalize the discharge power using the following formula to obtain the decimal power:

[0014] ;

[0015] where represents the decimal power at the th moment, represents the discharge power at the th moment, represents the minimum discharge power within a continuous time period, represents the difference between the maximum discharge power and the minimum discharge power within a continuous time period;

[0016] According to the decimal power, perform power preprocessing on the discharge power using the following formula to obtain the preprocessed power:

[0017] ;

[0018] where represents the preprocessed power, represents the decimal power at the th moment, represents the beta function, Represents the minimum discharge power within a continuous time period. , Represents a constant greater than 0 determined by the beta distribution of .

[0019] Optionally, calculating the power loss of the photovoltaic system using the preprocessed power includes:

[0020] Obtaining the solar panels, solar controller, storage battery, and AC inverter in the photovoltaic system;

[0021] Querying the first connection line between the solar panel and the storage battery;

[0022] Querying the second connection line between the storage battery and the AC inverter;

[0023] Querying the third connection line between the solar controller and the storage battery;

[0024] Calculating the line losses of the first connection line, the second connection line, and the third connection line using the following formula:

[0025] ;

[0026] Where represents the line loss, represents the resistance per unit length of the th connection line, represents the line length of the th connection line, represents the square of the current of the th connection line;

[0027] Calculating the panel loss, controller loss, battery loss, and inverter loss of the solar panel, the solar controller, the storage battery, and the AC inverter respectively using the preprocessed power;

[0028] Determining the power loss of the photovoltaic system based on the line loss, the panel loss, the controller loss, the battery loss, and the inverter loss.

[0029] Optionally, determining the line optimization objective of the photovoltaic system based on the power loss and the discharge efficiency includes:

[0030] Obtaining the reciprocal of the discharge efficiency;

[0031] Taking the minimization of the power loss and the minimization of the reciprocal of the efficiency as the line optimization objective.

[0032] Optionally, obtaining the line optimization plan corresponding to the total line length includes:

[0033] Obtain the first connection line, the second connection line, and the third connection line of the photovoltaic system;

[0034] Randomly generate the first line length, the second line length, and the third line length of the first connection line, the second connection line, and the third connection line respectively;

[0035] Judge whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length;

[0036] When the total length of the first line length, the second line length, and the third line length is inconsistent with the total line length, remove the first line length, the second line length, and the third line length, and return to the above step of judging whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length;

[0037] When the total length of the first line length, the second line length, and the third line length is consistent with the total line length, calculate the target fitness corresponding to the first line length, the second line length, and the third line length;

[0038] When the target fitness is the minimum fitness, use the first line length, the second line length, and the third line length as the line optimization plan.

[0039] Optionally, using the line optimization target and the line optimization plan to calculate the initial fitness corresponding to the total line length includes:

[0040] Obtain the connection line corresponding to the line optimization target;

[0041] Query the target line corresponding to the line optimization plan in the connection line;

[0042] Extract the target length of the target line from the line optimization plan;

[0043] After replacing the line length of the connection line with the target length, calculate the initial fitness corresponding to the total line length.

[0044] Optionally, based on the initial fitness, identifying the dominance relationship corresponding to the total line length includes:

[0045] Obtain the first total line length and the second total line length in the total line length;

[0046] Identify the first fitness and the second fitness of the first total line length and the second total line length respectively from the initial fitness;

[0047] When at least one first fitness of the first total line length is better than the second fitness of the second total line length, and all the first fitnesses of the first total line length are not worse than the second fitnesses of the second total line length, regard the relationship that the first total line length dominates the second total line length as the domination relationship.

[0048] Optionally, classifying the total line lengths based on the domination relationship to obtain classified lines, including:

[0049] Based on the domination relationship, use the following method to determine the domination quantity and domination set of the total line lengths:

[0050] ;

[0051] Among them, 、 represents the quantity of the th total line length being dominated in the domination relationship, represents the set of total line lengths dominated by the th total line length in the domination relationship, represents the th total line length that is dominated by the total line lengths.

[0052] Query the third total line length from the total line lengths whose domination quantity meets the preset quantity;

[0053] Regard the third total line length as the first-level total length;

[0054] Obtain the fourth total line length from the domination set corresponding to the first-level total length;

[0055] Query the target domination quantity and the traversal times of the fourth total line length;

[0056] Calculate the difference between the target domination quantity and the traversal times to obtain the updated domination quantity;

[0057] Query the fifth total line length from the fourth total line lengths whose updated domination quantity meets the preset quantity;

[0058] Regard the fifth total line length as the second-level total length;

[0059] Determine the third-level total length using the fifth total line length;

[0060] Regard the first-level total length, the second-level total length and the third-level total length as the classified lines.

[0061] Optionally, calculating the crowding distance corresponding to the classified line based on the initial fitness includes:

[0062] Extracting the classified fitness corresponding to the classified line from the initial fitness;

[0063] According to the classified fitness, calculating the crowding distance corresponding to the classified line by using the following formula:

[0064] ;

[0065] wherein, represents the crowding degree of the th total length of the th line in the th target, represents the classified fitness of the th total length of the th line in the th target, represents the classified fitness of the th total length of the th line in the th target, represents the maximum value of all the total lengths of the lines in the th target, represents the minimum value of all the total lengths of the lines in the th target.

[0066] To solve the above problems, the present invention further provides an energy efficiency optimization system for a photovoltaic system implemented based on a big data analysis algorithm. The system includes:

[0067] A power preprocessing module, configured to determine a photovoltaic system to be optimized in energy efficiency, collect the discharge power of the photovoltaic system in a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power. Wherein, the photovoltaic system includes a solar panel, a solar controller, a storage battery, and an AC inverter;

[0068] A target determination module, configured to calculate the power loss of the photovoltaic system by using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system by using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency;

[0069] A relationship recognition module, configured to initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate an initial fitness corresponding to the total line length by using the line optimization objective and the line optimization scheme, and identify a dominance relationship corresponding to the total line length based on the initial fitness;

[0070] A line update module, configured to classify the total line length based on the dominance relationship to obtain a classified line, calculate a crowding distance corresponding to the classified line based on the initial fitness, and update the total line length by using the crowding distance to obtain an updated line;

[0071] An energy efficiency optimization module, configured to obtain a target optimization scheme corresponding to the updated line, and perform line optimization on the photovoltaic system by using the target optimization scheme to achieve energy efficiency optimization of the photovoltaic system and obtain an energy efficiency optimization result of the photovoltaic system.

[0072] Compared with the problems in the background art, in the embodiment of the present invention, power preprocessing is performed on the discharge power to reduce the difference between the discharge powers at different times. In the embodiment of the present invention, the power loss of the photovoltaic system is calculated by using the preprocessed power to calculate a power loss with a small numerical difference. Further, in the embodiment of the present invention, the discharge efficiency of the photovoltaic system is calculated by using the power loss and the output power to characterize the discharge efficiency by the power loss. Further, in the embodiment of the present invention, the line optimization objective of the photovoltaic system is determined based on the power loss and the discharge efficiency to characterize the line optimization objective by the power loss, so that the size of the line optimization objective can be affected by the influence of the change in the line length on the power loss in the subsequent process. In the embodiment of the present invention, the total line length is classified based on the dominance relationship to obtain a classified line for optimizing by using a genetic algorithm. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, and has the characteristics of global search ability and parallel processing, which can improve the data search ability. Therefore, the method and system for realizing the energy efficiency optimization of the photovoltaic system based on the big data analysis algorithm provided by the embodiment of the present invention can reduce the numerical difference of the photovoltaic system and improve the data search ability during energy efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic flowchart of a method for realizing the energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm provided by an embodiment of the present invention;

[0074] Figure 2 It is a schematic module diagram of a system for realizing the energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm provided by an embodiment of the present invention.

[0075] The realization of the object, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0077] The embodiment of the present application provides an energy efficiency optimization method for a photovoltaic system based on a big data analysis algorithm. The execution subject of the energy efficiency optimization method for a photovoltaic system based on a big data analysis algorithm includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the energy efficiency optimization method for a photovoltaic system based on a big data analysis algorithm can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0078] Embodiment 1:

[0079] Refer to Figure 1 As shown, it is a flowchart of an energy efficiency optimization method for a photovoltaic system based on a big data analysis algorithm provided by an embodiment of the present invention. In this embodiment, the energy efficiency optimization method for a photovoltaic system based on a big data analysis algorithm includes:

[0080] S1. Determine the photovoltaic system to be optimized in energy efficiency, collect the discharge power of the photovoltaic system within a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power, where the photovoltaic system includes a solar panel, a solar controller, a storage battery, and an AC inverter.

[0081] In the embodiment of the present invention, power preprocessing is performed on the discharge power to reduce the difference between the discharge powers at different times.

[0082] Wherein, the discharge power refers to the power output by the photovoltaic system to the power distribution cabinet. It should be noted that the output end of the solar panel is connected to the storage battery to store electrical energy in the storage battery, the output end of the solar controller is connected to the storage battery to control the charging and discharging of the storage battery, the output end of the storage battery is connected to the AC inverter to convert the DC discharged by the storage battery into AC through the AC inverter, and the output end of the AC inverter is connected to the power distribution cabinet to store the AC in the power distribution cabinet.

[0083] In an embodiment of the present invention, performing power preprocessing on the discharge power to obtain preprocessed power includes: decimalizing the discharge power using the following formula to obtain decimal power:

[0084] ;

[0085] Among them, represents the fractional power at the moment, represents the discharge power at the moment, represents the minimum discharge power within a continuous time period, represents the difference between the maximum discharge power and the minimum discharge power within a continuous time period;

[0086] According to the fractional power, the discharge power is preprocessed using the following formula to obtain the preprocessed power:

[0087] ;

[0088] Among them, represents the preprocessed power, represents the fractional power at the moment, represents the beta function, represents the minimum discharge power within a continuous time period, , represents a constant greater than 0 determined by the beta distribution of .

[0089] S2. Calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency.

[0090] In the embodiment of the present invention, by calculating the power loss of the photovoltaic system using the preprocessed power, the power loss with a small numerical difference can be calculated.

[0091] In one embodiment of the present invention, calculating the power loss of the photovoltaic system using the preprocessed power includes: obtaining the solar panel, solar controller, battery, and AC inverter in the photovoltaic system; querying the first connection line between the solar panel and the battery; querying the second connection line between the battery and the AC inverter; querying the third connection line between the solar controller and the battery; calculating the line losses of the first connection line, the second connection line, and the third connection line using the following formula:

[0092] ;

[0093] Among them, represents the line loss, represents the The resistance corresponding to the unit length of each connection line denotes the line length of the th connection line; denotes the square of the current of the

[0094] Using the preprocessed power, calculate the panel loss, controller loss, battery loss, and inverter loss of the solar panel, the solar controller, the battery, and the AC inverter respectively; determine the power loss of the photovoltaic system through the line loss, the panel loss, the controller loss, the battery loss, and the inverter loss.

[0095] Optionally, the process of using the preprocessed power to calculate the panel loss, controller loss, battery loss, and inverter loss of the solar panel, the solar controller, the battery, and the AC inverter respectively refers to: obtaining the discharge power before preprocessing the preprocessed power, which is the power at the output end of the AC inverter. The conversion efficiencies of the input to output power of the solar controller, the battery, and the AC inverter are known in advance. From the conversion efficiency, the power at the input end of the AC inverter can be calculated. Subtracting the line loss experienced from the battery to the inverter from the power at the input end gives the power at the output end of the battery. Calculate the input power and output power of each component in turn. The difference between the input power and the output power is the loss of each component. The panel loss of the solar panel is regarded as 0. Further, the process of determining the power loss of the photovoltaic system through the line loss, the panel loss, the controller loss, the battery loss, and the inverter loss refers to the process of preprocessing the power loss. The preprocessing principle is similar to the principle of performing power preprocessing on the discharge power to obtain the preprocessed power, and will not be elaborated further here.

[0096] Among them, the output power refers to the power at the output end of the solar panel.

[0097] Further, in the embodiment of the present invention, the discharge efficiency of the photovoltaic system is calculated by using the power loss and the output power to characterize the discharge efficiency through the power loss.

[0098] In an embodiment of the present invention, the calculation of the discharge efficiency of the photovoltaic system by using the power loss and the output power includes: calculating the power difference between the output power and the power loss; taking the ratio of the power difference to the preset power as the discharge efficiency.

[0099] Among them, the preset power refers to the output power.

[0100] Further, in the embodiment of the present invention, the line optimization objective of the photovoltaic system is determined based on the power loss and the discharge efficiency, and the power loss is used to characterize the line optimization objective, so that the size of the line optimization objective can be affected by the influence of the change of the line length on the power loss in the subsequent process.

[0101] In an embodiment of the present invention, the determining the line optimization objective of the photovoltaic system based on the power loss and the discharge efficiency includes: obtaining the reciprocal of the efficiency of the discharge efficiency; taking the minimization of the power loss and the minimization of the reciprocal of the efficiency as the line optimization objective.

[0102] S3. Initialize the total line length of the photovoltaic system, obtain the line optimization scheme corresponding to the total line length, calculate the initial fitness corresponding to the total line length by using the line optimization objective and the line optimization scheme, and identify the domination relationship corresponding to the total line length based on the initial fitness.

[0103] Wherein, the total line length refers to the total length of all lines within a predetermined range, and the value of the total line length is a randomly generated value. Therefore, there are various sizes of the total line length, and the predetermined range refers to the range from the shortest total line length that the person in charge of the photovoltaic system can accept to the longest total line length.

[0104] In an embodiment of the present invention, the obtaining the line optimization scheme corresponding to the total line length includes: obtaining the first connection line, the second connection line and the third connection line of the photovoltaic system; respectively randomly generating the first line length, the second line length and the third line length of the first connection line, the second connection line and the third connection line; judging whether the total length of the first line length, the second line length and the third line length is consistent with the total line length; when the total length of the first line length, the second line length and the third line length is inconsistent with the total line length, removing the first line length, the second line length and the third line length, and returning to the above step of judging whether the total length of the first line length, the second line length and the third line length is consistent with the total line length; when the total length of the first line length, the second line length and the third line length is consistent with the total line length, calculating the target fitness corresponding to the first line length, the second line length and the third line length; when the target fitness is the minimum fitness, taking the first line length, the second line length and the third line length as the line optimization scheme.

[0105] Optionally, the principle of calculating the target fitness corresponding to the first line length, the second line length, and the third line length is similar to the principle of determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency described above. The target fitness includes two values, namely, the power loss and the reciprocal of the efficiency. It should be noted that during the process of randomly generating the first line length, the second line length, and the third line length of the first connection line, the second connection line, and the third connection line respectively, different line lengths will be generated. Therefore, the calculated target fitness is also a plurality of values.

[0106] Further, in the embodiment of the present invention, the initial fitness refers to the fitness of each total line length, and there are multiple values.

[0107] In an embodiment of the present invention, calculating the initial fitness corresponding to the total line length by using the line optimization target and the line optimization scheme includes: obtaining the connection line corresponding to the line optimization target; querying the target line corresponding to the line optimization scheme in the connection line; extracting the target length of the target line in the line optimization scheme; and after replacing the line length of the connection line with the target length, calculating the initial fitness corresponding to the total line length.

[0108] It should be noted that the principle of calculating the initial fitness corresponding to the total line length by using the line optimization target and the line optimization scheme is similar to the principle of determining the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency described above, and will not be further elaborated here.

[0109] In an embodiment of the present invention, identifying the dominance relationship corresponding to the total line length based on the initial fitness includes: obtaining the first total line length and the second total line length in the total line length; respectively identifying the first fitness and the second fitness of the first total line length and the second total line length from the initial fitness; and when at least one first fitness of the first total line length is better than the second fitness of the second total line length, and all the first fitnesses of the first total line length are not worse than the second fitness of the second total line length, taking the relationship that the first total line length dominates the second total line length as the dominance relationship.

[0110] Wherein, the first fitness includes two values, namely, the power loss and the reciprocal of the efficiency, and the second fitness is the same.

[0111] S4. Based on the dominance relationship, classifying the total line lengths to obtain classified lines, calculating the crowding distance corresponding to the classified lines based on the initial fitness, and updating the total line lengths by using the crowding distance to obtain updated lines.

[0112] In an embodiment of the present invention, based on the domination relationship, the total line length is classified into classified lines for optimizing using a genetic algorithm. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, with the characteristics of global search ability and parallel processing, which can improve the data search ability.

[0113] In an embodiment of the present invention, the classifying the total line length into classified lines based on the domination relationship includes: determining the domination quantity and domination set of the total line length by using the following method based on the domination relationship:

[0114] ;

[0115] wherein, 、 represents the quantity dominated by the -th total line length in the domination relationship, represents the set of total line lengths dominated by the -th total line length in the domination relationship, represents the -th total line length dominated by the -th total line length;

[0116] Query the third total line length with the domination quantity meeting a preset quantity from the total line lengths; use the third total line length as the first-level total length; obtain the fourth total line length from the domination set corresponding to the first-level total length; query the target domination quantity and the traversal times of the fourth total line length; calculate the difference between the target domination quantity and the traversal times to obtain the updated domination quantity; query the fifth total line length with the updated domination quantity meeting the preset quantity from the fourth total line lengths; use the fifth total line length as the second-level total length; determine the third-level total length using the fifth total line length; use the first-level total length, the second-level total length and the third-level total length as the classified lines.

[0117] wherein, the preset quantity is 0.

[0118] Exemplarily, first, each total line length is used as a node individual, is used as the power loss, is used as the reciprocal of efficiency, is used as the first-level total length set, is used as the second-level total length set, is used as the third-level total length set; for node , node and node in and Both are smaller than C, so it is called: Nodes D and E can dominate C, so , and is dominated nodes have , so ; Select individuals in the population with a count of 0, indicating that they are not dominated by other individuals. This set is divided into the first level, that is of , in order to find , Each individual in has a list that includes all the individuals it dominates. Currently , for each individual in the list, each time it is traversed, the corresponding count is decreased by 1. In the current case, it is traversed —times, so , at this time , traverse twice, so , at this time , traverse C twice, so , at this time , after that, the nodes with a count of 0 are classified into the second category, , in order to discover , just traverse all the individuals in, subtract again, and check which individual has a value of 0, and classify them into .

[0119] In an embodiment of the present invention, calculating the crowding distance corresponding to the classified line based on the initial fitness includes: extracting the classified fitness corresponding to the classified line from the initial fitness; calculating the crowding distance corresponding to the classified line according to the classified fitness by using the following formula:

[0120] ;

[0121] Wherein, represents the crowding degree of the total length of the th line in the total length of the th line on the th target, represents the classified fitness of the total length of the th line in the total length of the th line on the th target, represents the classified fitness of the total length of the th line in the total length of the th line on the th target, represents the maximum value of the total length of all lines on the th target, represents the total length of all lines on the The minimum value on a target.

[0122] Wherein, , represents the power loss, represents the reciprocal of efficiency.

[0123] Optionally, the process of using the crowding distance to update the total line length to obtain an updated line refers to: selecting the th level total length set with a large crowding distance, such as all the total line lengths in the first level total length, and then classifying the line total lengths to obtain sets of three levels F1, F2, and F3. Once again, use the crowding distance to screen out only one set until there is only one line total length in the screened set, and use this only one line total length as the updated line.

[0124] S5. Obtain the target optimization plan corresponding to the updated line, and use the target optimization plan to optimize the lines of the photovoltaic system to achieve the energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

[0125] Optionally, the process of using the target optimization plan to optimize the lines of the photovoltaic system refers to: the target optimization plan refers to the first line length, the second line length, and the third line length corresponding to the updated line, and adjust the line lengths of the photovoltaic system to the first line length, the second line length, and the third line length respectively.

[0126] Compared with the problems in the background art, in the embodiments of the present invention, power preprocessing is performed on the discharge power to reduce the difference between the discharge powers at different times. In the embodiments of the present invention, the power loss of the photovoltaic system is calculated by using the preprocessed power to calculate the power loss with a small numerical difference. Further, in the embodiments of the present invention, the discharge efficiency of the photovoltaic system is calculated by using the power loss and the output power to characterize the discharge efficiency through the power loss. Further, in the embodiments of the present invention, based on the power loss and the discharge efficiency, the line optimization target of the photovoltaic system is determined to characterize the line optimization target through the power loss, so that the magnitude of the line optimization target can be affected by the influence of the change in the line length on the power loss in the subsequent process. In the embodiments of the present invention, based on the domination relationship, the total line lengths are classified to obtain classified lines for optimization using the genetic algorithm. The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, with the characteristics of global search ability and parallel processing, which can improve the data search ability. Therefore, the method and system for realizing the energy efficiency optimization of the photovoltaic system based on the big data analysis algorithm provided by the embodiments of the present invention can reduce the numerical difference of the photovoltaic system and improve the data search ability during energy efficiency optimization.

[0127] Example 2:

[0128] As Figure 2 shown, it is a functional block diagram of an energy efficiency optimization system for a photovoltaic system implemented based on a big data analysis algorithm according to the present invention.

[0129] The energy efficiency optimization system 200 for a photovoltaic system implemented based on a big data analysis algorithm according to the present invention can be installed in an electronic device. According to the functions achieved, the energy efficiency optimization system for a photovoltaic system implemented based on a big data analysis algorithm may include a power preprocessing module 201, a target determination module 202, a relationship recognition module 203, a line update module 204, and an energy efficiency optimization module 205. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0130] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0131] The power preprocessing module 201 is used to determine the photovoltaic system to be optimized for energy efficiency, collect the discharge power of the photovoltaic system within a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power, where the photovoltaic system includes a solar panel, a solar controller, a storage battery, and an AC inverter;

[0132] The target determination module 202 is used to calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency;

[0133] The relationship recognition module 203 is used to initialize the total length of the lines of the photovoltaic system, obtain the line optimization scheme corresponding to the total length of the lines, calculate the initial fitness corresponding to the total length of the lines using the line optimization target and the line optimization scheme, and identify the dominance relationship corresponding to the total length of the lines based on the initial fitness;

[0134] The line update module 204 is used to classify the total length of the lines based on the dominance relationship to obtain classified lines, calculate the crowding distance corresponding to the classified lines based on the initial fitness, and update the total length of the lines using the crowding distance to obtain updated lines;

[0135] The energy efficiency optimization module 205 is configured to obtain a target optimization solution corresponding to the updated circuit, and use the target optimization solution to optimize the circuit of the photovoltaic system, so as to achieve the energy efficiency optimization of the photovoltaic system and obtain the energy efficiency optimization result of the photovoltaic system.

[0136] Specifically, each module in the energy efficiency optimization system 200 of the photovoltaic system implemented based on the big data analysis algorithm in the embodiments of the present invention adopts the same technical means as those in the Figure 1 energy efficiency optimization method of the photovoltaic system implemented based on the big data analysis algorithm described above, and can produce the same technical effects, which will not be elaborated here.

[0137] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not 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.

Claims

1. A method for realizing energy efficiency optimization of a photovoltaic system based on a big data analysis algorithm, characterized in that, The method includes: Determine a photovoltaic system to be optimized for energy efficiency, collect the discharge power of the photovoltaic system within a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power, where the photovoltaic system includes a solar panel, a solar controller, a storage battery, and an AC inverter; Calculate the power loss of the photovoltaic system using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; Initialize the total line length of the photovoltaic system, obtain the line optimization scheme corresponding to the total line length, calculate the initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme, and identify the dominance relationship corresponding to the total line length based on the initial fitness, where the calculating the initial fitness corresponding to the total line length using the line optimization target and the line optimization scheme includes: Obtain the connection line corresponding to the line optimization target; Query the target line corresponding to the line optimization scheme in the connection line; Extract the target length of the target line in the line optimization scheme; After replacing the line length of the connection line with the target length, calculate the initial fitness corresponding to the total line length; The identifying the dominance relationship corresponding to the total line length based on the initial fitness includes: Obtain the first total line length and the second total line length in the total line length; Respectively identify the first fitness and the second fitness of the first total line length and the second total line length from the initial fitness; When at least one first fitness of the first total line length is better than the second fitness of the second total line length, and all the first fitnesses of the first total line length are not worse than the second fitness of the second total line length, use the relationship that the first total line length dominates the second total line length as the dominance relationship; Based on the dominance relationship, classify the total line length to obtain classified lines, calculate the crowding distance corresponding to the classified lines based on the initial fitness, and update the total line length using the crowding distance to obtain updated lines; Obtain the target optimization scheme corresponding to the updated lines, and perform line optimization on the photovoltaic system using the target optimization scheme to achieve energy efficiency optimization of the photovoltaic system, and obtain the energy efficiency optimization result of the photovoltaic system.

2. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, wherein The performing power preprocessing on the discharge power to obtain preprocessed power includes: Decimalize the discharge power using the following formula to obtain decimal power: ； Among them, represents the fractional power at the moment, represents the discharge power at the moment, represents the minimum discharge power within a continuous time period, represents the difference between the maximum discharge power and the minimum discharge power within a continuous time period; According to the decimal power, perform power preprocessing on the discharge power using the following formula to obtain preprocessed power; ； Among them, represents the preprocessing power, represents the fractional power at the moment, and 、 represents a constant greater than 0 determined by the maximum likelihood estimation method.

3. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm according to claim 1, wherein The calculating the power loss of the photovoltaic system using the preprocessed power includes: Obtain the solar panel, the solar controller, the storage battery, and the AC inverter in the photovoltaic system; Query the first connection line between the solar panel and the storage battery; Query the second connection line between the storage battery and the AC inverter; Query the third connection line between the solar controller and the storage battery; Calculate the line losses of the first connection line, the second connection line, and the third connection line using the following formula: ； Among them, represents the line loss, represents the resistance corresponding to the unit length of the th connecting line, represents the line length of the th connecting line, represents the square of the current of the th connecting line; Calculate the panel loss, controller loss, battery loss, and inverter loss of the solar panel, the solar controller, the storage battery, and the AC inverter respectively using the preprocessed power; Determine the power loss of the photovoltaic system based on the line losses, the panel loss, the controller loss, the battery loss, and the inverter loss; 4. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm as claimed in claim 1, wherein Based on the power loss and the discharge efficiency, determine the line optimization objective of the photovoltaic system, including: Obtain the reciprocal of the efficiency of the discharge efficiency; Take the minimization of the power loss and the minimization of the reciprocal of the efficiency as the line optimization objective; 5. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm as claimed in claim 1, wherein The obtaining of the line optimization scheme corresponding to the total line length includes: Obtain the first connection line, the second connection line, and the third connection line of the photovoltaic system; Randomly generate the first line length, the second line length, and the third line length of the first connection line, the second connection line, and the third connection line respectively; Judge whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; When the total length of the first line length, the second line length, and the third line length is inconsistent with the total line length, remove the first line length, the second line length, and the third line length, and return to the above step of judging whether the total length of the first line length, the second line length, and the third line length is consistent with the total line length; When the total length of the first line length, the second line length, and the third line length is consistent with the total line length, calculate the target fitness corresponding to the first line length, the second line length, and the third line length; When the target fitness is the minimum fitness, take the first line length, the second line length, and the third line length as the line optimization scheme; 6. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm as claimed in claim 1, wherein Based on the dominance relationship, classify the total line length to obtain the classified lines, including: Based on the dominance relationship, use the following method to determine the dominance quantity and the dominance set of the total line length: ； Among them, , represents the number of the th total line lengths that are dominated in the domination relationship, represents the set of the total line lengths dominated by the th total line length in the domination relationship, represents the th total line length that is dominated by the th total line length Query the third total line length whose dominance quantity meets the preset quantity from the total line length; Take the third total line length as the first-level total length; Obtain the fourth total line length from the dominance set corresponding to the first-level total length; Query the target dominance quantity and the traversal times of the fourth total line length; Calculate the difference between the target dominance quantity and the traversal times to obtain the updated dominance quantity; Query the fifth total line length whose updated dominance quantity meets the preset quantity from the fourth total line length; Take the fifth total line length as the second-level total length; Determine the third-level total length using the fifth total line length; Take the first-level total length, the second-level total length, and the third-level total length as the classified lines.

7. The method for optimizing the energy efficiency of a photovoltaic system based on a big data analysis algorithm as claimed in claim 1, wherein Calculating the crowding distance corresponding to the classified line based on the initial fitness includes: Extracting the classified fitness corresponding to the classified line from the initial fitness; Calculating the crowding distance corresponding to the classified line according to the classified fitness by using the following formula: ； Among them, represents the congestion degree of the th total length of the th line total length on the th target, represents the classification fitness of the th total length of the th line total length on the th target, represents the classification fitness of the th total length of the th line total length on the th target, represents the maximum value of all line total lengths on the th target, represents the minimum value of all line total lengths on the th target.

8. An energy efficiency optimization system for a photovoltaic system implemented based on a big data analysis algorithm, characterized in that, The system includes: A power preprocessing module, configured to determine a photovoltaic system to be optimized for energy efficiency, collect the discharge power of the photovoltaic system within a continuous time period, and perform power preprocessing on the discharge power to obtain preprocessed power, where the photovoltaic system includes a solar panel, a solar controller, a storage battery, and an AC inverter; A target determination module, configured to calculate the power loss of the photovoltaic system by using the preprocessed power, collect the output power of the solar panel, calculate the discharge efficiency of the photovoltaic system by using the power loss and the output power, and determine the line optimization target of the photovoltaic system based on the power loss and the discharge efficiency; A relationship recognition module, configured to initialize the total line length of the photovoltaic system, obtain a line optimization scheme corresponding to the total line length, calculate the initial fitness corresponding to the total line length by using the line optimization target and the line optimization scheme, and recognize the dominance relationship corresponding to the total line length based on the initial fitness, where calculating the initial fitness corresponding to the total line length by using the line optimization target and the line optimization scheme includes: Obtaining a connection line corresponding to the line optimization target; Querying a target line corresponding to the line optimization scheme in the connection line; Extracting the target length of the target line in the line optimization scheme; After replacing the line length of the connection line with the target length, calculating the initial fitness corresponding to the total line length; Recognizing the dominance relationship corresponding to the total line length based on the initial fitness includes: Obtaining a first total line length and a second total line length in the total line length; Respectively recognizing a first fitness and a second fitness of the first total line length and the second total line length from the initial fitness; When at least one first fitness of the first total line length is better than the second fitness of the second total line length, and all first fitnesses of the first total line length are not worse than the second fitness of the second total line length, taking the relationship that the first total line length dominates the second total line length as the dominance relationship; A line update module, configured to classify the total line length based on the dominance relationship to obtain a classified line, calculate the crowding distance corresponding to the classified line based on the initial fitness, and update the total line length by using the crowding distance to obtain an updated line; An energy efficiency optimization module, configured to obtain a target optimization scheme corresponding to the updated line, and perform line optimization on the photovoltaic system by using the target optimization scheme to achieve energy efficiency optimization of the photovoltaic system and obtain an energy efficiency optimization result of the photovoltaic system.

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