CN111814399A - Model parameter optimization extraction method and measurement data prediction method for solar photovoltaic cell system - Google Patents
Model parameter optimization extraction method and measurement data prediction method for 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 for 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 the 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 accuracy of the parameters of the photovoltaic model is improved, and the model with the actual measurement value prediction capability is obtained and further used for predicting 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 method for extracting model parameters of a solar photovoltaic cell system based on a particle swarm search strategy and a random reselection mechanism.
Background
The frequent occurrence of extreme weather in recent years indicates that the earth's environment is in the process of ever-deteriorating. With the continuous understanding of the pollution source, it is found that the overuse of fossil energy by human beings is an important reason for this situation. The conversion of solar energy into electrical energy through photovoltaic cell systems is one of the important means to solve the pollution and energy problems. Since the characteristics of the photovoltaic cell system deeply affect the efficiency of converting solar energy into electric energy, it has become a hot point to develop an accurate mathematical model. At present, single-diode and double-diode models are most widely used to describe the current-voltage characteristics of photovoltaic cell systems. Both models contain parameters that cannot be determined by simple mathematical operations, and the determination of the parameters has a significant impact on the model.
To solve the problem of parameter identification of photovoltaic cell systems, many researchers have proposed a variety of methods from different perspectives: extracting 5 parameters of the single diode by adopting a least square optimization algorithm based on a modified Newton model; the method for extracting the single diode lumped circuit model by adopting the 5-point method is more reliable and accurate than a curve fitting method; processing a super-implicit equation of the photovoltaic model by adopting a Lambert W function; and extracting the performance of the single diode parameters by adopting a gradient descent method, and the like.
The successful application of the meta-heuristic algorithm in other fields provides another idea for solving the problem of parameter identification of the photovoltaic cell system, and the idea 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 the solar photovoltaic model, but the defects of the two algorithms are gradually exposed along with the continuous deep research. The particle swarm algorithm is easy to have the defects of precocity and stagnation, and the cuckoo algorithm has the problem of low convergence speed.
At present, no solar photovoltaic model parameter extraction method based on a particle swarm search strategy and a random reselection mechanism is found in published documents and patents.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides 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 the measurement data of a solar cell system under specific actual measurement conditions according to a model with 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 purpose, the technical scheme of the invention comprises the following steps:
step S1: under the specific actual measurement condition, measuring the solar photovoltaic cell system to obtain measurement data, wherein the measurement data comprises output current ILAn output voltage VL；
Step S2: establishing a solar photovoltaic cell system model which comprises a current-voltage characteristic equation of a single diode and a current-voltage characteristic equation of a double diode, and determining an objective function of an optimization problem;
step S3: extracting parameters of the solar photovoltaic cell system model by utilizing a particle swarm search strategy and a random reselection mechanism to obtain an optimized solar photovoltaic cell system model;
step S4: and 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 setting that the step S2 specifically includes:
step S2.1: establishing a mathematical model of the single diode, wherein a specific formula is shown as (1);
wherein, ILTo output current, IphIs a photo-generated current, IsdIs reverse saturation currentQ is a charge constant, VLTo output a voltage, RSIs a series resistor, n is an ideal factor, k is the Boltzmann constant, T is the Kelvin temperature, R is the resistanceshFor shunt resistance, the resulting single diode model contains 5 unknown variables: i isph，Isd，RS，Rsh，n；
Step S2.2: an objective function of a single diode is provided, and the 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 as (4);
wherein, Isd1And Isd2Respectively representing diffusion current and saturation current, n1And n2Representing different ideality factors, the other parameters are consistent with the single-diode mathematical model, and the obtained double-diode model comprises 7 parameters: i isph，Isd1，Isd2，RS，Rsh，n1，n2；
Step S2.4: an objective function of the double diodes is provided, and the specific formulas are shown as (5) and (6);
further setting that the step S3 specifically includes:
step S3.1: defining initial parameter values: maximum number of iterationsThe number T, the number N of particle swarms, the dimension dim, an upper limit ub of a numeric range, a lower limit lb of the numeric range, a group velocity v, an inertia weight w and an individual factor r1Social factor r2Probability of random reselection pa；
Step S3.2: randomly generating an initial particle swarm position in a defined domain by adopting a formula (7);
XN，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 pbestAnd global best candidate solution gbest；
Step S3.4: iteratively searching and updating the particle swarm positions, and calculating by using formulas (8) and (9) to obtain new swarm positions;
XN，dim＝XN，dim+vN，dim； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.6: randomly reselecting an individual and generating a new position by adopting the formulas (10), (11) and (12);
K＝rand(sizepop，dim)＞pa(10)
stepsize＝rand×（Xrandpermutation，dim-Xrandpermutation，dim)； (11)
step S3.7: calculating the fitness value of the current randomly regenerated new individual, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.8: determining a global best candidate solution gbestIf the change is generated, replacing and updating if a better solution is generated, otherwise, continuously keeping;
step S3.9: and judging whether the maximum iteration number T is reached, if so, outputting the position of the particle swarm, namely the parameters of the diode model, 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 and extraction method.
The embodiment of the invention has the following beneficial effects:
the solar photovoltaic model parameter extraction method and the prediction method based on the particle swarm search strategy and the random reselection mechanism improve the search capability of the original particle swarm algorithm, avoid falling into the local optimal solution, and further improve the capability of identifying the parameter precision of the solar photovoltaic cell system. In addition, the model optimized according to the parameters can be used for predicting strategy data under specific actual measurement conditions, the frequency of real-time detection during operation management of the solar photovoltaic battery system is reduced, the operation efficiency is improved, and the cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
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 two-diode model;
FIG. 4 is a flow chart of an algorithm;
FIG. 5 is a relative error fit curve of current and power for a single diode model;
FIG. 6 is a relative error fit curve of current and power for a two-diode model;
FIG. 7 is a plot of the fit between calculated and measured data for a current-voltage, voltage-power single diode model;
FIG. 8 is a fitted curve between calculated and measured data for a current-voltage, voltage-power two-diode model;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, a solar photovoltaic model parameter extraction method 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/m2The data are measured on a block of rtcfrange photovoltaic cell with a diameter of 57 mm.
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 the specific formula is shown in (1);
wherein, ILTo output current, IphIs a photo-generated current, IsdFor reverse saturation current, q is the charge constant, VLTo output a voltage, RSIs a series resistor, n is an ideal factor, k is the Boltzmann constant, T is the Kelvin temperature, R is the resistanceshFor shunt resistance, a single diode model is obtained containing 5The unknown variables: (I)ph，Isd，RS，Rsh，n)；
Step S2.2: an objective function of a single diode is provided, and the specific formulas are shown as (2) and (3);
step S2.3: fig. 3 is an equivalent circuit of a two-diode model, and the specific formula is shown in (4);
wherein, Isd1And Isd2Respectively representing diffusion current and saturation current, n1And n2Representing different ideality factors, the other parameters are consistent with those of a single diode, and the obtained double-diode model comprises 7 parameters: (I)ph，Isd1，Isd2，RS，Rsh，n1，n2)；
Step S2.4: an objective function of the double diodes is provided, and the 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 particle swarm number N is 30, the value ranges of the single-diode model are shown in table 1, the value ranges of the double-diode model are shown in table 2, and the initial iteration is carried outGroup velocity v is 0, inertial weight w is 0.9-0.7T/T, individual factor r 12, social factor r 22, probability of random reselection pa ＝0.25；
TABLE 1
TABLE 2
Step S3.2: randomly generating an initial particle swarm position in a defined domain by adopting a formula (7);
XN，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 pbestAnd global best candidate solution gbest；
Step S3.4: iteratively searching and updating the particle swarm positions, and calculating by using formulas (8) and (9) to obtain new swarm positions;
XN，dim＝XN，dim+vN，dim； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.6: randomly reselecting an individual and generating a new position by adopting the formulas (10), (11) and (12);
K＝rand(sizepop，dim)＞pa(10)
stepsize＝rand×(Xrandpermutation，dim-Xrandpermutation，dim) (11)
step S3.7: calculating the fitness value of the current randomly regenerated new individual, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.8: determining a global best candidate solution gbestIf the change is generated, replacing and updating if a better solution is generated, otherwise, continuously keeping;
step S3.9: judging whether the maximum iteration number T is reached, if so, outputting the position of the particle swarm, namely the parameters of the diode model, and otherwise, turning to the step S3.4;
step S4: and (3) constructing a solar photovoltaic cell system model according to the parameters optimally extracted in the table 3 (single-diode model parameters) and the 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 accuracy of the optimization model. Fig. 5 is a graph of a fitted relative error of current and power for a single diode model, and fig. 6 is a graph of a fitted relative error of current and power for a two-diode model.
TABLE 3
TABLE 4
And predicting the measurement data of the solar photovoltaic cell system under the specific actual measurement condition according to the approved solar photovoltaic cell system model obtained by the parameter optimization extraction method. Fig. 7 is a fitted curve between calculated data of a single diode model of current-voltage, voltage-power and measured data of RTC France photovoltaic cells. Fig. 8 is a fitting curve between the calculated data of the current-voltage, voltage-power two-diode model and the measured data of the rtcfance photovoltaic cell.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
It should be noted that the embodiments of the present invention can 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 specially designed 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 code being provided, for example, in programmable memory or on a data carrier such as an optical or electronic signal carrier.
Claims (4)
1. A method for optimizing and extracting model parameters of a solar photovoltaic cell system is characterized by comprising the following steps:
step S1: under the specific actual measurement condition, measuring the solar photovoltaic cell system to obtain measurement data, wherein the measurement data comprises output current ILAn output voltage VL；
Step S2: establishing a solar photovoltaic cell system model which comprises a current-voltage characteristic equation of a single diode and a current-voltage characteristic equation of a double diode, and determining an objective function of an optimization problem;
step S3: extracting parameters of the solar photovoltaic cell system model by utilizing a particle swarm search strategy and a random reselection mechanism to obtain an optimized solar photovoltaic cell system model;
step S4: and 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.
2. The method for optimizing and extracting the parameters of the solar photovoltaic cell system model according to claim 1, wherein the step S2 specifically comprises:
step S2.1: establishing a mathematical model of the single diode, wherein a specific formula is shown as (1);
wherein, ILTo output current, IphIs a photo-generated current, IsdFor reverse saturation current, q is the charge constant, VLTo output a voltage, RSIs a series resistor, n is an ideal factor, k is the Boltzmann constant, T is the Kelvin temperature, R is the resistanceshFor shunt resistance, the resulting single diode model contains 5 unknown variables: i isph，Isd，RS，Rsh，n；
Step S2.2: an objective function of a single diode is provided, and the 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 as (4);
wherein, Isd1And Isd2Respectively representing diffusion current and saturation current, n1And n2Representing different ideality factors, the other parameters are consistent with the single-diode mathematical model, and the obtained double-diode model comprises 7 parameters: i isPh，Isd1，Isd2，RS，Rsh，n1，n2；
Step S2.4: an objective function of the double diodes is provided, and the specific formulas are shown as (5) and (6);
3. the method for optimizing and extracting parameters of a solar photovoltaic cell system model according to claim 2, wherein the step S3 specifically comprises:
step S3.1: defining initial parameter values: the maximum iteration time T, the particle swarm number N, the dimension dim, the upper limit ub of the value range, the lower limit lb of the value range, the group velocity v, the inertia weight w and the individual factor r1Social factor r2Probability of random reselection pa；
Step S3.2: randomly generating an initial particle swarm position in a defined domain by adopting a formula (7);
XN，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 pbestAnd global best candidate solution gbest；
Step S3.4: iteratively searching and updating the particle swarm positions, and calculating by using formulas (8) and (9) to obtain new swarm positions;
XN，dim＝XN，dim+vN，dim； (9)
step S3.5: calculating the fitness value of the current updated particle swarm position, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.6: randomly reselecting an individual and generating a new position by adopting the formulas (10), (11) and (12);
K＝rand(sizepop，dim)＞pa(10)
stepsize＝rand×(Xrandpermutation，dim-Xrandpermutaion，dim)； (11)
step S3.7: calculating the fitness value of the current randomly regenerated new individual, and judging whether the fitness value is superior to the current optimal candidate solution pbestIf the result is better, replacing and updating, otherwise, continuing to maintain;
step S3.8: determining a global best candidate solution gbestIf the change is generated, replacing and updating if a better solution is generated, otherwise, continuously keeping;
step S3.9: and judging whether the maximum iteration number T is reached, if so, outputting the position of the particle swarm, namely the parameters of the diode model, and otherwise, turning to the step S3.4.
4. A method for predicting measurement data of a solar photovoltaic battery system under actual measurement conditions based on a solar photovoltaic battery 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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