CN111814399B - Model parameter optimization extraction method and measurement data prediction method of solar photovoltaic cell system - Google Patents
Model parameter optimization extraction method and measurement data prediction method of solar photovoltaic cell system Download PDFInfo
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Abstract
The invention discloses a model parameter optimization extraction method and a measurement data prediction method of a solar photovoltaic cell system, which comprise the following steps: measuring an actual photovoltaic module and collecting data; establishing a model of a photovoltaic cell system, and determining an objective function of an optimization problem; extracting parameters of a photovoltaic module model by utilizing a particle swarm search strategy and a random reselection mechanism; and obtaining calculation data according to the newly constructed solar photovoltaic cell system model, analyzing and comparing the calculation data with the measurement data, and finally evaluating the optimization efficiency of the algorithm and the precision of the model. By implementing the method, the precision of the photovoltaic model parameters is improved, and the model with the actual measurement numerical value prediction capability is obtained, so that the method is further used for predicting the actual measurement data.
Description
Technical Field
The invention relates to a detection technology of a solar cell and a photovoltaic power generation array, in particular to a model parameter extraction method of a solar photovoltaic cell system with a particle swarm search strategy and a random reselection mechanism.
Background
The frequent occurrence of extreme weather in recent years has indicated that the earth's environment is in the process of continually deteriorating. With the continued understanding of pollution sources, it was found that excessive use of fossil energy by humans was an important cause of this situation. The conversion of solar energy into electrical energy via a photovoltaic cell system is one of the important means to solve pollution and energy problems. Since the characteristics of the photovoltaic cell system deeply influence the efficiency of converting solar energy into electric energy, the research of an accurate mathematical model becomes a hot spot. Currently, the current-voltage characteristics of photovoltaic cell systems are most widely described using single-diode and dual-diode models. Both models contain parameters that cannot be determined by simple mathematical operations, and the determination of parameters has an important impact on the model.
To solve the parameter identification problem of photovoltaic cell systems, many researchers have proposed various methods from different angles: extracting 5 parameters of a single diode by adopting a least square optimization algorithm based on a modified Newton model; the single diode lumped circuit model is extracted by a 5-point method, which is more reliable and more accurate than a curve fitting method; processing a super-implicit equation of the photovoltaic model by using a Lambert W function; and extracting the performance of the single diode parameters by adopting a gradient descent method.
The successful application of the meta-heuristic algorithm in other fields provides another thought for solving the parameter identification problem of the photovoltaic cell system, and the thought comprises a genetic algorithm, a particle swarm algorithm and the like. The particle swarm algorithm and the cuckoo algorithm have good performance in extracting parameters of a solar photovoltaic model, but the defects of the two algorithms are gradually exposed along with the continuous deep research. Particle swarm algorithms are prone to the disadvantages of early ripening and stagnation, and the cuckoo algorithm has the problem of slower convergence speed.
Currently, a solar photovoltaic model parameter extraction method based on a particle swarm search strategy and a random reselection mechanism is not found in published literature and patents.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings in the prior art and provide a solar photovoltaic model parameter extraction method, which is based on a particle swarm search strategy and a random reselection mechanism, improves the precision of photovoltaic model parameters, and can accurately predict measurement data of a solar cell system under specific actual measurement conditions according to a model of optimized parameters, so that the operation management of the solar cell system can be improved based on the data, and the operation cost is reduced.
In order to achieve the above purpose, the technical scheme of the invention comprises the following steps:
step S1: under specific actual measurement conditions, measuring the solar photovoltaic cellThe system obtains measurement data including output current I L Output voltage V L ；
Step S2: establishing a solar photovoltaic cell system model, wherein the model comprises a single diode current-voltage characteristic equation and a double diode current-voltage characteristic equation, and determining an objective function of an optimization problem;
step S3: extracting parameters of the solar photovoltaic cell system model by using a particle swarm search strategy and a random reselection mechanism to obtain an optimized solar photovoltaic cell system model;
step S4: and (3) obtaining model data under specific actual measurement conditions according to the optimized solar photovoltaic cell system model, and analyzing and comparing the model data with the measurement data in the step (S1) to obtain the solar photovoltaic cell system model with accurate verification.
Further, the step S2 specifically includes:
step S2.1: establishing a mathematical model of a single diode, wherein a specific formula is shown in (1);
wherein I is L To output current, I ph For generating current by light, I sd Is reverse saturated current, q is charge constant, V L To output voltage, R S Is a series resistance, n is an ideal factor, k is a Boltzmann constant, T is a Kelvin temperature, R sh For shunt resistance, the single diode model was found to contain 5 unknown variables: i ph ，I sd ，R S ，R sh ，n；
Step S2.2: providing an objective function of a single diode, wherein specific formulas are shown as (2) and (3);
step S2.3: establishing a mathematical model of the double diodes, wherein a specific formula is shown in (4);
wherein I is sd1 And I sd2 Represents diffusion current and saturation current, respectively, n 1 And n 2 Representing different idealities, other parameters were consistent with the single diode mathematical model, yielding a dual diode model containing 7 parameters: i ph ，I sd1 ，I sd2 ，R S ，R sh ，n 1 ，n 2 ；
Step S2.4: providing a target function of the double diodes, wherein specific formulas are shown as (5) and (6);
further, the step S3 specifically includes:
step S3.1: defining initial parameter values: maximum iteration number T, number N of particle groups, dimension dim, upper limit ub of value range, lower limit lb of value range, group velocity v, inertial weight w, individual factor r 1 Social factor r 2 Random reselection probability p a ；
Step S3.2: randomly generating initial particle swarm positions in a definition domain by adopting a mode of a formula (7);
X N，dim ＝lb+(ub-lb)×rand(N，dim)； (7)
wherein rand is a random number subject to uniform distribution between [0,1 ];
step S3.3: calculate and save the current bestBest candidate solution p best Global best candidate solution g best ；
Step S3.4: iteratively searching and updating the particle swarm position, and calculating to obtain a new population position by using formulas (8) and (9);
X N，dim ＝X N，dim +v N，dim ； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.6: randomly reselecting the individual and generating a new position by adopting the modes of formulas (10), (11) and (12);
K＝rand(sizepop，dim)＞p a (10)
stepsize＝rand×（X randpermutation，dim -X randpermutation，dim )； (11)
step S3.7: calculating the fitness value of a new individual regenerated at present randomly, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.8: judging global best candidate solution g best If the change occurs, replacing and updating if a better solution is generated, otherwise, continuing to keep;
step S3.9: and judging whether the maximum iteration times T are reached, if so, outputting the particle swarm position, namely the diode model parameter, and otherwise, turning to the step S3.4.
In addition, the method for predicting the measurement data of the solar photovoltaic cell system under the actual measurement condition based on the solar photovoltaic cell system model is used for predicting the measurement data of the solar photovoltaic cell system under the specific actual measurement condition according to the solar photovoltaic cell system model with accurate verification obtained by the parameter optimization extraction method.
The embodiment of the invention has the following beneficial effects:
according to the solar photovoltaic model parameter extraction method and the solar photovoltaic model parameter prediction method based on the particle swarm search strategy and the random reselection mechanism, the search capability of an original particle swarm algorithm is improved, the situation that a local optimal solution is trapped is avoided, and the capability of improving the accuracy of identifying the model parameters of the solar photovoltaic cell system is further achieved. In addition, the model optimized according to the parameters can be used for predicting strategy data under specific actual measurement conditions, so that the frequency of real-time detection during operation management of the solar photovoltaic cell system is reduced, the operation efficiency is improved, and the cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
FIG. 1 is a flow chart of the present invention.
FIG. 2 is an equivalent circuit diagram of a single diode model;
FIG. 3 is an equivalent circuit diagram of a dual diode model;
FIG. 4 is a flow chart of an algorithm;
FIG. 5 is a graph of a relative error fit of current and power for a single diode model;
FIG. 6 is a graph of a relative error fit of current and power for a dual diode model;
FIG. 7 is a graph of a fit between calculated and measured data for a single diode model of current-voltage, voltage-power;
FIG. 8 is a graph of a fit between calculated and measured data for a current-voltage, voltage-power dual diode model;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, in an embodiment of the present invention, a method for extracting parameters of a solar photovoltaic model based on a particle swarm search strategy and a random reselection mechanism is provided, including the following steps:
step S1: measuring an actual photovoltaic module and collecting data;
for example: at a temperature of 33 ℃ and an illumination intensity of 1000W/m 2 Data on a block of RTC France photovoltaic cells of 57mm diameter were measured.
Step S2: constructing a single diode model and a double diode model as mathematical models of the photovoltaic module, and converting the numerical problem into an optimization problem;
step S2.1: FIG. 2 is an equivalent circuit of a single diode model, and a specific formula is shown in (1);
wherein I is L To output current, I ph For generating current by light, I sd Is reverse saturated current, q is charge constant, V L To output voltage, R S Is a series resistance, n is an ideal factor, k is a Boltzmann constant, T is a Kelvin temperature, R sh For shunt resistance, the single diode model was found to contain 5 unknown variables: (I) ph ，I sd ，R S ，R sh ，n)；
Step S2.2: providing an objective function of a single diode, wherein specific formulas are shown as (2) and (3);
step S2.3: FIG. 3 is an equivalent circuit of a dual diode model, and the specific formula is shown as (4);
wherein I is sd1 And I sd2 Represents diffusion current and saturation current, respectively, n 1 And n 2 Representing different idealities, other parameters were consistent with a single diode, yielding a two-diode model containing 7 parameters: (I) ph ，I sd1 ，I sd2 ，R S ，R sh ，n 1 ，n 2 )；
Step S2.4: providing a target function of the double diodes, wherein specific formulas are shown as (5) and (6);
step S3: as shown in fig. 4, parameters of the photovoltaic module model are extracted by using a particle swarm search strategy and a random reselection mechanism;
step S3.1: defining initial parameter values: the maximum iteration number T is 20000, the number N of particle groups is 30, the value range of the single diode model is shown in table 1, the value range of the double diode model is shown in table 2, the initial group velocity v=0, the inertia weight w=0.9-0.7T/T, the individual factor r 1 =2, social factor r 2 =2, random reselection probability p a ＝0.25；
TABLE 1
TABLE 2
Step S3.2: randomly generating initial particle swarm positions in a definition domain by adopting a mode of a formula (7);
X N，dim ＝lb+(ub-lb)×rand(N，dim)； (7)
wherein rand is a random number subject to uniform distribution between [0,1 ];
step S3.3: calculating and saving the current best candidate solution p best Global best candidate solution g best ；
Step S3.4: iteratively searching and updating the particle swarm position, and calculating to obtain a new population position by using formulas (8) and (9);
X N，dim ＝X N，dim +v N，dim ； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.6: randomly reselecting the individual and generating a new position by adopting the modes of formulas (10), (11) and (12);
K＝rand(sizepop，dim)＞p a (10)
stepsize＝rand×(Xr andpermutation，dim -X randpermutation，dim ) (11)
step S3.7: calculating the fitness value of a new individual regenerated at present randomly, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.8: judging global best candidate solution g best If the change occurs, replacing and updating if a better solution is generated, otherwise, continuing to keep;
step S3.9: judging whether the maximum iteration times T are reached, if so, outputting the particle swarm position, namely the diode model parameter, otherwise, turning to the step S3.4;
step S4: solar photovoltaic cell system models constructed from the parameters optimized for extraction in table 3 (single diode model parameters) and table 4 (double diode model parameters). And obtaining calculation data according to the model, analyzing and comparing the calculation data with the measurement data, and finally evaluating the optimization efficiency of the algorithm and the precision of the optimization model. Fig. 5 is a graph of the relative error fit of current and power for a single diode model, and fig. 6 is a graph of the relative error fit of current and power for a dual diode model.
TABLE 3 Table 3
TABLE 4 Table 4
And predicting measurement data of the solar photovoltaic cell system under specific actual measurement conditions according to the verified solar photovoltaic cell system model obtained by the parameter optimization extraction method. Fig. 7 is a fitted curve between the calculated data of the current-voltage, voltage-power single diode model and the measured data of the RTC France photovoltaic cell. Fig. 8 is a fitted curve between the calculated data of the current-voltage, voltage-power dual diode model and the measured data of the RTC France photovoltaic cell.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.
It should be noted that embodiments of the present invention may be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.
Claims (3)
1. The solar photovoltaic cell system model parameter optimization extraction method is characterized by comprising the following steps of:
step S1: under specific actual measurement conditions, measuring a solar photovoltaic cell system to obtain measurement data, wherein the measurement data comprises an output current I L Output voltage V L ；
Step S2: establishing a solar photovoltaic cell system model, wherein the model comprises a single diode current-voltage characteristic equation and a double diode current-voltage characteristic equation, and determining an objective function of an optimization problem;
step S3: extracting parameters of the solar photovoltaic cell system model by using a particle swarm search strategy and a random reselection mechanism to obtain an optimized solar photovoltaic cell system model;
step S4: obtaining model data under specific actual measurement conditions according to the optimized solar photovoltaic cell system model, and analyzing and comparing the model data with the measurement data in the step S1 to obtain a solar photovoltaic cell system model with accurate verification;
the step S2 specifically includes:
step S2.1: establishing a mathematical model of a single diode, wherein a specific formula is shown in (1);
wherein I is L To output current, I ph For generating current by light, I sd Is reverse saturated current, q is charge constant, V L To output voltage, R S Is a series resistance, n is an ideal factor, k is a Boltzmann constant, T is a Kelvin temperature, R sh For shunt resistance, the single diode model was found to contain 5 unknown variables: i ph ，I sd ，R S ，R sh ，n；
Step S2.2: providing an objective function of a single diode, wherein specific formulas are shown as (2) and (3);
step S2.3: establishing a mathematical model of the double diodes, wherein a specific formula is shown in (4);
wherein I is sd1 And I sd2 Represents diffusion current and saturation current, respectively, n 1 And n 2 Representing different idealities, other parameters were consistent with the single diode mathematical model, yielding a dual diode model containing 7 parameters: i ph ，I sd1 ，I sd2 ，R S ，R sh ，n 1 ，n 2 ；
Step S2.4: providing a target function of the double diodes, wherein specific formulas are shown as (5) and (6);
2. the method for optimizing and extracting parameters of a solar photovoltaic cell system model according to claim 1, wherein the step S3 specifically comprises:
step S3.1: defining initial parameter values: maximum iteration number T, number N of particle groups, dimension dim, upper limit ub of value range, lower limit lb of value range, group velocity v, inertial weight w, individual factor r 1 Social factor r 2 Random reselection probability p a ；
Step S3.2: randomly generating initial particle swarm positions in a definition domain by adopting a mode of a formula (7);
X N，dim ＝lb+(ub-lb)×rand(N，dim)； (7)
wherein rand is a random number subject to uniform distribution between [0,1 ];
step S3.3: calculating and saving the current best candidate solution p best Global best candidate solution g best ；
Step S3.4: iteratively searching and updating the particle swarm position, and calculating to obtain a new population position by using formulas (8) and (9);
X N，dim ＝X N，dim +v N，dim ； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.6: randomly reselecting the individual and generating a new position by adopting the modes of formulas (10), (11) and (12);
K＝rand(sizepop，dim)>p a (10)
stepsize＝rand×(X randpermutation，dim -X randpermutaion，dim )； (11)
step S3.7: calculating the fitness value of a new individual regenerated at present randomly, and judging whether the fitness value is better than the current best candidate solution p best If the updating is more optimal, replacing the updating, otherwise, continuing to keep;
step S3.8: judging global best candidate solution g best If the change occurs, replacing and updating if a better solution is generated, otherwise, continuing to keep;
step S3.9: and judging whether the maximum iteration times T are reached, if so, outputting the particle swarm position, namely the diode model parameter, and otherwise, turning to the step S3.4.
3. A method for predicting measurement data of a solar photovoltaic cell system under actual measurement conditions based on a solar photovoltaic cell system model is characterized by comprising the following steps of: the solar photovoltaic cell system model with accurate verification obtained by the parameter optimization extraction method according to claim 1 is used for predicting the measurement data of the solar photovoltaic cell system under specific actual measurement conditions.
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