CN109388845B - Photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution - Google Patents
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Abstract
The invention relates to a photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution, which comprises the following steps: and acquiring an actual I-V characteristic curve of the photovoltaic panel, and selecting a corresponding photovoltaic model. An objective function for the optimization problem is determined. The position of the initial point is optimized by a reverse learning algorithm (OBL) algorithm. Model parameters are extracted from different circuit models using an enhanced complex evolution algorithm (ESCE). The model parameters of the photovoltaic panel under different actual measurement conditions are extracted through the algorithm. The photovoltaic array parameter extraction method based on the reverse learning strategy and the enhanced complex evolutionary algorithm is high in speed, strong in convergence and good in stability.
Description
Technical Field
The invention relates to a photovoltaic power generation array model parameter extraction technology, in particular to a photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution.
Background
With the exhaustion of global energy, people have an increasing demand for new green energy. Photovoltaic power generation converts solar energy into electric energy, is green, clean and pollution-free, and is considered to be one of the most available schemes capable of replacing traditional fossil energy. As the core of photovoltaic power generation, a large-scale photovoltaic array is mostly formed by connecting photovoltaic modules in series and in parallel. Therefore, modeling and parameter extraction are carried out on the I-V characteristics of the photovoltaic cell under the actual measurement condition, and the method has important significance for overall performance evaluation, system optimization design and real-time fault detection of a photovoltaic power generation system.
In order to evaluate a photovoltaic power generation system, a photovoltaic array needs to be modeled first, and currently, the main photovoltaic models mainly include a single-diode five-parameter model and a double-diode seven-parameter model. The five-parameter model has certain accuracy and is simple and effective to calculate, the seven-parameter model is complex in structure, low in calculation efficiency and more accurate, and the estimated parameters can better approximate to an actually measured curve.
The current main parameter extraction methods can be divided into analytic methods, intelligent optimization algorithms and hybrid methods. The analytical method can directly and quickly obtain model parameters, but has poor accuracy and robustness and needs complex mathematical derivation. Therefore, the problem of searching for parameters such as photovoltaic model by using an intelligent optimization algorithm is widely favored. At present, a large number of intelligent optimization algorithms are applied to parameter extraction of photovoltaic array models (such as ABC, GOFPANM, rcr-IJADE, STLBO, GOTLBO and the like), and the algorithms have the characteristics of low convergence speed, large calculation amount, poor robustness, low stability and the like. The chaotic complex evolution (SCE) algorithm was first proposed in 1993, primarily using deterministic competitive complex evolution to optimize the complex shapes, and then extracting new complex shapes by mixing. The algorithm has the characteristics of good robustness, high calculation efficiency and the like. However, when facing a multi-target non-linear problem, the selection of the initial point may affect the convergence of the algorithm. To further improve the convergence of SCE, VCMarian proposed an algorithm in 2011 in which Differential Evolution (DE) and SCE are mixed. The algorithm combines an adaptive downhill simplex search algorithm (NMS) with differential evolution. The method can effectively improve the overall performance and the calculation efficiency of the SCE.
Disclosure of Invention
The invention aims to provide a photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution, so as to overcome the defects in the prior art.
In order to realize the purpose, the technical scheme of the invention is as follows: the photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution is realized according to the following steps:
s1, acquiring an actual I-V characteristic curve of a photovoltaic panel, and selecting a corresponding photovoltaic model;
step S2: determining an objective function of an optimization problem;
and step S3: optimizing the position of the initial point by adopting a reverse learning algorithm;
and step S4: extracting model parameters according to different circuit models by adopting an enhanced complex evolution algorithm;
step S5: and extracting model parameters of the photovoltaic panel under different actual measurement conditions.
In an embodiment of the present invention, in the step S1, the method further includes the following steps:
step S11: collecting output current and voltage data of the photovoltaic panel under different illumination and irradiance conditions through a collecting plate, and respectively storing the data as a number table of N x 1; wherein N is the number of the collected sample points, and the number of the collected sample points of the current and the voltage is the same;
step S12: selecting a single diode model and a double diode model as photovoltaic models;
the single diode model is:
wherein, I pv Is the photo-generated current, I o Is the saturation current of the diode, a is the ideality factor of the diode, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a Boltzmann constant (1.380653X 10) -23 J/K), T is ambient temperature, q is absolute value of electron charge amount (1.60217646 × 10) -19 C)；
The two-diode model is:
wherein, I pv Is a photo-generated current, I o1 ,I o2 Saturation currents of two different diodes, a 1 ,a 2 Ideality factor, R, of two diodes, respectively s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a Boltzmann constant (1.380653X 10) -23 J/K), T is ambient temperature, q is the absolute value of the electron charge amount (1.60217646X 10) -19 C)。
In an embodiment of the present invention, in the step S2, the objective function, that is, the fitness function, is obtained according to the following method:
the root mean square error is recorded as:
wherein N is the total number of samples on the I-V characteristic curve, and f (V, I, X) is the absolute error between the measured current and the model estimated current;
f (V, I, X) for a single diode is:
f (V, I, X) for a dual diode is:
in an embodiment of the present invention, in the step S3, the method further includes the following steps:
step S31: initializing corresponding control parameters, including: the number of complex shapes npg, the number of solutions in each complex shape ngs, the total number of sample candidates npt, and the lower and upper bounds of the candidate solutions BL and BU;
step S32: randomly initializing npt candidate sample points in the lower and upper bounds BL, BU by the following process:
x i ＝BL+rand*(BU-BL),i＝1,2,...,npt
calculating the adaptive function value of each position;
step S33: obtaining the opposite solution x by utilizing the reverse learning mode for the candidate solution o The process is as follows:
x o ＝BU+BL-x i
calculating fitness function value of opposite solution, and calculating x o And x i And (4) recording the mixed solution as a new candidate solution s, and performing ascending arrangement according to the size of the fitness function of the new candidate solution s.
In an embodiment of the present invention, in the step S4, the following step is further included:
step S41: partitioning the optimal npt candidate solutions into npg complex shapes;
step S42: evolving each complex form through an enhanced complex evolution algorithm to obtain the position of a new candidate solution;
step S43: replacing the original complex shape candidate points and the fitness function values corresponding to the original complex shape candidate points with the optimized points in the evolved complex shape;
step S44: and when the calculation times of the target function reach the maximum, finishing the algorithm and returning to the optimal solution.
In an embodiment of the present invention, in the step S52, the method further includes the following steps:
step S521: initializing control parameters; the number of the simplex vertexes is recorded as q, the control coefficients are respectively alpha, beta and gamma, and the evolution times of the primary and secondary complex shapes are (m and n);
step S522: selecting q points from npt points of the complex shape as the top points of the simplex shape with ngs, and calculating the central points ce of the q points, wherein the calculation method comprises the following steps:
wherein u is j Is the position of the q-1 simplex vertices;
step S523: obtaining corresponding reflection point u according to the central point r ＝ce-α(ce-u q ) After adopting boundary control, calculating a fitness function value of the reflection point; if the fitness function value of the reflection point is smaller than the optimal point in the complex shape, the value of the extension point is calculated as follows: u. of e ＝ce+γ·(ce-u q ) Replacing the worst position in the vertex with the best position in the reflection point and the expansion point;
step S524: when the position of the reflection point is worse than the best point in the complex shape, a compression point is generated according to the best point, the center point, the worst point and the reflection point in the complex shape as follows:
wherein u is g And u q Positions of global optimum and worst points, u, respectively r Is the position of the reflection point, beta is the shrinkage factor, F q-1 Is the fitness function value of the sub-difference point, F r Is the fitness function value of the reflection point, F q Is the fitness function value of the worst point;
step S525: if the compression point cannot update the worst point, optimizing the worst point according to the global optimum point and the local optimum point in the following mode: u. u z ＝u q +β(u g -u q )+γ×r·(u b -u q ) R.epsilon (-1, 1), where u z Is the position of the update point, u g Is the global optimum point, u b Is a local optimum, r is a random number between-1 and 1, γ is a spreading factor;
step S526: the steps S522 to S525 are repeated n times, and the steps S521 to S525 are repeated m times.
Compared with the prior art, the invention has the following beneficial effects: the photovoltaic array parameter extraction method based on the reverse learning and the enhanced complex evolution (ESCE-OBL) adopts a reverse learning strategy to carry out secondary initialization on unknown parameters of a model, then selects a complex shape, optimizes the selected complex shape through an improved complex evolution algorithm, replaces the previous complex shape with the evolved complex shape, mixes and combines a plurality of complex shapes, sorts the complex shapes, and then separates out a new complex shape for optimization. In the optimization process, the minimum value of the objective function is solved, and finally, the model parameter value when the objective function value is minimum is approximated, and the model parameter value is considered to be the optimal objective function value in convergence. Compared with the traditional SCE algorithm, the method has higher convergence rate and robustness in the application of photovoltaic parameter extraction. The accuracy of the method can be the same as that of the SCE, but the convergence speed of the method is obviously higher than that of the SCE, the method has better stability and robustness, and the stability of the method can reach more than one order of magnitude by taking the variance of RMSE under different data samples as reference.
Drawings
FIG. 1 is a flow chart of a photovoltaic array parameter extraction method based on a reverse learning strategy and an enhanced complex evolution algorithm in the invention
FIG. 2 is a detailed flow chart of the ESCE-OBL algorithm in one embodiment of the present invention
FIG. 3 is a flow chart of the enhanced CCE evolution strategy in one embodiment of the present invention
Fig. 4 is a schematic diagram illustrating comparison of the parameter extraction results of the ESCE-OBL and the existing algorithm in a single diode model according to an embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating comparison of the parameter extraction results of the ESCE-OBL and the prior algorithm in the two-diode model according to an embodiment of the present invention.
Fig. 6 is a schematic diagram for further comparing the extraction results of the ESCE-OBL and SCE algorithms for the photovoltaic cell and the photovoltaic module parameters under the single-diode model according to an embodiment of the present invention.
Fig. 7 is a schematic diagram illustrating further comparison of the extraction results of the ESCE-OBL and SCE algorithms for the photovoltaic cell and the photovoltaic module parameters under the two-diode model according to an embodiment of the present invention.
FIG. 8 is a diagram illustrating the convergence curves of the ESCE-OBL and SCE algorithms for two extraction cases in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention provides a photovoltaic array parameter extraction method based on a reverse learning strategy and an enhanced complex evolution algorithm, which is realized according to the following steps as shown in fig. 1 and fig. 2:
s1, acquiring an actual I-V characteristic curve of a photovoltaic panel, and selecting a corresponding photovoltaic model;
step S2: determining an objective function of an optimization problem;
and step S3: optimizing the position of the initial point by adopting a reverse learning algorithm;
and step S4: extracting model parameters according to different circuit models by adopting an enhanced complex evolution algorithm;
step S5: and extracting model parameters of the photovoltaic panel under different actual measurement conditions.
Further, in this embodiment, in step S1, the following steps are further included:
step S11: collecting output current and voltage data of the photovoltaic panel under different illumination and irradiance conditions through a collecting plate, and respectively storing the data as a number table of N x 1; wherein N is the number of the collected sample points, and the number of the collected sample points of the current and the voltage is the same;
step S12: selecting a single diode model and a double diode model as photovoltaic models;
the single diode model is:
wherein, I pv Is a photo-generated current, I o Is the saturation current of the diode, a is the ideality factor of the diode, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a Boltzmann constant (1.380653X 10) -23 J/K), T is ambient temperature, q is absolute value of electron charge amount (1.60217646 × 10) -19 C)；
The double diode model is:
wherein, I pv Is a photo-generated current, I o1 ,I o2 Saturation currents of two different diodes, a 1 ,a 2 Ideal factor of two diodes, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a Boltzmann constant (1.380653X 10) -23 J/K), T is ambient temperature, q is absolute value of electron charge amount (1.60217646 × 10) -19 C)。
Further, in this embodiment, a single diode model is selected to perform parameter extraction on the photovoltaic array, and the advantages of the photovoltaic array are further verified by performing parameter extraction on the photovoltaic cell through a double diode model.
Further, in this embodiment, in step S2, the objective function, that is, the fitness function, is obtained according to the following method:
the root mean square error is recorded as:
wherein N is the total number of samples on the I-V characteristic curve, and f (V, I, X) is the absolute error between the measured current and the model estimated current;
f (V, I, X) for a single diode is:
f (V, I, X) for a dual diode is:
further, in this embodiment, in step S3, the following steps are further included:
step S31: initializing corresponding control parameters, including: the number of complex shapes npg, the number of solutions in each complex shape ngs, the total number of samples of candidate solutions npt, and the lower and upper boundaries of the candidate solutions BL and BU;
step S32: randomly initializing npt candidate sample points in the lower and upper bounds BL, BU, which comprises the following steps:
x i ＝BL+rand*(BU-BL),i＝1,2,...,npt
calculating the adaptive function value of each position;
step S33: obtaining the opposite solution x by utilizing the reverse learning mode for the candidate solution o The process is as follows:
x o ＝BU+BL-x i
calculating fitness function value of opposite solution, and calculating x o And x i And (4) after mixing, marking as a new candidate solution s, and performing ascending arrangement according to the size of the fitness function.
Further, in this embodiment, in step S4, the following steps are further included:
step S41: partitioning the optimal npt candidate solutions into npg complex shapes;
step S42: evolving each complex form through an enhanced complex evolution algorithm to obtain the position of a new candidate solution;
step S43: replacing the original complex shape candidate points and the fitness function values corresponding to the original complex shape candidate points by the optimized points in the evolved complex shape;
step S44: and when the calculation times of the target function reach the maximum, ending the algorithm and returning to the optimal solution.
Further, in the present embodiment, as shown in fig. 3, in step S52, the following steps are further included:
step S521: initializing a control parameter; the number of the simplex vertexes is recorded as q, the control coefficients are respectively alpha, beta and gamma, and the evolution times of the primary and secondary complex shapes are (m and n); preferably, q =6 (single diode model), q =8 (two diode model), α =1, β =0.5, γ =2,m =1, n =1;
step S522: selecting q points from npt points of the complex shape as the vertices of the simplex shape with ngs, and calculating the central points ce of the q points, wherein the calculation method comprises the following steps:
wherein u is j Is the position of the q-1 simplex vertices;
step S523: obtaining corresponding reflection point u according to the central point r ＝ce-α(ce-u q ) After adopting boundary control, calculating a fitness function value of the reflection point; if the fitness function value of the reflection point is smaller than the optimal point in the complex shape, the numerical value of the extension point is calculated according to the following mode: u. of e ＝ce+γ·(ce-u q ) Replacing the worst position in the vertex with the best position in the reflection point and the expansion point;
step S524: when the position of the reflection point is worse than the best point in the complex shape, a compression point is generated according to the best point, the center point, the worst point and the reflection point in the complex shape as follows:
wherein u is g And u q Positions of global optimum and worst points, u, respectively r Is the position of the reflection point, beta is the shrinkage factor, F q-1 Is the fitness function value of the sub-difference point, F r Is reflectionFitness function value of a point, F q Is the fitness function value of the worst point; (ii) a
Step S525: if the compression point cannot update the worst point, optimizing the worst point according to the global optimum point and the local optimum point in the following way: u. of z ＝u q +β(u g -u q )+γ×r·(u b -u q ) R is equal to (-1, 1), wherein u z Is the position of the update point, u g Is the global optimum point, u b Is a local optimum, r is a random number between-1 and 1, γ is a spreading factor;
step S526: the steps S522 to S525 are repeated n times, and the steps S521 to S525 are repeated m times.
In order to make those skilled in the art further understand the technical solution proposed by the present invention, the following description is made with reference to specific examples.
Further, as shown in fig. 4, for the comparison between the proposed algorithm and the proposed ABC, STLBO, gotlparm, rcr-IJADE in the single diode model, RMSE represents the root mean square error of the objective function, and the smaller the value of RMSE, the smaller the error between the sample data between the measured data and the fitting data, i.e. the higher the accuracy of the parameter extraction process, MNFES means the maximum number of times of calculating the objective function, and the smaller the value, the better the convergence. It is evident from fig. 4 that the proposed ESCE-OBL has the same accuracy as the other algorithms, but the proposed ESCE-OBL is significantly better than the other algorithms at MNFES, indicating a better convergence, as compared specifically to SCE, as will be given in the following results.
Further, as shown in fig. 5, for the comparison of the proposed algorithm of the present invention with the proposed ABC, STLBO, gothbo, GOFPANM, rcr-IJADE under the two-diode model, it is obvious from fig. 5 that the proposed ESCE-OBL has the same accuracy as the other algorithms, but the proposed ESCE-OBL is significantly better than the other algorithms in MNFES to show that it has better convergence, and its comparison with SCE specifically will be given in the following results.
Further, as shown in fig. 6, for comparison of statistical methods of photovoltaic cell parameter extraction by SCE and ESCE-OBL under a single diode model, the results of the variance and the maximum value of ESCE-OBL are both smaller than SCE, which indicates that ESCE-OBL is more robust and stable under 5000 iterations of the single diode model.
Further, as shown in fig. 7, for comparison of statistical methods of photovoltaic cell parameter extraction by SCE and ESCE-OBL under a single-diode model, the results of variance and maximum value of ESCE-OBL are smaller than SCE, which indicates that ESCE-OBL is more robust and stable under 5000 iterations of a single-diode model.
Further, as shown in fig. 8, for the convergence curves of SCE and ESCE-OBL extracted on the photovoltaic cell parameters under the single-diode and double-diode model, the speed of ESCE-OBL is obviously faster than SCE, and the speed of ESCE-OBL decrease is faster than SCE, which shows that ESCE-OBL effectively improves the convergence of the conventional SCE algorithm.
The above are preferred embodiments of the present invention, and all changes made according to the technical solutions of the present invention that produce functional effects do not exceed the scope of the technical solutions of the present invention belong to the protection scope of the present invention.
Claims (2)
1. The photovoltaic array parameter extraction method based on reverse learning and enhanced complex evolution is characterized by comprising the following steps:
step S1: acquiring an actual I-V characteristic curve of a photovoltaic panel, and selecting a corresponding photovoltaic model;
step S2: determining an objective function of an optimization problem;
and step S3: optimizing the position of the initial point by adopting a reverse learning algorithm;
and step S4: extracting model parameters according to different circuit models by adopting an enhanced complex evolution algorithm;
step S5: extracting model parameters of the photovoltaic panel under different actual measurement conditions;
in step S2, the objective function, that is, the fitness function, is obtained according to the following method:
the root mean square error is recorded as:
wherein N is the total number of samples on the I-V characteristic curve, and f (V, I, X) is the absolute error between the measured current and the model estimated current;
f (V, I, X) for a single diode is:
wherein, I pv Is a photo-generated current, I o Is the saturation current of the diode, a is the ideality factor of the diode, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a boltzmann constant, T is an ambient temperature, q is an absolute value of an electron charge amount;
f (V, I, X) for a dual diode is:
wherein, I pv Is a photo-generated current, I o1 ,I o2 Saturation currents of two different diodes, a 1 ,a 2 Ideality factor, R, of two diodes, respectively s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is the boltzmann constant, T is the ambient temperature, q is the absolute value of the amount of electronic charge;
in step S3, the method further includes the steps of:
step S31: initializing corresponding control parameters, including: the number of complex shapes npg, the number of solutions in each complex shape nps, the total number of samples of candidate solutions npt, and the lower and upper bounds of the candidate solutions BL and BU;
step S32: randomly initializing npt candidate sample points in the lower and upper bounds BL, BU by the following process:
x i ＝BL+rand*(BU-BL),i＝1,2,...,npt
and calculating the adaptive function value of each position;
step S33: obtaining candidate solutions by reverse learningOpposite solution x o The process is as follows:
x o ＝BU+BL-x i
calculating fitness function value of the opposite solution, and dividing x o And x i Marking as a new candidate solution s after mixing, and performing ascending arrangement according to the size of the fitness function of the solution s;
in step S4, the method further includes the steps of:
step S41: partitioning the optimal npt candidate solutions into npg complex shapes;
step S42: evolving each complex form through an enhanced complex evolution algorithm to obtain the position of a new candidate solution;
step S43: replacing the original complex shape candidate points and the fitness function values corresponding to the original complex shape candidate points with the optimized points in the evolved complex shape;
step S44: when the calculation times of the target function reach the maximum, finishing the algorithm and returning to the optimal solution;
in step S42, the method further includes the steps of:
step S421: initializing a control parameter; the number of simplex peaks is recorded as d, the control coefficients are respectively alpha, beta and gamma, and the evolution times of primary and secondary complex shapes are recorded as (m, n);
step S422: d points are selected from npt points of the complex shape to be used as the vertexes of the ngs simplex shape, and the center points ce of the d points are calculated, wherein the calculation method is as follows:
wherein u is j Is the position of the d-1 simplex vertices;
step S423: obtaining corresponding reflection point u according to the central point r ＝ce-α(ce-u q ) After adopting boundary control, calculating a fitness function value of the reflection point; if the fitness function value of the reflection point is smaller than the optimal point in the complex shape, the numerical value of the extension point is calculated according to the following mode: u. of e ＝ce+γ·(ce-u q ) Replacing the worst position in the vertex by the best position in the reflection point and the expansion point;
step S424: when the reflection point is locatedWhen the optimal point difference in the complex shape is compared, generating a compression point according to the optimal point, the central point, the worst point and the reflection point in the complex shape as follows:
wherein u is g And u q The positions of the global optimum and worst points, u, respectively r Is the position of the reflection point, F q-1 Is the fitness function value of the sub-difference point, F r Is the fitness function value of the reflection point, F q Is the fitness function value of the worst point;
step S425: if the compression point cannot update the worst point, optimizing the worst point according to the global optimum point and the local optimum point in the following mode: u. of z ＝u q +β(u g -u q )+γ×r·(u b -u q ) R is equal to (-1, 1), wherein u z Is the position of the update point, u g Is the global optimum point, u b Is a local optimum, r is a random number between-1 and 1;
step S426: the steps S422 to S425 are repeated n times, and the steps S421 to S425 are repeated m times.
2. The photovoltaic array parameter extraction method based on inverse learning and enhanced complex evolution of claim 1, wherein in the step S1, the method further comprises the following steps:
step S11: collecting output current and voltage data of the photovoltaic panel under different illumination and irradiance conditions through a collecting plate, and respectively storing the data as a number table of N x 1;
step S12: selecting a single diode model and a double diode model as photovoltaic models;
the single diode model is:
wherein, I pv Is a photo-generated current, I o Is saturation of the diodeCurrent, a is the ideality factor of the diode, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a boltzmann constant, T is an ambient temperature, q is an absolute value of an electron charge amount;
the two-diode model is:
wherein, I pv Is a photo-generated current, I o1 ,I o2 Saturation currents of two different diodes, a 1 ,a 2 Ideal factor of two diodes, R s Is an equivalent parallel resistance, R sh Is an equivalent series resistance, K is a boltzmann constant, T is an ambient temperature, and q is an absolute value of an electron charge amount.
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