A method and system for constructing a data model of a high-altitude photovoltaic system

Abstract

The present invention relates to the technical field of photovoltaic system model construction, and specifically to a method and system for constructing a data model of a high-altitude photovoltaic system. The method includes obtaining first data; starting the photovoltaic power generation system, and measuring a first radiation intensity data sequence, a first output power data sequence, and a first ambient temperature data sequence within a first test time; aggregating to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and constructing a fitting sample set; calculating a first function by using a fitting algorithm; and denoting the first function as the photovoltaic system data model. The present invention realizes the construction of a data model of a high-altitude photovoltaic system by obtaining the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic system model construction, and in particular to a data model construction method and system for a high-altitude photovoltaic system. Background Art

[0002] With the transformation of energy structure and the rapid development of renewable energy, photovoltaic power generation system, as an important part of distributed power source, plays an increasingly important role in energy supply. In order to ensure the safe and stable operation of the power grid, accurate prediction of the output power of photovoltaic system is of great significance to the grid dispatch.

[0003] At present, the output power prediction of photovoltaic systems is mainly achieved by constructing data models. The method commonly used in the prior art is to establish a functional relationship model between the predicted average value of solar radiation intensity, the predicted average value of ambient temperature and the predicted output power of the photovoltaic power generation system, and then predict the output power of the photovoltaic power generation system based on the pre-obtained predicted average value of solar radiation intensity and the predicted average value of ambient temperature. However, for photovoltaic systems in high-altitude areas, the above method has obvious shortcomings. This is because high-altitude areas have the characteristics of large temperature differences and drastic temperature fluctuations. The resistance of the photovoltaic system is not only affected by the average value of the ambient temperature, but also significantly affected by the severity of the temperature fluctuations. When constructing data models for high-altitude photovoltaic systems, the prior art often ignores the important factor of temperature fluctuations, resulting in a large deviation between the prediction results and the actual situation.

[0004] Therefore, there is an urgent need to provide a method for predicting the output power of photovoltaic power generation systems in environments with drastic temperature fluctuations in high-altitude areas. By building a more complete data model, the prediction accuracy can be improved to provide strong support for the grid-connected operation of photovoltaic systems in high-altitude areas. Summary of the invention

[0005] (1) Technical issues to be resolved

[0006] The purpose of the present invention is to provide a data model construction method and system for a high-altitude photovoltaic system, so as to obtain a functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted discreteness of the ambient temperature, so as to realize the data model construction of the high-altitude photovoltaic system.

[0007] (2) Technical solution

[0008] To achieve the above object, the present invention provides a method for constructing a data model of a high-altitude photovoltaic system, the method comprising the following steps:

[0009] S1. Obtain the first data, where the first data includes the rated photoelectric conversion efficiency, the area of the photovoltaic panel, the rated resistance of the photovoltaic power generation system, the rated voltage of the photovoltaic power generation system, the preset sampling aggregation quantity, the preset first sampling interval time, and the preset first test time.

[0010] S2. Turn on the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel at intervals of the first sampling interval time within the first test time, to obtain the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence.

[0011] S3. Aggregate the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence, and construct a fitting sample set based on the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence.

[0012] S4. According to the fitting sample set, use a fitting algorithm to calculate the first function, where the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature; denote the first function as the photovoltaic system data model.

[0013] Further, the method of aggregating the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence, and constructing a fitting sample set based on the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence includes:

[0014] According to the first radiation intensity data sequence, use the radiation intensity aggregation formula to calculate the second radiation intensity data sequence; the radiation intensity aggregation formula is:

[0015] ;

[0016] where represents the th data in the second radiation intensity data sequence; represents the th data in the first radiation intensity data sequence; represents the sampling aggregation quantity; The value of is greater than 1; is an integer variable with values ranging from 1 to ; is an integer variable with values ranging from 1 to ; represents the second length; The calculation formula of

[0017] ;

[0018] Among them, represents the first test time, represents the first sampling interval time.

[0019] According to the first output power data sequence, the second output power data sequence is calculated by using the power aggregation formula; the power aggregation formula is:

[0020] ;

[0021] Among them, represents the th data in the second output power data sequence; represents the th data in the first output power data sequence.

[0022] According to the first ambient temperature data sequence, the second ambient temperature data sequence is calculated by using the second temperature aggregation formula; the second temperature aggregation formula is:

[0023] ;

[0024] Among them, represents the th data in the second ambient temperature data sequence; represents the th data in the first ambient temperature data sequence.

[0025] According to the first ambient temperature data sequence, the third ambient temperature data sequence is calculated by using the third temperature aggregation formula; the third temperature aggregation formula is:

[0026] ;

[0027] Among them, represents the th data in the third ambient temperature data sequence.

[0028] A fitting sample set is constructed according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; the fitting sample set is expressed as:

[0029] ;

[0030] Among them, represents the fitting sample set; represents the th sample in the fitting sample set; is expressed as:

[0031] .

[0032] Furthermore, the method for calculating the first function based on the fitting sample set by using a fitting algorithm, where the dependent variable of the first function is the predicted output power of the photovoltaic power generation system and the independent variables of the first function are the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature includes:

[0033] Construct a first fitting equation according to the rated photovoltaic conversion efficiency, the area of the photovoltaic panel, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as:

[0034] ;

[0035] Among them, represents the area of the photovoltaic panel, represents the rated photovoltaic conversion efficiency, represents the rated voltage of the photovoltaic power generation system, represents the rated resistance of the photovoltaic power generation system, represents a preset reference temperature, represents the first resistance temperature coefficient, represents the second resistance temperature coefficient, represents the remaining power.

[0036] Construct a first objective function according to the fitting sample set and the first fitting equation; construct a first constraint condition according to the fitting sample set and the first fitting equation.

[0037] With the goal of minimizing the value of the first objective function, with the first constraint condition as the constraint, and with the first resistance temperature coefficient and the second resistance temperature coefficient as the optimization variables, use the particle swarm optimization algorithm to calculate the optimal values of the first resistance temperature coefficient and the second resistance temperature coefficient, which are respectively denoted as the first optimization coefficient , the second optimization coefficient .

[0038] Calculate the first optimized remaining power to the optimized remaining power by using the salvage value formula; the salvage value formula is:

[0039] ;

[0040] Among them, represents the optimized remaining power.

[0041] According to the first optimization coefficient , the second optimization coefficient , the first optimized remaining power to the optimized remaining power to obtain the first optimization equation; the first optimization equation is expressed as:

[0042] ;

[0043] Among them, represents the predicted output power of the photovoltaic power generation system, represents the predicted average value of the solar radiation intensity obtained in advance, represents the predicted average value of the ambient temperature obtained in advance, represents the predicted dispersion of the ambient temperature obtained in advance.

[0044] According to the first optimization equation, the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature is solved and denoted as the first function; the first function is expressed as:

[0045] .

[0046] Furthermore, the method for constructing the first objective function according to the fitting sample set and the first fitting equation includes:

[0047] Construct the first objective function according to the fitting sample set and the first fitting equation , the first objective function is expressed as:

[0048] .

[0049] Furthermore, the method for constructing the first constraint condition according to the fitting sample set and the first fitting equation includes:

[0050] Construct the first constraint condition according to the fitting sample set and the first fitting equation, and the first constraint condition is expressed as:

[0051] ;

[0052] Among them, represents the upper limit of the remaining fluctuation constraint set in advance.

[0053] On the other hand, based on the same inventive concept, the present invention also provides a data model construction system for a high-altitude photovoltaic system, and the system includes:

[0054] A data reading module, configured to obtain first data, where the first data includes rated photovoltaic conversion efficiency, photovoltaic panel area, rated resistance of the photovoltaic power generation system, rated voltage of the photovoltaic power generation system, a preset sampling aggregation quantity, a preset first sampling interval time, and a preset first test time.

[0055] A data measurement module, connected to the data reading module, configured to turn on the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel at intervals of the first sampling interval time within the first test time, so as to obtain a first radiation intensity data sequence, a first output power data sequence, and a first ambient temperature data sequence.

[0056] A sample construction module, connected to the data measurement module, configured to aggregate the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence.

[0057] A data model construction module, connected to the sample construction module, configured to calculate a first function according to the fitting sample set by using a fitting algorithm, where the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature; and record the first function as the photovoltaic system data model.

[0058] Further, the sample construction module includes:

[0059] A second radiation intensity data sequence construction module, configured to calculate a second radiation intensity data sequence according to the first radiation intensity data sequence by using a radiation intensity aggregation formula; the radiation intensity aggregation formula is:

[0060] ;

[0061] Wherein, represents the th data in the second radiation intensity data sequence; represents the th data in the first radiation intensity data sequence; represents the sampling aggregation quantity; The value of ranges from 1 to and is an integer variable; ranges from 1 to and is an integer variable; represents the second length; The calculation formula for

[0062] is:

[0063] where represents the first test time, and

[0064] The second output power data sequence construction module, connected to the second radiation intensity data sequence construction module, is used to calculate the second output power data sequence according to the first output power data sequence by using the power aggregation formula; the power aggregation formula is:

[0065] ;

[0066] where represents the -th data in the second output power data sequence; represents the -th data in the first output power data sequence.

[0067] The second environmental temperature data sequence construction module, connected to the second output power data sequence construction module, is used to calculate the second environmental temperature data sequence according to the first environmental temperature data sequence by using the second temperature aggregation formula; the second temperature aggregation formula is:

[0068] ;

[0069] where represents the -th data in the second environmental temperature data sequence; represents the -th data in the first environmental temperature data sequence.

[0070] The third environmental temperature data sequence construction module, connected to the second environmental temperature data sequence construction module, is used to calculate the third environmental temperature data sequence according to the first environmental temperature data sequence by using the third temperature aggregation formula; the third temperature aggregation formula is:

[0071] ;

[0072] where represents the a data

[0073] A fitting sample set generation module, connected to the third ambient temperature data sequence construction module, is configured to construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; the fitting sample set is expressed as:

[0074] ;

[0075] wherein represents the fitting sample set; represents the th sample in the fitting sample set; is expressed as:

[0076] .

[0077] Further, the data model construction module includes:

[0078] A first fitting equation construction module, configured to construct a first fitting equation according to the rated photoelectric conversion efficiency, the photovoltaic panel area, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as:

[0079] ;

[0080] wherein represents the photovoltaic panel area, represents the rated photoelectric conversion efficiency, represents the rated voltage of the photovoltaic power generation system, represents the rated resistance of the photovoltaic power generation system, represents a preset reference temperature, represents the first resistance temperature coefficient, represents the second resistance temperature coefficient, represents the remaining power.

[0081] An optimization model construction module, connected to the first fitting equation construction module, is configured to construct a first objective function according to the fitting sample set and the first fitting equation; construct a first constraint condition according to the fitting sample set and the first fitting equation.

[0082] An optimization calculation module, connected to the optimization model construction module, is configured to take the minimum value of the first objective function as the target, take the first constraint condition as the constraint, take the first resistance temperature coefficient and the second resistance temperature coefficient as optimization variables, and use the particle swarm optimization algorithm to calculate the optimal values of the first resistance temperature coefficient and the second resistance temperature coefficient, which are respectively denoted as the first optimization coefficient , Second optimization coefficient .

[0083] Optimized residual power calculation module, connected to the optimization calculation module, for calculating the first optimized residual power using the salvage value formula to the Optimized residual power ; The salvage value formula is:

[0084] ;

[0085] Among them, represents the Optimized residual power.

[0086] First optimization equation construction module, connected to the optimized residual power calculation module, for obtaining the first optimization equation according to the first optimization coefficient , Second optimization coefficient , First optimized residual power to the Optimized residual power to obtain the first optimization equation; The first optimization equation is expressed as:

[0087] ;

[0088] Among them, represents the predicted output power of the photovoltaic power generation system obtained in advance, represents the predicted average value of solar radiation intensity obtained in advance, represents the predicted average value of ambient temperature obtained in advance, represents the predicted dispersion of ambient temperature obtained in advance.

[0089] First function construction module, connected to the first optimization equation construction module, for solving the functional relationship between the predicted output power of the photovoltaic power generation system, the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature according to the first optimization equation, denoted as the first function; The first function is expressed as:

[0090] .

[0091] Furthermore, the optimization model construction module includes:

[0092] First objective function construction module, for constructing the first objective function according to the fitting sample set and the first fitting equation , The first objective function is expressed as:

[0093] .

[0094] Furthermore, the optimization model construction module further includes:

[0095] A first constraint condition construction module, configured to construct a first constraint condition according to the fitting sample set and the first fitting equation, and the first constraint condition is expressed as:

[0096] ;

[0097] Wherein, represents a preset upper limit of the remaining fluctuation constraint.

[0098] (3) Beneficial effects

[0099] Compared with the prior art, the beneficial effects of the present invention are:

[0100] According to the fitting sample set, the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature is calculated by using a fitting algorithm, so as to realize the construction of the data model of the high-altitude photovoltaic system. Brief description of the drawings

[0101] Figure 1 is a flowchart of a method for constructing a data model of a high-altitude photovoltaic system according to Embodiment 1 of the present invention;

[0102] Figure 2 is a schematic diagram of the module composition of a data model construction system of a high-altitude photovoltaic system according to Embodiment 2 of the present invention. Detailed implementation manners

[0103] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0104] Before giving examples, it is necessary to elaborate on the application scenario of the inventive concept of the present invention. The present invention is applied to obtain the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature in an environment with severe ambient temperature fluctuations at high altitudes, so as to realize the construction of the data model of the high-altitude photovoltaic system.

[0105] Embodiment 1: As Figure 1 shown, this embodiment provides a method for constructing a data model of a high-altitude photovoltaic system, and the method includes the following steps:

[0106] S1. Obtain the first data, where the first data includes the rated photoelectric conversion efficiency, the area of the photovoltaic panel, the rated resistance of the photovoltaic power generation system, the rated voltage of the photovoltaic power generation system, the preset sampling aggregation quantity, the preset first sampling interval time, and the preset first test time.

[0107] Exemplarily, obtain the rated photoelectric conversion efficiency, the area of the photovoltaic panel, the rated resistance of the photovoltaic power generation system, and the rated voltage of the photovoltaic power generation system by reading the design specification of the photovoltaic power generation system. The rated photoelectric conversion efficiency is 17.8%, the area of the photovoltaic panel is 39.6 square meters, the rated resistance of the photovoltaic power generation system is 0.3 ohms, the rated voltage of the photovoltaic power generation system is 220 volts, the preset sampling aggregation quantity is 60, the preset first sampling interval time is 60 seconds, and the preset first test time is 360,000 seconds.

[0108] S2. Turn on the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel at intervals of the first sampling interval time within the first test time to obtain the first solar radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence.

[0109] Exemplarily, turn on the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel at intervals of 60 seconds within 360,000 seconds to obtain the first solar radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence. Among them, the unit of the solar radiation intensity at the location of the photovoltaic panel is watts per square meter, and it is measured by a solar irradiance meter; the unit of the output power of the photovoltaic power generation system is watts, and it is measured by a power meter; the unit of the ambient temperature is degrees Celsius, and it is measured by a thermometer. The lengths of the first solar radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence are all 6,000.

[0110] S3. Aggregate the first solar radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain the second solar radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence, and construct a fitting sample set based on the second solar radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence.

[0111] Exemplarily, the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence are aggregated according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence. The lengths of the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence are all 100. A fitting sample set is constructed according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence, and the number of samples in the sample set is 100.

[0112] S4. According to the fitting sample set, a first function is calculated by using a fitting algorithm. The dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature; the first function is denoted as the photovoltaic system data model.

[0113] Further, the method for aggregating the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and constructing a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence includes:

[0114] According to the first radiation intensity data sequence, a second radiation intensity data sequence is calculated by using a radiation intensity aggregation formula; the radiation intensity aggregation formula is:

[0115] ;

[0116] where represents the -th data in the second radiation intensity data sequence; represents the -th data in the first radiation intensity data sequence; represents the sampling aggregation quantity; The value of is greater than 1; is an integer variable with a value from 1 to is an integer variable with a value from 1 to ; represents the second length; The calculation formula of

[0117] ;

[0118] Among them, represents the first test time, represents the first sampling interval time.

[0119] Exemplarily, seconds, seconds, , so it is calculated that . It is calculated that to . Taking , as an example, The calculation formula of

[0120] is watts per square meter;

[0121] The calculation formula of

[0122] is watts per square meter;

[0123] And so on, it is calculated that to . Combining to obtains the second radiation intensity data sequence.

[0124] According to the first output power data sequence, the second output power data sequence is calculated by using the power aggregation formula; the power aggregation formula is:

[0125] ;

[0126] Among them, represents the th data in the second output power data sequence; represents the th data in the first output power data sequence.

[0127] Exemplarily, taking , as an example, The calculation formula of

[0128] is watts;

[0129] The calculation formula of

[0130] is watts;

[0131] And so on, it is calculated that to . Combining to Combine to obtain the second output power data sequence.

[0132] According to the first ambient temperature data sequence, use the second temperature aggregation formula to calculate the second ambient temperature data sequence; the second temperature aggregation formula is:

[0133] ;

[0134] Where represents the th data in the second ambient temperature data sequence; represents the th data in the first ambient temperature data sequence.

[0135] Exemplarily, taking , as an example, The calculation formula of

[0136] is:

[0137] The calculation formula of

[0138] is:

[0139] And so on to calculate to . Combine to to obtain the second ambient temperature data sequence.

[0140] According to the first ambient temperature data sequence, use the third temperature aggregation formula to calculate the third ambient temperature data sequence; the third temperature aggregation formula is:

[0141] ;

[0142] Where represents the th data in the third ambient temperature data sequence.

[0143] Exemplarily, taking , as an example, The calculation formula of

[0144] is:

[0145] The calculation formula of

[0146] is:

[0147] Calculate and obtain by analogy to . Combine to to obtain the third environmental temperature data sequence.

[0148] Construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second environmental temperature data sequence, and the third environmental temperature data sequence; the fitting sample set is expressed as:

[0149] ;

[0150] wherein, represents the fitting sample set; represents the th sample in the fitting sample set; is expressed as:

[0151] .

[0152] Exemplarily, obtain the fitting sample set:

[0153] .

[0154] Taking , as an example:

[0155] ;

[0156] .

[0157] Furthermore, the method for calculating the first function according to the fitting sample set by using a fitting algorithm, where the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of solar radiation intensity, the predicted average value of environmental temperature, and the predicted dispersion of environmental temperature includes:

[0158] Construct a first fitting equation according to the rated photoelectric conversion efficiency, the area of the photovoltaic panel, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as:

[0159] ;

[0160] wherein, represents the area of the photovoltaic panel, represents the rated photoelectric conversion efficiency, represents the rated voltage of the photovoltaic power generation system, represents the rated resistance of the photovoltaic power generation system, represents a preset reference temperature, Represents the first temperature coefficient of resistance, Represents the second temperature coefficient of resistance, Represents the Residual power.

[0161] Exemplarily, a preset reference temperature in degrees Celsius, the unit of the first temperature coefficient of resistance is ohms per degree Celsius, and the unit of the second temperature coefficient of resistance is ohms.

[0162] Construct a first objective function according to the fitting sample set and the first fitting equation; construct a first constraint condition according to the fitting sample set and the first fitting equation.

[0163] Taking the minimum value of the first objective function as the goal, using the first constraint condition as the constraint, and using the first temperature coefficient of resistance and the second temperature coefficient of resistance as the optimization variables, the optimal values of the first temperature coefficient of resistance and the second temperature coefficient of resistance are calculated by using the particle swarm optimization algorithm, and are respectively denoted as the first optimization coefficient and the second optimization coefficient .

[0164] Exemplarily, the first optimization coefficient and the second optimization coefficient are calculated.

[0165] Calculate the first optimized residual power by using the residual value formula to the optimized residual power ; the residual value formula is:

[0166] ;

[0167] where represents the optimized residual power.

[0168] Exemplarily, taking and as examples, it is calculated that:

[0169] watts;

[0170] watts;

[0171] Using the same method, calculate the first optimized residual power to the 100th optimized residual power .

[0172] According to the first optimization coefficient and the second optimization coefficient and the first optimized residual power to the Optimize the remaining power to obtain a first optimization equation; the first optimization equation is expressed as:

[0173] ;

[0174] wherein, represents the predicted output power of the photovoltaic power generation system, represents the predicted average value of solar radiation intensity obtained in advance, represents the predicted average value of ambient temperature obtained in advance, represents the predicted dispersion of ambient temperature obtained in advance.

[0175] Exemplarily, the first optimization equation characterizes the relationship between the predicted output power of the photovoltaic power generation system, the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature. Substituting the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature into the first optimization equation, the predicted output power of the photovoltaic power generation system can be obtained.

[0176] According to the first optimization equation, solve the functional relationship between the predicted output power of the photovoltaic power generation system, the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature, denoted as the first function; the first function is expressed as:

[0177] .

[0178] Further, the method for constructing the first objective function according to the fitting sample set and the first fitting equation includes:

[0179] Construct the first objective function according to the fitting sample set and the first fitting equation , the first objective function is expressed as:

[0180] .

[0181] Further, the method for constructing the first constraint condition according to the fitting sample set and the first fitting equation includes:

[0182] Construct the first constraint condition according to the fitting sample set and the first fitting equation, and the first constraint condition is expressed as:

[0183] ;

[0184] wherein, represents the upper limit of the remaining fluctuation constraint set in advance.

[0185] Exemplarily, the preset upper limit of the remaining fluctuation constraint is 2.

[0186] Embodiment 2: Based on the same inventive concept, as Figure 2 shown, this embodiment also provides a data model construction system for a high-altitude photovoltaic system, and the system includes:

[0187] A data reading module, configured to obtain first data, where the first data includes a rated photoelectric conversion efficiency, a photovoltaic panel area, a rated resistance of the photovoltaic power generation system, a rated voltage of the photovoltaic power generation system, a preset sampling aggregation quantity, a preset first sampling interval time, and a preset first test time.

[0188] A data measurement module, connected to the data reading module, configured to turn on the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the position where the photovoltaic panel is located at intervals of the first sampling interval time within the first test time, so as to obtain a first radiation intensity data sequence, a first output power data sequence, and a first ambient temperature data sequence.

[0189] A sample construction module, connected to the data measurement module, configured to aggregate the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence.

[0190] A data model construction module, connected to the sample construction module, configured to calculate a first function according to the fitting sample set by using a fitting algorithm, where the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature; and the first function is denoted as the photovoltaic system data model.

[0191] Further, the sample construction module includes:

[0192] A second radiation intensity data sequence construction module, configured to calculate a second radiation intensity data sequence according to the first radiation intensity data sequence by using a radiation intensity aggregation formula; the radiation intensity aggregation formula is:

[0193] ;

[0194] Wherein, represents the th data in the second radiation intensity data sequence; Represents the th data in the first radiation intensity data sequence; Represents the sampling aggregation quantity; The value of is greater than 1; is an integer variable with values from 1 to is an integer variable with values from 1 to ; Represents the second length; The calculation formula of

[0195] ;

[0196] wherein, represents the first test time, represents the first sampling interval time.

[0197] The second output power data sequence construction module, connected to the second radiation intensity data sequence construction module, is used to calculate the second output power data sequence according to the first output power data sequence by using the power aggregation formula; the power aggregation formula is:

[0198] ;

[0199] wherein, represents the th data in the second output power data sequence; represents the th data in the first output power data sequence.

[0200] The second ambient temperature data sequence construction module, connected to the second output power data sequence construction module, is used to calculate the second ambient temperature data sequence according to the first ambient temperature data sequence by using the second temperature aggregation formula; the second temperature aggregation formula is:

[0201] ;

[0202] wherein, represents the th data in the second ambient temperature data sequence; represents the th data in the first ambient temperature data sequence.

[0203] The third ambient temperature data sequence construction module, connected to the second ambient temperature data sequence construction module, is used to calculate the third ambient temperature data sequence according to the first ambient temperature data sequence by using the third temperature aggregation formula; the third temperature aggregation formula is:

[0204] ;

[0205] Among them, represents the th data in the third ambient temperature data sequence.

[0206] The fitting sample set generation module is connected to the third ambient temperature data sequence construction module, and is used to construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; the fitting sample set is expressed as:

[0207] ;

[0208] Among them, represents the fitting sample set; represents the th sample in the fitting sample set; is expressed as:

[0209] .

[0210] Furthermore, the data model construction module includes:

[0211] The first fitting equation construction module is used to construct a first fitting equation according to the rated photoelectric conversion efficiency, the area of the photovoltaic panel, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as:

[0212] ;

[0213] Among them, represents the area of the photovoltaic panel, represents the rated photoelectric conversion efficiency, represents the rated voltage of the photovoltaic power generation system, represents the rated resistance of the photovoltaic power generation system, represents the preset reference temperature, represents the first resistance temperature coefficient, represents the second resistance temperature coefficient, represents the remaining power.

[0214] The optimization model construction module is connected to the first fitting equation construction module, and is used to construct a first objective function according to the fitting sample set and the first fitting equation; construct a first constraint condition according to the fitting sample set and the first fitting equation.

[0215] Optimization calculation module, connected to the optimization model construction module, is used to aim at minimizing the value of the first objective function, taking the first constraint condition as the constraint, using the first resistance temperature coefficient and the second resistance temperature coefficient as optimization variables, and adopting the particle swarm optimization algorithm to calculate the optimal values of the first resistance temperature coefficient and the second resistance temperature coefficient, which are respectively denoted as the first optimization coefficient , the second optimization coefficient .

[0216] Optimization residual power calculation module, connected to the optimization calculation module, is used to calculate the first optimized residual power by using the residual value formula to the optimized residual power ; The residual value formula is:

[0217] ;

[0218] wherein, represents the optimized residual power

[0219] First optimization equation construction module, connected to the optimization residual power calculation module, is used to obtain the first optimization equation according to the first optimization coefficient , the second optimization coefficient , the first optimized residual power to the optimized residual power ; The first optimization equation is expressed as:

[0220] ;

[0221] wherein, represents the predicted output power of the photovoltaic power generation system obtained in advance, represents the predicted average value of solar radiation intensity obtained in advance, represents the predicted average value of ambient temperature obtained in advance, represents the predicted dispersion of ambient temperature obtained in advance

[0222] First function construction module, connected to the first optimization equation construction module, is used to solve the functional relationship between the predicted output power of the photovoltaic power generation system, the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted dispersion of ambient temperature according to the first optimization equation, and is denoted as the first function; The first function is expressed as:

[0223] .

[0224] Furthermore, the optimization model construction module includes:

[0225] The first objective function construction module is used to construct a first objective function according to the fitting sample set and the first fitting equation , and the first objective function is expressed as:

[0226] .

[0227] Furthermore, the optimization model construction module further includes:

[0228] The first constraint condition construction module is used to construct a first constraint condition according to the fitting sample set and the first fitting equation, and the first constraint condition is expressed as:

[0229] ;

[0230] wherein, represents the upper limit of the remaining fluctuation constraint set in advance.

[0231] It should be noted that regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0232] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a data model for a high altitude photovoltaic system, characterized in that: The method comprises the following steps: S1, obtaining first data, wherein the first data includes rated photoelectric conversion efficiency, photovoltaic panel area, rated resistance of photovoltaic power generation system, rated voltage of photovoltaic power generation system, a preset sampling aggregation number, a preset first sampling interval time, and a preset first test time; S2, start the photovoltaic power generation system, and measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel within a first test time and at a first sampling interval, to obtain a first radiation intensity data sequence, a first output power data sequence, and a first ambient temperature data sequence; S3, aggregating the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and constructing a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; S4. According to the fitting sample set, a fitting algorithm is used to calculate a first function, wherein the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted discreteness of ambient temperature; the first function is recorded as a photovoltaic system data model.

2. A method for constructing a data model for a high altitude photovoltaic system according to claim 1, characterized in that: The method of aggregating the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence, and constructing a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence comprises: According to the first radiation intensity data sequence, the second radiation intensity data sequence is calculated using a radiation intensity aggregation formula; the radiation intensity aggregation formula is: ； in, Indicates the first individual data; Indicates the first radiation intensity data sequence individual data; Indicates the number of sampling aggregations; The value of is greater than 1; The value range is 1 to integer variable of ; The value range is 1 to integer variable of ; Indicates the second length; The calculation formula is: ； in, Indicates the first test time, Indicates the first sampling interval time; According to the first output power data sequence, a second output power data sequence is calculated using a power aggregation formula; the power aggregation formula is: ； in, Indicates the first individual data; Indicates the first output power data sequence individual data; According to the first ambient temperature data sequence, a second ambient temperature data sequence is calculated using a second temperature aggregation formula; the second temperature aggregation formula is: ； in, Indicates the first individual data; Indicates the first ambient temperature data sequence individual data; According to the first ambient temperature data sequence, a third ambient temperature data sequence is calculated using a third temperature aggregation formula; the third temperature aggregation formula is: ； in, Indicates the third ambient temperature data sequence individual data; A fitting sample set is constructed according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; the fitting sample set is expressed as: ； in, represents the fitting sample set; represents the first samples; It is expressed as: 。 3. A method for constructing a data model for a high altitude photovoltaic system according to claim 2, characterized in that: The method of calculating a first function using a fitting algorithm according to the fitting sample set, wherein the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of solar radiation intensity, the predicted average value of ambient temperature, and the predicted discreteness of ambient temperature includes: The first fitting equation is constructed according to the rated photoelectric conversion efficiency, the photovoltaic panel area, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as: ； in, represents the photovoltaic panel area, Indicates the rated photoelectric conversion efficiency, Indicates the rated voltage of the photovoltaic power generation system. Indicates the rated resistance of the photovoltaic power generation system. Indicates the preset reference temperature. represents the first resistance temperature coefficient, represents the second resistance temperature coefficient, Indicates Residual power; Constructing a first objective function according to the fitting sample set and the first fitting equation; constructing a first constraint condition according to the fitting sample set and the first fitting equation; Taking the minimum value of the first objective function as the goal, the first constraint condition as the constraint, the first resistance temperature coefficient and the second resistance temperature coefficient as the optimization variables, the particle swarm optimization algorithm is used to calculate the optimal value of the first resistance temperature coefficient and the optimal value of the second resistance temperature coefficient, which are respectively recorded as the first optimization coefficients , the second optimization coefficient ; The first optimized residual power is calculated using the residual value formula To Optimizing remaining power ; The residual value formula is: ； in, Indicates Optimize remaining power; According to the first optimization coefficient , the second optimization coefficient , First optimize the remaining power To Optimizing remaining power The first optimization equation is obtained; the first optimization equation is expressed as: ； in, represents the predicted output power of the photovoltaic power generation system, represents the average value of solar radiation intensity predicted in advance, represents the predicted average ambient temperature obtained in advance, Indicates the predicted discreteness of the ambient temperature obtained in advance; According to the first optimization equation, the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature is obtained, which is recorded as the first function; the first function is expressed as: 。 4. A method for constructing a data model for a high altitude photovoltaic system according to claim 3, characterized in that: The method for constructing a first objective function according to the fitting sample set and the first fitting equation includes: According to the fitting sample set and the first fitting equation, a first objective function is constructed. , the first objective function is expressed as: 。 5. A method for constructing a data model for a high altitude photovoltaic system according to claim 4, characterized in that: The method for constructing the first constraint condition according to the fitting sample set and the first fitting equation includes: According to the fitting sample set and the first fitting equation, a first constraint condition is constructed, and the first constraint condition is expressed as: ； in, Represents the pre-set upper limit of the residual volatility constraint.

6. A data model building system for a high altitude photovoltaic system, characterized in that: The system comprises: A data reading module, used to obtain first data, wherein the first data includes a rated photoelectric conversion efficiency, a photovoltaic panel area, a rated resistance of a photovoltaic power generation system, a rated voltage of a photovoltaic power generation system, a preset sampling aggregation number, a preset first sampling interval time, and a preset first test time; The data measurement module is connected to the data reading module and is used to start the photovoltaic power generation system, measure the solar radiation intensity, the output power of the photovoltaic power generation system, and the ambient temperature at the location of the photovoltaic panel at a first sampling interval within a first test time, and obtain a first radiation intensity data sequence, a first output power data sequence, and a first ambient temperature data sequence; a sample construction module connected to the data measurement module, and configured to aggregate the first radiation intensity data sequence, the first output power data sequence, and the first ambient temperature data sequence according to the sampling aggregation quantity to obtain a second radiation intensity data sequence, a second output power data sequence, a second ambient temperature data sequence, and a third ambient temperature data sequence, and to construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; The data model construction module is connected to the sample construction module, and is used to calculate the first function according to the fitting sample set using a fitting algorithm, wherein the dependent variable of the first function is the predicted output power of the photovoltaic power generation system, and the independent variables of the first function are the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted discreteness of the ambient temperature; the first function is recorded as the photovoltaic system data model.

7. A data model building system for a high altitude photovoltaic system as claimed in claim 6, characterized in that: The sample building blocks include: The second radiation intensity data sequence construction module is used to calculate the second radiation intensity data sequence using the radiation intensity aggregation formula according to the first radiation intensity data sequence; the radiation intensity aggregation formula is: ； in, Indicates the first individual data; Indicates the first radiation intensity data sequence individual data; Indicates the number of sampling aggregations; The value of is greater than 1; The value range is 1 to integer variable of ; The value range is 1 to integer variable of ; Indicates the second length; The calculation formula is: ； in, Indicates the first test time, Indicates the first sampling interval time; The second output power data sequence construction module is connected to the second radiation intensity data sequence construction module and is used to calculate the second output power data sequence using a power aggregation formula according to the first output power data sequence; the power aggregation formula is: ； in, Indicates the first individual data; Indicates the first output power data sequence individual data; The second ambient temperature data sequence construction module is connected to the second output power data sequence construction module and is used to calculate the second ambient temperature data sequence using the second temperature aggregation formula according to the first ambient temperature data sequence; the second temperature aggregation formula is: ； in, Indicates the first individual data; Indicates the first ambient temperature data sequence individual data; The third ambient temperature data sequence construction module is connected to the second ambient temperature data sequence construction module and is used to calculate the third ambient temperature data sequence using the third temperature aggregation formula according to the first ambient temperature data sequence; the third temperature aggregation formula is: ； in, Indicates the third ambient temperature data sequence individual data; The fitting sample set generation module is connected to the third ambient temperature data sequence construction module, and is used to construct a fitting sample set according to the second radiation intensity data sequence, the second output power data sequence, the second ambient temperature data sequence, and the third ambient temperature data sequence; the fitting sample set is expressed as: ； in, represents the fitting sample set; represents the first samples; It is expressed as: 。 8. A data model building system for a high altitude photovoltaic system as claimed in claim 7, characterized in that: The data model building module includes: The first fitting equation construction module is used to construct a first fitting equation according to the rated photoelectric conversion efficiency, the photovoltaic panel area, and the rated resistance of the photovoltaic power generation system; the first fitting equation is expressed as: ； in, represents the photovoltaic panel area, Indicates the rated photoelectric conversion efficiency, Indicates the rated voltage of the photovoltaic power generation system. Indicates the rated resistance of the photovoltaic power generation system. Indicates the preset reference temperature. represents the first resistance temperature coefficient, represents the second resistance temperature coefficient, Indicates Residual power; An optimization model construction module is connected to the first fitting equation construction module, and is used to construct a first objective function according to the fitting sample set and the first fitting equation; and to construct a first constraint condition according to the fitting sample set and the first fitting equation; The optimization calculation module is connected to the optimization model building module, and is used to take the minimum value of the first objective function as the goal, the first constraint condition as the constraint, the first resistance temperature coefficient and the second resistance temperature coefficient as the optimization variables, and use the particle swarm optimization algorithm to calculate the optimal value of the first resistance temperature coefficient and the optimal value of the second resistance temperature coefficient, which are respectively recorded as the first optimization coefficients , the second optimization coefficient ; The optimized residual power calculation module is connected to the optimized calculation module and is used to calculate the first optimized residual power using the residual value formula To Optimizing remaining power ; The residual value formula is: ； in, Indicates Optimize remaining power; The first optimization equation building module is connected to the optimization residual power calculation module and is used to calculate the residual power according to the first optimization coefficient. , the second optimization coefficient , First optimize the remaining power To Optimizing remaining power The first optimization equation is obtained; the first optimization equation is expressed as: ； in, represents the predicted output power of the photovoltaic power generation system obtained in advance, represents the average value of solar radiation intensity predicted in advance, represents the predicted average ambient temperature obtained in advance, Indicates the predicted discreteness of the ambient temperature obtained in advance; The first function construction module is connected to the first optimization equation construction module, and is used to solve the functional relationship between the predicted output power of the photovoltaic power generation system and the predicted average value of the solar radiation intensity, the predicted average value of the ambient temperature, and the predicted dispersion of the ambient temperature according to the first optimization equation, which is recorded as the first function; the first function is expressed as: 。 9. A data model building system for a high altitude photovoltaic system as claimed in claim 8, characterized in that: The optimization model building module includes: The first objective function construction module is used to construct the first objective function according to the fitting sample set and the first fitting equation. , the first objective function is expressed as: 。 10. A data model building system for a high altitude photovoltaic system according to claim 9, characterized in that: The optimization model building module also includes: The first constraint condition construction module is used to construct a first constraint condition according to the fitting sample set and the first fitting equation. The first constraint condition is expressed as: ； in, Represents the pre-set upper limit of the residual volatility constraint.

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