CN113868996B - Photovoltaic solar model parameter estimation method based on hierarchical Newton identification algorithm - Google Patents
Photovoltaic solar model parameter estimation method based on hierarchical Newton identification algorithm Download PDFInfo
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Abstract
The invention discloses a two-stage iteration parameter estimation method based on a single-diode photovoltaic cell model, which comprises the steps of constructing a two-stage Newton iteration parameter identification algorithm flow, constructing a two-stage Newton iteration algorithm and the like. The method is simple, convenient and efficient, and can be applied to parameter identification of the photovoltaic cell system.
Description
Technical Field
The invention relates to a two-stage iteration parameter estimation method of a photovoltaic cell model based on a layering identification principle.
Background
Photovoltaic cells are devices that convert solar energy into electrical energy by the photovoltaic effect. Has no exhaustion risk; absolute clean (no pollution, except for batteries); is not limited by the resource distribution region; the energy quality is high; the time spent for obtaining the energy is short; the power supply system is reliable in operation and the like. The reaction of the photovoltaic cell has the characteristics of nonlinearity, random interference, multiple input and multiple output and the like. And the photovoltaic cell is greatly influenced by radiation intensity and temperature, so that the measurement and analysis of the photovoltaic cell are difficult. In contrast, the photovoltaic cell is modeled by an identification method, and parameters of the photovoltaic cell can be estimated better by means of the model.
The two-stage newton iterative algorithm can divide the model into linear and nonlinear parts and gradually linearize the nonlinear parts. The algorithm can play an important role in the aspects of design, performance evaluation, real-time maximum power point tracking and the like of a photovoltaic system by simplifying a photovoltaic cell model. The two-stage Newton iteration parameter identification method can simplify calculation, reduce calculation amount, improve efficiency and be easier to realize. Therefore, the invention adopts a two-stage Newton iterative algorithm to carry out parameter identification on the single-diode photovoltaic cell.
The invention aims to identify photovoltaic cell parameters by using a photovoltaic cell model based on a layering identification principle.
The solution of the invention is as follows:
(1) The identification model of the single diode photovoltaic cell model is built, and the specific steps are as follows:
the first step: the single diode equivalent circuit model for the photovoltaic module was constructed as follows:
See figure 1;
and a second step of: according to the model, a single-diode photovoltaic cell model output current is constructed:
the expression:
I＝Iph-I0[exp(bVh(V+RsI))-1]-GshV-ρI (1)
the meaning of each symbol in the above formula: i ph is the photocurrent generated by the incident light, I 0 is the dark saturation current, b: =1/a, V h：＝1/Vth,Rs is the series resistance, G sh：＝1/Rsh,ρ：＝GshRs, a is the diode management ideal coefficient, V th：＝q/Ns kT is the p-N junction thermal voltage, R sh is the shunt resistance, N s is the number of cells in series, K is the boltzmann constant, q is the electron charge, and T is the p-N junction temperature in kelvin.
And a third step of: constructing a single-diode photovoltaic cell model virtual current:
Expression 1:
I1(t)：＝I(t)+I0[exp(bVh(V(t)+RsI(t)))-1] (2)
Expression 2:
I2(t)：＝I(t)-Iph+Gsh(V(t)+RsI(t)) (3)
Fourth step: two sub-identification models of a single-diode photovoltaic cell model are constructed:
sub-recognition model 1:
I1(t)＝Iph-GshV(t)-ρI(t) (4)
Sub-recognition model 2:
I2(t)＝-I0[exp(bVh(V(t)+RsI(t)))-1] (5)
fifth step: two sub-identification models of the single diode photovoltaic cell model are obtained:
sub-recognition model 1:
Sub-recognition model 2:
(2) Construct a two-stage Newton iterative algorithm flow
The first step: starting an identification algorithm;
And a second step of: let iteration number l=0, set up the initial value;
and a third step of: acquiring input voltage data of a single-diode photovoltaic cell as input data, and outputting current data as output data;
Fourth step: structure of the device And/>
Fifth step: structure of the deviceAnd/>
Sixth step: structure of the deviceAnd/>
Seventh step: calculation ofAnd/>Matrix elements;
Eighth step: construction of sea plug matrix
Ninth step: calculation ofAnd/>
Tenth step: if it isThen l=l+1 repeats the above procedure, otherwise, getsAnd/>Ending the flow.
Wherein the variables are defined as follows:
defining the input quantity as V (t) and the output quantity as I (t);
Defining V (L) as an information vector formed by input quantity, I (L) as an information vector formed by output quantity, and L as a data length;
Definition of the definition And/>Is a related information vector;
Definition of sea plug matrix Is an iteration matrix;
Defining theta 1 and theta 2 as parameter vectors;
Definition of the definition An estimate for the L-th iteration of θ 1; /(I)An estimate for the L-th iteration of θ 2; /(I)Is thatAn estimated value of the L th iteration; /(I)For/>An estimated value of the L th iteration; /(I)And/>Estimated values for f 1(θ1, t) and f 2(θ2, t) the L-th iteration, respectively; /(I)And/>Respectively express/>And/>An estimated value at the L-th iteration;
(3) According to the flow of the two-stage Newton iterative algorithm, the two-stage Newton iterative algorithm is constructed as follows:
I(L)＝[I(1)，I(2)，…，I(L)]T， (10)
The specific steps of the algorithm are as follows:
(1) Starting an identification algorithm, enabling L=0, and setting an initial value Wherein p 0＝106 is a significant number;
(2) And acquiring the input voltage of the single-diode photovoltaic cell as input data, taking the output current of the single-diode photovoltaic cell as output data, and performing data preprocessing.
(3) Obtained by the formula (14), the formula (15), the formula (16), the formula (12), the formula (13), the formula (17) and the formulas (19) - (22) Matrix elements;
(4) Construction of sea plug matrix with (18)
(5) Calculated by the formula (8) and the formula (9)And/>
(6) If it isThen l=l+1 repeats the above procedure, otherwise, get/>AndEnding the flow.
Wherein the variables are defined as follows:
defining the input quantity as V (t) and the output quantity as I (t);
Defining V (L) as an information vector formed by input quantity, I (L) as an information vector formed by output quantity, and L as a data length;
Definition of the definition And/>Is a related information vector;
Definition of sea plug matrix Is an iteration matrix;
Defining theta 1 and theta 2 as parameter vectors;
Definition of the definition An estimate for the L-th iteration of θ 1; /(I)An estimate for the L-th iteration of θ 2; /(I)Is thatAn estimated value of the L th iteration; /(I)For/>An estimated value of the L th iteration; /(I)And/>Estimated values for f 1(θ1, t) and f 2(θ2, t) the L-th iteration, respectively; /(I)And/>Respectively express/>And/>An estimated value at the L-th iteration;
The method is accurate in calculation and suitable for parameter identification based on the single-diode photovoltaic cell model.
Drawings
The invention is further described below with reference to the drawings and examples.
Fig. 1 is a single diode equivalent circuit of a photovoltaic module.
FIG. 2 is a flow chart of a Newton iteration parameter identification algorithm of the model.
Fig. 3 to 6 are schematic diagrams showing specific examples of the method of the present invention. Fig. 3 shows the relative error of parameter estimation, and fig. 4 shows the relative error of I sc、Voc、Vm、Im and P m. Fig. 5 and 6 are diagrams obtained by performing data processing using a large number of collected voltage data, current data and power data of a single-diode photovoltaic cell as input data, and data corresponding to actual parameters, and then comparing the calculated data with the data corresponding to actual parameters by using the parameter identification algorithm.
Claims (1)
1. The photovoltaic solar model parameter estimation method based on the hierarchical Newton identification algorithm is characterized by comprising the following steps of: comprises the following steps:
(1) The identification model of the single diode photovoltaic cell model is built, and the specific steps are as follows:
the first step: constructing a single diode equivalent circuit model for the photovoltaic module;
And a second step of: according to the system, a single-diode photovoltaic cell model output current expression is constructed as follows:
I＝Iph-I0[exp(bVh(V+RsI))-1]-GshV-ρI， (1)
The meaning of each symbol in the above formula: i ph is the photocurrent generated by the incident light, I 0 is the dark saturation current, b: =1/a, V h：＝1/Vth,Rs is the series resistance, G sh：＝1/Rsh,ρ：＝GshRs, a is the diode management ideal coefficient, V th：＝q/Ns kT is the p-N junction thermal voltage, R sh is the shunt resistance, N s is the number of cells in series, K is the boltzmann constant, q is the electron charge, T is the p-N junction temperature in kelvin;
And a third step of: the single diode photovoltaic cell model virtual current and expression are constructed as follows:
I1(t)：＝I(t)+I0[exp(bVh(V(t)+RsI(t)))-1]， (2)
I2(t)：＝I(t)-Iph+Gsh(V(t)+RsI(t))， (3)
Fourth step: two sub-identification models of the single diode photovoltaic cell model are constructed as follows:
I1(t)＝Iph-GshV(t)-ρI(t)， (4)
I2(t)＝-I0[exp(bVh(V(t)+RsI(t)))-1]， (5)
fifth step: two sub-identification models of the single diode photovoltaic cell model are obtained:
(2) Aiming at a single-diode photovoltaic cell model, a two-stage Newton iteration parameter identification algorithm for effectively solving the problem of a photovoltaic solar model is provided
The first step: starting an identification algorithm;
And a second step of: let iteration number l=0, set up the initial value;
and a third step of: acquiring input voltage data of a single-diode photovoltaic cell as input data, and outputting current data as output data;
Fourth step: structure of the device And/>
Fifth step: structure of the deviceAnd/>
Sixth step: structure of the deviceAnd/>
Seventh step: calculation ofAnd/>Matrix elements;
Eighth step: construction of sea plug matrix
Ninth step: if it isThen l=l+1 repeats the above procedure, otherwise, get/>AndEnding the flow;
Wherein the variables are defined as follows:
defining the input quantity as V (t) and the output quantity as I (t);
Defining V (L) as an information vector formed by input quantity, I (L) as an information vector formed by output quantity, and L as a data length;
Definition of the definition And/>Is a related information vector;
Definition of sea plug matrix Is an iteration matrix;
Defining theta 1 and theta 2 as parameter vectors;
Definition of the definition An estimate for the L-th iteration of θ 1; /(I)An estimate for the L-th iteration of θ s; /(I)For/>An estimated value of the L th iteration; /(I)For/>An estimated value of the L th iteration; /(I)And/>Estimated values for f 1(θ1, t) and f 2(θ2, t) the L-th iteration, respectively; /(I)And/>Respectively express/>And/>An estimated value at the L-th iteration; the two-stage iteration parameter estimation method based on the single-diode photovoltaic cell model comprises the following steps of:
I(L)＝[I(1)，I(2)，…，I(L)]T， (10)
The specific steps of the algorithm are as follows:
(1) Starting an identification algorithm, enabling L=0, and setting an initial value Wherein p 0＝106 is a significant number;
(2) Acquiring input voltage of a single-diode photovoltaic cell as input data, taking output current of the single-diode photovoltaic cell as output data, and performing data preprocessing;
(3) Obtained by the formula (14), the formula (15), the formula (16), the formula (12), the formula (13), the formula (17) and the formulas (19) - (22) Matrix elements;
(4) Construction of sea plug matrix with (18)
(5) Calculated by the formula (8) and the formula (9)And/>
(6) If it isThen l=l+1 repeats the above procedure, otherwise, get/>And/>Ending the flow;
Wherein the variables are defined as follows:
defining the input quantity as V (t) and the output quantity as I (t);
Defining V (L) as an information vector formed by input quantity, I (L) as an information vector formed by output quantity, and L as a data length;
Definition of the definition And/>Is a related information vector;
Definition of sea plug matrix Is an iteration matrix;
Defining theta 1 and theta 2 as parameter vectors;
Definition of the definition An estimate for the L-th iteration of θ 1; /(I)An estimate for the L-th iteration of θ 2; /(I)For/>An estimated value of the L th iteration; /(I)For/>An estimated value of the L th iteration; /(I)And/>Estimated values for f 1(θ1, t) and f 2(θ2, t) the L-th iteration, respectively; /(I)And/>Respectively express/>And/>An estimate at the L-th iteration.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN105786761A (en) * | 2016-03-25 | 2016-07-20 | 南通大学 | Maximum likelihood and Newton's iteration identification algorithm for input nonlinear colored noise system |
| CN109635506A (en) * | 2019-01-10 | 2019-04-16 | 浙江大学 | The photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm |
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| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN105786761A (en) * | 2016-03-25 | 2016-07-20 | 南通大学 | Maximum likelihood and Newton's iteration identification algorithm for input nonlinear colored noise system |
| CN109635506A (en) * | 2019-01-10 | 2019-04-16 | 浙江大学 | The photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm |
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