CN113779733A - Photovoltaic module model parameter hybrid optimization identification method - Google Patents
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Abstract
The invention discloses a photovoltaic module model parameter hybrid optimization identification method. And determining internal parameters of the solar photovoltaic cell panel based on the single diode model of the photovoltaic cell, and establishing a photovoltaic module five-parameter model. And performing parameter identification on the I-V curve of the solar photovoltaic module by utilizing a particle swarm optimization algorithm and a wolf optimization algorithm. In the specific implementation process, the particle swarm algorithm sets the particles in the swarm at random positions, the wolf algorithm is used for avoiding the particles from falling into local optimum, and the main aim is to obtain a group of parameters when the root mean square error between experimental data and theoretical data is minimum. The particle swarm and gray wolf hybrid optimization algorithm has the main advantages that the global search capability of the particle swarm algorithm and the local search capability of the gray wolf algorithm are combined, and the self-adaptive inertial weight is adopted in the particle swarm algorithm to ensure the optimization iteration speed of the algorithm. The method has higher accuracy in identifying the parameters of the photovoltaic cell, and can effectively avoid the problem of falling into premature convergence.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic module model parameter hybrid optimization identification method.
Background
The ever-increasing demand for electricity and the necessity to protect the environment have prompted increased attention to renewable sources. Solar energy is considered a key and promising alternative energy source due to its advantages in availability and cleanliness. The photovoltaic module is a key part of a photovoltaic power station for externally conveying energy, and a reasonable and accurate mathematical modeling mode is an important basis for improving the design concept of the photovoltaic module. As a widely used mathematical model, the single-diode model has certain difficulty in extracting parameters of the photovoltaic module due to the fact that an I-V curve of the output characteristic of the single-diode model is nonlinear. The meta-heuristic photovoltaic module parameter identification method has the advantages of high speed and high precision, but the conventional methods such as a genetic algorithm, a particle swarm algorithm or an artificial bee colony algorithm have certain defects and are easy to fall into local optimization.
Recently, hybrid technology is prevalent in solving the problem of extracting parameters of a photovoltaic model, and different strategies or algorithms are combined together to solve the defect of a single algorithm. In order to better realize the extraction of the model parameters of the photovoltaic module, the invention adopts a hybrid technology to complete the parameter identification of the I-V characteristic curve of the photovoltaic module under any environmental condition.
Disclosure of Invention
The invention aims to provide a photovoltaic module model parameter hybrid optimization identification method to solve the problems of large error of a calculation result and poor robustness of a photovoltaic module model parameter identification method in the prior art.
In order to achieve the purpose, the invention adopts the technical scheme that: a photovoltaic module model parameter mixing optimization identification method comprises the following steps:
a. determining a photovoltaic module five-parameter model according to the photovoltaic cell single diode model, and further determining parameters to be identified;
b. acquiring photovoltaic assembly I-V curve data under certain temperature and irradiance conditions, determining a feasible solution interval of each parameter to be identified according to the I-V curve data, and defining a fitness function;
c. establishing a particle population by combining parameters of a photovoltaic module to be identified, initializing each basic parameter of the particle population according to an operation method of a particle swarm algorithm, and initializing the fitness value of each particle through a fitness function;
d. updating the speed and the position of the particle population by adopting an updating formula for adjusting the particle speed by adopting self-adaptive inertia weight and combining a gray wolf algorithm;
e. calculating the fitness value of each particle through a fitness function, and updating the individual optimal position and the group optimal position of each particle;
f. judging whether the iteration times of the algorithm reach the maximum or the accuracy of the fitness value meets the requirement, if so, outputting a global optimal solution to obtain a group of photovoltaic module model parameters meeting the conditions; if not, repeating the steps d-f until a preset stopping condition is met.
In the step a, the photovoltaic module five-parameter model is as follows:
in the formula, I is output current of the photovoltaic module; v is the output voltage of the photovoltaic module; i isphIs a photo-generated current; i is0Is a diode reverse saturation current; q is an electronic charge; k is Boltzmann constant; n is a diodeAn ideality factor; rsIs a series resistor; rshIs a parallel resistor; t is the Kelvin temperature of the component during operation; vtIs a thermal voltage; n is a radical ofsThe number of the battery pieces is the number of the components connected in series.
The parameter to be identified is Iph、I0、n、Rs、Rsh。
The step b comprises the following steps:
b1, determining a feasible solution interval of each parameter to be identified, wherein the specific method comprises the following steps: n is in the range of [0.3,2 ]]；IphThe maximum value of the actually measured current is taken as the center of the interval, and the variation range is 12 percent; i is0The theoretical value calculated by the formula (3) is taken as the center of the interval, and the variation range is 55 percent; rsTaking a standard value under a standard test condition as an interval center, wherein the variation range is 15%; rshTaking the reciprocal of the derivative value of the actually measured I-V curve at the short-circuit current as the center of the interval, wherein the variation range is 20%;
in the formula I0,refThe reverse saturation current of the photovoltaic module under the standard test condition is obtained; i issc,refShort-circuit current of the photovoltaic module under standard test conditions; voc，refThe open circuit voltage of the photovoltaic module under the standard test condition; n isrefThe ideal factor of the photovoltaic module under the standard test condition is obtained; t isrefIs the temperature under standard test conditions; egThe forbidden bandwidth is; calculated I0' can be taken as I0Taking the central value of the interval;
b2, defining a fitness function, selecting the root mean square error of the measured current and the theoretical current as the fitness function, wherein the formula is as follows:
in the formula, FfitFor the fitness function to be calculated, m is the number of actually measured data points, IrealTo measure the current value, IcalThe resulting current values were calculated for theory.
In step c, the initialization parameters of the particle swarm algorithm include: maximum number of iterations TmaxThe number N of the particle populations and the dimension D represent the speed V and the position S of the particle populations of the photovoltaic module model five parameters, and the boundaries of the speed and the position of the particles are preset according to the requirements in the step b 1; wherein the particle velocity and position are initially assigned to the particle using a random function.
The step d comprises the following steps:
d1, updating the formula by using the adaptive inertia weight to adjust the velocity of the particles, wherein the formula is expressed as follows:
in the formula (I), the compound is shown in the specification,
is the speed of the ith particle in the d-dimension in the (k + 1) th iteration, omega is the inertia weight, k is the current iteration number, c1Learning a factor for a population, c2Learning a factor for an individual, r1And r2Is a random coefficient, and is a random coefficient,
for the optimal position of the ith particle in the d-dimension in the kth iteration,
the position of the population-optimal particle in the d dimension in the k iteration is determined; in addition, the expression of the adaptive inertial weight is as follows:
in the formula: omegastartIs the initial inertial weight; omegaendTo terminate inertial weights; t is the current generation times;tmaxis the maximum generation times set initially; k is a control factor.
d2, judging whether the speed and position of the particle are less than a certain threshold, if so, indicating that the part of the particle is trapped in a local optimizing process, and replacing the part of the particle by combining a wolf optimization algorithm; if not, continuing to the next step.
The step d2, namely the replacement process of the unsatisfactory particles by the gray wolf optimization algorithm, includes:
d21, initializing a gray wolf population, generating a certain number of gray wolf positions, designing the population size of the gray wolf according to the number of particles to be replaced, initializing parameters alpha, beta and sigma, and determining the maximum iteration number;
d22, calculating the individual fitness value of the wolf, and finding the optimal solution of the fitness value (the position X of the alpha wolf)α) Sub-optimal solution (bit position X of the beta wolf)β) And the third best solution (position X of sigma wolf)σ) Updating the position information of the remaining wolfs and updating the values of the parameters alpha, beta and sigma;
d23, judging whether the maximum iteration number is reached or a preset threshold value is reached, if the optimization process is terminated, replacing the original speed and position of the particles which do not meet the requirements with the average value of the searched optimal solution, the searched second optimal solution and the searched third optimal solution; otherwise, the step d 22-d 23 is executed continuously.
Compared with the prior art, the invention has the following beneficial effects: the method initializes the distribution position of the population by presetting the feasible solution interval of the parameter to be identified, improves the optimization iteration speed of the particle swarm optimization by self-adaptive inertia weight, and solves the problem that the particle swarm optimization falls into local optimization in the optimization process by combining the gray wolf calculation. The algorithm can better solve the defect of premature convergence in the parameter identification process of the meta-heuristic method, and has higher precision on the parameter identification of the photovoltaic module model.
Drawings
Fig. 1 is a structural block diagram of a photovoltaic module model parameter hybrid optimization identification method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a specific algorithm of a method for identifying a model parameter mixture optimization of a photovoltaic module according to an embodiment of the present invention;
FIG. 3 is a flow chart of a gray wolf algorithm of the present invention;
FIG. 4 is a schematic diagram of a photovoltaic cell single diode model of the present invention;
FIG. 5 is a graph of the comparison of the I-V curve obtained by the five-parameter calculation obtained by the proposed algorithm and the measured I-V curve.
Detailed Description
The invention is further described below with reference to fig. 1-5. The following examples are only for more clearly describing the technical solutions of the present invention, and the protection scope of the present invention is not limited thereby.
The method provided by the invention is used for verifying the effectiveness of the method by acquiring the actually measured I-V curve data of a certain type of photovoltaic module in the photovoltaic power station.
Step 1: fig. 4 is an equivalent circuit diagram of a photovoltaic cell single-diode model; according to the photovoltaic cell model, a photovoltaic module five-parameter model shown in the following formula can be established, and parameters to be identified are determined:
in the formula, I is output current of the photovoltaic module; v is the output voltage of the photovoltaic module; i isphIs a photo-generated current; i is0Is a diode reverse saturation current; q is the electronic charge and takes the value of 1.602e-19C; k is Boltzmann constant and takes 1.38e-23J/K; n is the ideal factor of the diode; rsIs a series resistor; rshIs a parallel resistor; t is the Kelvin temperature of the component during operation; vtIs a thermal voltage; n is a radical ofsThe number of the battery pieces connected in series is the component; under the standard test condition, the temperature is 298K, and the irradiance value is 1000W/m2(ii) a Finally determining the parameter to be identified as Iph、I0、 n、Rs、Rsh。
Step 2: inputting photovoltaic module data plate parameter, actual output voltage V and output current I of photovoltaic module under certain temperature T and irradiance Irr under the actual measurement condition are obtained, and this embodiment adopts actual measurement I-V curve under the multiunit environmental parameter corresponds, and its environmental parameter is respectively: 802W/m2，32.15℃；703W/m2，28.93℃； 605.5W/m2，25.42℃；505.9W/m2，20.75℃；
And step 3: and determining a feasible solution interval of each parameter to be identified, and defining a fitness function. The step 3 comprises the following specific steps:
step 3-1: determining a feasible solution interval of each parameter to be identified, wherein the specific method comprises the following steps: n is in the range of [0.3,2 ]]； IphThe maximum value of the actually measured current is taken as the center of the interval, and the variation range is 12 percent; i is0The theoretical value calculated by the formula (3) is taken as the center of the interval, and the variation range is 55 percent; rsTaking a standard value 8.932 omega under a standard test condition as an interval center, wherein the variation range is 15%; rshTaking the reciprocal of the derivative value of the actually measured I-V curve at the short-circuit current as the center of the interval, and the variation range is 20%.
In the formula I0，refThe reverse saturation current of the photovoltaic module under the standard test condition is obtained; i issc，refShort-circuit current of the photovoltaic module under standard test conditions; voc，refThe open circuit voltage of the photovoltaic module under the standard test condition; n isrefThe ideal factor of the photovoltaic module under the standard test condition is obtained; t isrefIs the temperature under standard test conditions; egThe forbidden bandwidth is; calculated I0' can be taken as I0The central value of the value interval.
Step 3-2: defining a fitness function, selecting the root mean square error of the measured current and the theoretical current as the fitness function, wherein the formula is as follows:
in the formula, FfitFor the fitness function to be calculated, m is the number of actually measured data points, IrealTo measure the current value, IcalThe resulting current values were calculated for theory.
And 4, step 4: the initialization parameters of the particle swarm algorithm comprise: the maximum iteration number is 800, the number of the particle populations is 60, the dimension is 5, the ranges of the speed and the position of the particle populations in each dimension are [ -1, 1] and [ -0.4, 0.4], respectively, and the speed and the position of the particles are initially assigned with random functions.
And 5: and updating the speed and the position of the particle population. Step 5 comprises the following sub-steps:
step 5-1: the velocity of the particles is adjusted using the adaptive inertial weight to update a formula, which is expressed as follows:
in the formula (I), the compound is shown in the specification,
is the speed of the ith particle in the d-dimension in the (k + 1) th iteration, omega is the inertia weight, k is the current iteration number, c10.15 is taken as a group learning factor; c. C2Taking 0.3 as an individual learning factor; r is1And r2Are random coefficients and have value ranges of [0, 1]]，
For the optimal position of the ith particle in the d-dimension in the kth iteration,
the position of the population-optimal particle in the d dimension in the k iteration is determined; in addition, the expression of the adaptive inertial weight is as follows:
in the formula: omegastartTaking the experience value as 0.9 for the initial inertia weight; omegaendTo terminate the inertial weight, take the empirical value of 0.48; t is the current generation times; t is tmaxIs the maximum generation times set initially; k is a control factor, and the value of k is 0.6;
step 5-2: judging whether the speed and the position of the particles are smaller than a certain threshold value, if so, indicating that the part of the particles are trapped in a local optimizing process, and replacing the part of the particles by combining a wolf optimization algorithm; if not, continuing to perform the next step;
step 6: replacing the particles which do not meet the requirements by adopting a wolf optimization algorithm, wherein the method comprises the following steps:
step 6-1: initializing a gray wolf population, generating a certain number of gray wolf positions, designing the population size of the gray wolf according to the number of particles to be replaced, initializing parameters alpha, beta and sigma, and determining the maximum iteration number;
step 6-2: calculating the individual fitness value of the wolf, and finding the optimal solution of the fitness value (the position X of the alpha wolf)α) Sub-optimal solution (bit position X of the beta wolf)β) And the third best solution (position X of sigma wolf)σ) Updating the position information of the remaining wolfs and updating the values of the parameters alpha, beta and sigma;
step 6-3: judging whether the maximum iteration number is reached or a preset threshold value is reached, if the optimization process is terminated, replacing the original speed and position of the particles which do not meet the requirements with the average value of the searched optimal solution, the searched suboptimal solution and the third optimal solution; otherwise, continuing to execute the steps 6-2 to 6-3;
and 7: and calculating the fitness of each particle, and updating the individual optimal position and the group optimal position.
And 8: and judging whether the iteration times reach the maximum or the accuracy of the calculated adaptability value meets a threshold value, if so, ending the optimization process, turning to the step 9, and if not, turning to the step 5.
And step 9: and outputting a global optimal solution which is the five parameters to be identified of the photovoltaic module, and ending the algorithm process.
Through the above embodiment, parameter identification of the photovoltaic module can be completed, in the embodiment, the comparison result between the I-V curve obtained through parameter calculation obtained through the provided algorithm and the actually measured I-V curve is shown in fig. 5, the four groups of curves are basically overlapped, and the average value of the root mean square error of the current is only 0.0105A.
The invention improves the speed of the particle swarm optimization algorithm in the whole optimization iteration through the self-adaptive inertial weight, and solves the problem that the particle swarm is easy to fall into local optimization in the optimization process by adopting the gray wolf optimization algorithm. The final result has good precision, and the method has strong robustness.
The above-described embodiments are merely preferred embodiments of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A photovoltaic module model parameter mixing optimization identification method is characterized by comprising the following steps:
a. establishing a five-parameter model of the photovoltaic module according to the photovoltaic cell single diode model, and determining parameters to be identified;
b. acquiring photovoltaic assembly I-V curve data under certain temperature and irradiance conditions, determining a feasible solution interval of each parameter to be identified according to the I-V curve data, and defining a fitness function;
c. establishing a particle population by combining parameters of a photovoltaic module to be identified, initializing each basic parameter of the particle population according to an operation method of a particle swarm algorithm, and initializing the fitness value of each particle through a fitness function;
d. updating the speed and the position of the particle population by adopting an updating formula for adjusting the particle speed by adopting self-adaptive inertia weight and combining a gray wolf algorithm;
e. and calculating the fitness value of each particle through a fitness function, and updating the individual optimal position and the group optimal position of the particle.
f. Judging whether the iteration times of the algorithm reach the maximum or the accuracy of the fitness value meets the requirement, if so, outputting a global optimal solution to obtain a group of photovoltaic module model parameters meeting the conditions; if not, repeating the steps d-f until a preset stopping condition is met.
2. The method according to claim 1, wherein in step a, the photovoltaic module five-parameter model is:
in the formula, I is output current of the photovoltaic module; v is the output voltage of the photovoltaic module; i isphIs a photo-generated current; i is0Is a diode reverse saturation current; q is an electronic charge; k is Boltzmann constant; n is the ideal factor of the diode; rsIs a series resistor; rshIs a parallel resistor; t is the Kelvin temperature of the component during operation; vtIs a thermal voltage; n is a radical ofsThe number of the battery pieces is the number of the components connected in series. The parameter to be identified is Iph、I0、n、Rs、Rsh。
3. The method according to claim 2, wherein the step b comprises:
b1, determining a feasible solution interval of each parameter to be identified, wherein the specific method comprises the following steps: n is in the range of [0.3,2 ]]；IphThe maximum value of the actually measured current is taken as the center of the interval, and the variation range is 12 percent; i is0The theoretical value calculated by the formula (3) is taken as the center of the interval, and the variation range is 55 percent; rsTaking a standard value under a standard test condition as an interval center, wherein the variation range is 15%; rshTaking the reciprocal of the derivative value of the actually measured I-V curve at the short-circuit current as the center of the interval, wherein the variation range is 20%;
in the formula I0,refThe reverse saturation current of the photovoltaic module under the standard test condition is obtained; i issc,refShort-circuit current of the photovoltaic module under standard test conditions; voc,refThe open circuit voltage of the photovoltaic module under the standard test condition; n isrefThe ideal factor of the photovoltaic module under the standard test condition is obtained; t isrefIs the temperature under standard test conditions; egThe forbidden bandwidth is; calculated I0' can be taken as I0Taking the central value of the interval;
b2, defining a fitness function, selecting the root mean square error of the measured current and the theoretical current as the fitness function, wherein the formula is as follows:
in the formula, FfitFor the fitness function to be calculated, m is the number of actually measured data points, IrealTo measure the current value, IcalThe resulting current values were calculated for theory.
4. The method for identifying model parameters of photovoltaic modules by hybrid optimization as claimed in claim 2, wherein in said step c, the initialization parameters of the particle swarm algorithm comprise: maximum number of iterations TmaxThe number N of particle populations, the dimension D, the velocity V and the position S of the particle populations representing the five parameters of the photovoltaic module model, the boundaries of the particle velocity and the position being preset according to claim 3; wherein the particle velocity and position are determined by using a randomizerThe number is assigned to the particle initialization.
5. The method according to claim 2, wherein the step d comprises:
d1, updating the formula by using the adaptive inertia weight to adjust the velocity of the particles, wherein the formula is expressed as follows:
in the formula (I), the compound is shown in the specification,
is the speed of the ith particle in the d-dimension in the (k + 1) th iteration, omega is the inertia weight, k is the current iteration number, c1Learning a factor for a population, c2Learning a factor for an individual, r1And r2Is a random coefficient, and is a random coefficient,
for the optimal position of the ith particle in the d-dimension in the kth iteration,
the position of the population-optimal particle in the d dimension in the k iteration is determined; in addition, the expression of the adaptive inertial weight is as follows:
in the formula: omegastartIs the initial inertial weight; omegaendTo terminate inertial weights; t is the current generation times; t is tmaxIs the maximum generation times set initially; k is a control factor;
d2, judging whether the speed and position of the particle are less than a certain threshold, if so, indicating that the part of the particle is trapped in a local optimizing process, and replacing the part of the particle by combining a wolf optimization algorithm; if not, continuing to the next step.
6. The method of claim 5, wherein the d2 replacement process of the unsatisfactory particles by the Grey wolf optimization algorithm comprises:
d21, initializing a gray wolf population, generating a certain number of gray wolf positions, designing the population size of the gray wolf according to the number of particles to be replaced, initializing parameters alpha, beta and sigma, and determining the maximum iteration number;
d22, calculating the individual fitness value of the wolf; finding the optimal solution for the fitness value, i.e. the position X of the alpha wolfα(ii) a Sub-optimal solution, i.e. bit position X of the beta wolfβ(ii) a And the third best solution, position X of sigma wolfσUpdating the position information of the remaining wolfs and updating the values of the parameters alpha, beta and sigma;
d23, judging whether the maximum iteration number is reached or a preset threshold value is reached, if the optimization process is terminated, replacing the original speed and position of the particles which do not meet the requirements with the average value of the searched optimal solution, the searched second optimal solution and the searched third optimal solution; otherwise, the step d 22-d 23 is executed continuously.
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