CN113595132B - Photovoltaic online parameter identification method based on maximum power point and hybrid optimization algorithm - Google Patents
Photovoltaic online parameter identification method based on maximum power point and hybrid optimization algorithm Download PDFInfo
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Abstract
The application relates to a photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm, which firstly provides a mutation point detection algorithm for detecting waveform mutation time in a dynamic working point global maximum power point tracking process, converts a waveform corresponding to GMPPT into a static key segment I-V characteristic curve, and provides a novel hybrid method, namely a photovoltaic model online parameter identification method based on a quantum particle swarm algorithm and a Levenberg-Marquard algorithm.
Description
Technical Field
The application relates to the technical field of detection of solar cells and photovoltaic arrays, in particular to a photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm.
Background
In order to cope with the problems of increasingly serious environmental pollution, climate deterioration, exhaustion of fossil energy, etc., solar energy has received a great deal of attention as a renewable and widely available clean energy. The photovoltaic array is used as a core of a photovoltaic power generation device and is generally formed by connecting photovoltaic modules in series and parallel. However, the photovoltaic modules and arrays generally operate in a complex outdoor environment, and from the viewpoint of long-term operation, the performance of the photovoltaic modules is not only reduced with the increase of service life, resulting in the change of model parameters with time, but also failure caused by the influence of severe natural environment. Therefore, the method has important application value and practical significance for the performance evaluation and real-time photovoltaic fault diagnosis of the whole power generation system under the offline actual measurement condition and the grid-connected power generation condition of the dynamic working point I-V characteristic modeling and parameter identification of the photovoltaic module.
The equivalent models of the photovoltaic module and the array are mainly divided into a single-diode five-parameter model and a double-diode seven-parameter model, and accurately and quickly identifying model parameters is a key of photovoltaic modeling. According to the mutation point detection method, the I-V characteristic modeling of grid connection on line is realized through the extraction from dynamic waveforms to static I-V characteristics; the parameter extraction method can be used for statically actually measuring the I-V characteristic curve and dynamically extracting the key section I-V characteristic curve from the working point, can extract corresponding parameters of the photovoltaic array model, and can update the photovoltaic array model on line by combining the inverter grid-connected MPPT scanning process, so that the actual working condition of the photovoltaic power station can be effectively evaluated.
Currently existing photovoltaic model parameter extraction methods can be generally divided into three types, namely an analytical method, a numerical optimization method and a mixed method of the two. The analytical method mainly solves the parameters of the photovoltaic model by constructing an explicit equation of the photovoltaic model based on few key point data (such as open-circuit voltage, short-circuit current, maximum power point voltage, current and temperature coefficient) given by or actually measured by a photovoltaic module manufacturer. Although the method is simple and has small calculation amount, the accuracy of model parameters is poor and is easily influenced by the accuracy and noise of the key point data. In order to overcome the shortcomings of the analytical methods, various deterministic and random numerical optimization methods are sequentially proposed, which accurately extract model parameters by minimum simulation and root mean square error of the measured I-V curve. Deterministic numerical optimization methods include Newton-Raphson methods, pattern search methods, and the like. Although the method has high convergence speed and small calculation amount, the method is easy to sink into a local optimal value, and the precision of model parameters is easy to be influenced by a searching starting point and is relatively unstable. The random numerical optimization method for extracting the photovoltaic model parameters mainly comprises differential evolution, genetic algorithm, bee colony algorithm, particle swarm algorithm, pollen propagation algorithm and the like. The algorithm has strong global searching capability, but has large calculation amount and slow convergence speed, and is difficult to be suitable for real-time parameter extraction. In order to take advantage of the advantages of such methods while overcoming the disadvantages thereof, some hybrid methods have been proposed, including a cuckoo-to-simplex method, an analytical method-to-simplex method, an artificial bee colony-to-trust domain algorithm, and the like. There are some problems with the above, including the following: firstly, few algorithms proposed in the past are transplanted into a hardware platform to extract parameters, and the main reason is that the convergence speed is not improved substantially, and the algorithm is high in complexity and cannot be realized on the hardware platform due to excessive iterative times of the algorithm; secondly, the algorithm proposed in the past is only suitable for static complete I-V characteristic curves, and cannot realize the on-line extraction of parameters in the grid-connected power generation process. At present, a photovoltaic model online parameter identification method based on mutation point detection and mixed quantum particle swarm optimization and a Levenberg-Marquardt algorithm is not found in published documents and patents, the mutation point detection method is also applied to the first time of extracting a critical section I-V curve in the process of scanning a GMPPT of a photovoltaic grid-connected inverter, and the mixed optimization algorithm is also applied to the first time of extracting parameters of the photovoltaic model.
Disclosure of Invention
In view of the above, the application aims to provide a photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm, so as to overcome the defects of the prior art, thereby realizing grid-connected online parameter extraction and improving the performance of photovoltaic model parameter identification.
The application is realized by adopting the following scheme: a photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm comprises the following steps:
step S1: according to the time sequence voltage and current waveforms of the dynamic working points of the photovoltaic inverter, identifying the maximum power point tracking step by adopting a mutation point detection algorithm, recording waveform mutation time, and extracting the voltage and current waveforms in a steady state;
step S2: according to the voltage step length change characteristics in the voltage and current waveforms, identifying a global maximum power point tracking process, namely a waveform of GMPPT, and extracting a key segment I-V characteristic curve of the waveform corresponding to the GMPPT;
step S3: tracking and extracting the obtained I-V characteristic curve and the number N of solar cells connected in series and in parallel of the photovoltaic array according to the global maximum power point s And N p Selecting a single diode five-parameter model for parameter identification, and extracting five electrical parameters of photocurrent, single diode reverse saturation current, ideal factors, equivalent series resistance and equivalent parallel resistance in the model according to the search range of the electrical parameters of the model;
step S4: performing global search on parameters of the photovoltaic model by adopting a quantum particle swarm intelligent optimization algorithm, and acquiring an optimal initial value vector of the parameters of the photovoltaic model;
step S5: adopting a Levenberg-Marquardt algorithm, and carrying out further local search by using the optimal photovoltaic model parameter vector obtained in the step S4 as an initial search parameter;
step S6: carrying out equivalent single diode model parameter extraction on two types of photovoltaic data through a mixed quantum particle swarm algorithm and a Levenberg-Marquardt algorithm, namely a QPSO-LM algorithm; the two types of photovoltaic data are static complete I-V characteristic curves and key section I-V curves extracted in the dynamic working point global maximum power point tracking process under different actual measurement conditions.
Further, according to the collection frequency of the dynamic working point data, the mutation point detection algorithm in step S1 uses each continuous 200 photovoltaic array working voltages as a set, randomly selects a point to divide the set into two parts, calculates residuals of average values of points at two sides and each part, and can find a mutation point when the total residuals reach a minimum value, where the mutation point detection algorithm is as follows:when J gets the minimum value, recordV-recording r＝k And the time node is the moment of the abrupt change of the voltage and current waveform of the dynamic working point.
Further, in the step S2, the global maximum power point tracking process is a disturbance observation method adopted for the inverter operation, and the disturbance voltage step size range is 0.2V to 4V. The output voltage of the photovoltaic array is disturbed according to the voltage step length of 4V between the lowest working voltage 80V of the inverter and the normal working voltage 120V of the photovoltaic array, the GMPPT process is realized, the corresponding voltage and current are recorded according to the abrupt point moment, and the key section I-V curve of the GMPPT process is extracted.
Further, in the step S3, the photovoltaic model is a single diode five-parameter model; the mathematical model of the single diode photovoltaic module is shown as follows:
the mathematical model of a single diode photovoltaic array is as follows:
wherein I is t And V t Current and voltage values in the measured I-V curve; k is Boltzmann constant 1.3806503 ×10 -23 J/K, q is the basic charge 1.60217646 ×10 -19 C, performing operation; five parameter vectors of single diode photovoltaic model are [ I ] ph ,I s ,n,R s ,R p ]Wherein I ph Is photocurrent, I s Is the reverse saturation current of a single diode, n is the idealized factor of the single diode, R s Equivalent series resistance R p Is equivalent to parallel resistance N s And N p The number of cells in series and parallel, respectively.
Further, the algorithm in steps S4 and S5 finds an optimal set of parameter vectors X within the search range of a given parameter vector so that the following formula is shown:
taking the sum variance SSE as an objective function of a hybrid optimization algorithm (QPSO-LM), and when the value of the sum variance SSE is minimum, enabling the actually measured I-V curve to be in optimal fit with the calculated simulation I-V curve, namely representing the actually measured I-V curve to be in optimal fit with the calculated simulation I-V curve.
Further, the hybrid algorithm (QPSO-LM) uses a range of parameter vectors for its single diode cell model for reference data by being within a range of given parameter vectors: i ph ∈[0,1],I s ∈[0,1],n∈[1,2],R s ∈[0,0.5],R p ∈[0,100]The method comprises the steps of carrying out a first treatment on the surface of the Single diode photovoltaic module: i ph ∈[0,2],I s ∈[0,50],n∈[1,50],R s ∈[0,2],R p ∈[0,2000]。
Further, the step S4 specifically includes the following steps:
step S41: according toIs the D-dimensional parameter vector of the search space, D is the number of parameters to be optimized, LB j ,UB j Respectively the upper and lower bounds of the parameters;
step S42: setting population number N of particles p And the maximum iteration number IterMax, calculating the fitness value of each particle in the particle swarm, wherein a fitness value calculation formula is an objective function, namely the value of a calculation sum variance SSE;
step S43: initializing the iteration number Iter of particles to be 1, and entering an iterative search process;
step S44: calculating a particle historical best position average value m best The calculation formula is as follows:
m is the population size, p best_i Representing the current stackExtremum of the ith particle of the generation;
step S45: updating a formula according to the particle position:
calculating the optimal solution of the current particle in the search space;
step S46: calculating fitness values of the population and the individuals, judging whether the iteration number Iter is smaller than or equal to the maximum iteration number IterMax set by the algorithm, if so, adding 1 to the current iteration number Iter, and re-entering step S44; otherwise, sorting the fitness value obtained by the last iterative calculation and outputting an optimal initial solution vector X 0 。
Further, the step S5 specifically includes the following steps:
step S51: the optimal initial solution vector X obtained according to step S4 0 Calculating an objective function SSE and recording as F (X), wherein a recording vector F (X) is the difference between an actual measurement value and a calculated value at a solution vector X, and a Jacobian matrix J (X) of the F (X) is calculated;
step S52: setting an iteration parameter Iter=0, a maximum objective function calculation value MaxFunEvals and a maximum iteration number MaxIter; an optimality tolerance value tol;
step S53: the gradient vector G (x) of f (x) is calculated, and the calculation formula of G (x) is as follows: g (x) =2j (x) T F (x); the Hessian matrix H (x) of f (x) is calculated, and the calculation formula of H (x) is as follows: h (x) =2j (x) T J (x) +2Q (x); wherein,D i (x) For each F i (x) Is a Hessian matrix of (2);
step S54: the algorithm performs an iteration according to the equation: (J (x) k ) T J(x k )+λ k I)d k ＝-J(x k ) T F(x k ) Calculate the search direction d k The direction is the solution of the linear least square problem;
step S55: boundary detection, judging whether the iteration point x is in a constraint boundary, if the iteration point x is out of the boundary, projecting the step to the nearest feasible point by an algorithm, and modifying the iteration point x into P (x) by the algorithm, wherein the updating method of P (x) comprises the following steps: p (x) =lb if x < LB; p (x) =ubif x > UB; p (x) =xotherwise;
step S56: calculating whether the objective function SSE, i.e. f (x), satisfies the algorithm termination condition 1: or termination condition 2: iter>And if not, continuing to add 1 to the Iter, and proceeding to step S54 for iterative computation until reaching a termination condition, and outputting a final result.
Compared with the prior art, the application has the following beneficial effects:
the application extracts a critical section I-V characteristic curve in the process of scanning the inverter GMPPT by identifying the abrupt change moment of a dynamic working point time sequence voltage current waveform; the photovoltaic model online parameter identification method of the hybrid quantum particle swarm optimization algorithm is characterized in that a standard data set and an actual measurement data set are used for verifying an integrated I-V characteristic curve under static conditions and a key section I-V characteristic curve extracted in a dynamic working point GMPPT process, and an optimal initial point is selected by adopting a Quantum Particle Swarm Optimization (QPSO) algorithm, so that the problem of slow convergence caused by the influence of the initial point selection on the basis of a Levenberg-Marquardt algorithm (LM) is avoided. In summary, compared with the existing photovoltaic model parameter identification algorithm, the photovoltaic grid-connected online modeling is realized, and the speed, precision, reliability, convergence and stability of the photovoltaic model parameter extraction are greatly improved.
Drawings
Fig. 1 is a general flow chart of a photovoltaic model online parameter identification method according to an embodiment of the present application.
Fig. 2 is an equivalent circuit diagram of a photovoltaic module and a photovoltaic array or string in a single diode module according to an embodiment of the present application, wherein fig. 2 (a) is a single diode module equivalent model diagram, and fig. 2 (b) is a single diode array or string equivalent model.
Fig. 3 is a schematic diagram of a photovoltaic array, a dynamic operating point voltage and current acquisition system and an inverter according to an embodiment of the present application, where fig. 3 (a) is a 3*6 photovoltaic array diagram, fig. 3 (b) is a data acquisition system physical diagram, and fig. 3 (c) is a photovoltaic inverter physical diagram.
FIG. 4 is a graph of voltage and current waveform mutation point detection during the GMPPT process of the inverter dynamic operating point according to an embodiment of the application, wherein FIG. 4 (a) is irradiance 582W/m 2 Schematic of mutation point detection under working conditions, FIG. 4 (b) shows irradiance 748W/m 2 Schematic diagram of mutation point detection under working conditions. Fig. 5 is a graph and a fitted graph of a critical section I-V extracted from a GMPPT scanning process based on mutation point detection according to an embodiment of the present application, wherein fig. 5 (a) is a graph of a critical section I-V extracted from a GMPPT process under different irradiance, and fig. 5 (b) is a fitted graph of a critical section I-V curve. Fig. 6 is a comparison chart of the convergence speed of the parameter extraction result under the single diode model based on the reference data according to the embodiment of the present application and the existing algorithm, wherein fig. 6 (a) is a comparison chart of the convergence speed of different algorithms on the rtc.france data set, and fig. 6 (b) is a comparison chart of the convergence speed of different algorithms on the photostat-PWP 201 data set. Fig. 7 is a graph of the parameter extraction result of the single diode model on the measured photovoltaic array under different conditions according to the embodiment of the present application, wherein fig. 7 (a) is an I-V characteristic curve fitting graph, which is the parameter extraction of the measured photovoltaic array, and fig. 7 (b) is a P-V characteristic curve fitting graph of the measured photovoltaic array.
Detailed Description
The application will be further described with reference to the accompanying drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiment provides a photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm, which comprises the following steps:
step S1: according to the time sequence voltage and current waveforms of the dynamic working points of the photovoltaic inverter, identifying the maximum power point tracking step by adopting a mutation point detection algorithm, recording waveform mutation time, and extracting the voltage and current waveforms in a steady state;
step S2: according to the voltage step length change characteristics in the voltage and current waveforms, identifying a global maximum power point tracking process, namely a waveform of GMPPT, and extracting a key segment I-V characteristic curve of the waveform corresponding to the GMPPT;
step S3: tracking and extracting the obtained I-V characteristic curve and the number N of solar cells connected in series and in parallel of the photovoltaic array according to the global maximum power point s And N p The method comprises the steps of carrying out a first treatment on the surface of the Selecting a single diode five-parameter model for parameter identification, and extracting five electrical parameters of photocurrent, single diode reverse saturation current, ideal factors, equivalent series resistance and equivalent parallel resistance in the model according to the search range of the electrical parameters of the model;
step S4: performing global search on parameters of the photovoltaic model by adopting a quantum particle swarm intelligent optimization algorithm, and acquiring an optimal initial value vector of the parameters of the photovoltaic model;
step S5: adopting a Levenberg-Marquardt algorithm, and carrying out further local search by using the optimal photovoltaic model parameter vector obtained in the step S4 as an initial search parameter;
step S6: carrying out equivalent single diode model parameter extraction on two types of photovoltaic data through a mixed quantum particle swarm algorithm and a Levenberg-Marquardt algorithm, namely a QPSO-LM algorithm; the two types of photovoltaic data are static complete I-V characteristic curves and key section I-V curves extracted in the dynamic working point global maximum power point tracking process under different actual measurement conditions.
In this embodiment, the mutation point detection algorithm in step S1 uses each continuous 200 photovoltaic array operating voltages as a set according to the acquisition frequency of the dynamic operating point data, randomly selects a point to divide the set into two parts, calculates the residual error of the average value of each point and each part on both sides, and when the total residual error reaches the minimum value,
the mutation points can be found, and the mutation point detection algorithm is shown as the following formula:
when J takes the minimum value, record V r＝k And the time node is the moment of the abrupt change of the voltage and current waveform of the dynamic working point.
In this embodiment, in the step S2, the global maximum power point tracking process is a disturbance observation method adopted for the inverter to work, and the value range of the disturbance voltage is 0.2V to 4V; the output voltage of the photovoltaic array is disturbed according to the voltage step length of 4V between the lowest working voltage 80V of the inverter and the normal working voltage 120V of the photovoltaic array, the GMPPT process is realized, the corresponding voltage and current are recorded according to the abrupt point moment, and the key section I-V curve of the GMPPT process is extracted.
In this embodiment, the photovoltaic model in the step S3 is a single diode five-parameter model; the mathematical model of the single diode photovoltaic module is shown as follows:
the mathematical model of a single diode photovoltaic array is as follows:
wherein I is t And V t Current and voltage values in the measured I-V curve; k is Boltzmann constant 1.3806503 ×10 -23 J/K，q1.60217646 ×10 as the basic charge quantity -19 C, performing operation; five parameter vectors of single diode photovoltaic model are [ I ] ph ,I s ,n,R s ,R p ]Wherein I ph Is photocurrent, I s Is the reverse saturation current of a single diode, n is the idealized factor of the single diode, R s Equivalent series resistance R p Is equivalent to parallel resistance N s And N p The number of cells in series and parallel, respectively.
In this embodiment, the algorithm in steps S4 and S5 finds an optimal set of parameter vectors X within the search range of a given parameter vector so that the following formula is shown:
and taking the square sum error SSE as an objective function of a hybrid optimization algorithm (QPSO-LM), and when the value of the square sum error SSE is minimum, enabling the actually measured I-V curve to be in optimal fit with the calculated simulation I-V curve, namely representing the actually measured I-V curve to be in optimal fit with the calculated simulation I-V curve.
In this embodiment, the hybrid algorithm (QPSO-LM) uses the parameter vector range of its single diode cell model for reference data by being within the range of the given parameter vector: i ph ∈[0,1],I s ∈[0,1],n∈[1,2],R s ∈[0,0.5],R p ∈[0,100]The method comprises the steps of carrying out a first treatment on the surface of the Single diode photovoltaic module: i ph ∈[0,2],I s ∈[0,50],n∈[1,50],R s ∈[0,2],R p ∈[0,2000]。
In this embodiment, the step S4 specifically includes the following steps:
step S41: according to Is the D-dimensional parameter vector of the search space, D is the number of parameters to be optimized, LB j ,UB j Respectively the upper and lower bounds of the parameters;
step S42: setting population number N of particles p And the maximum iteration number IterMax, calculating the fitness value of each particle in the particle swarm, wherein a fitness value calculation formula is an objective function, namely the value of a calculation sum variance SSE;
step S43: initializing the iteration number Iter of particles to be 1, and entering an iterative search process;
step S44: calculating a particle historical best position average value m best ，
The calculation formula is as follows:
m is the population size, p best_i An extremum representing the ith particle of the current iteration;
step S45: updating a formula according to the particle position:
calculating the optimal solution of the current particle in the search space;
step S46: calculating fitness values of the population and the individuals, judging whether the iteration number Iter is smaller than or equal to the maximum iteration number IterMax set by the algorithm, if so, adding 1 to the current iteration number Iter, and re-entering step S44; otherwise, sorting the fitness value obtained by the last iterative calculation and outputting an optimal initial solution vector X 0 。
In this embodiment, the step S5 specifically includes the following steps:
step S51: the optimal initial solution vector X obtained according to step S4 0 Calculating an objective function SSE and recording as F (X), wherein a recording vector F (X) is the difference between an actual measurement value and a calculated value at a solution vector X, and a Jacobian matrix J (X) of the F (X) is calculated;
step S52: setting an iteration parameter Iter=0, a maximum objective function calculation value MaxFunEvals and a maximum iteration number MaxIter; an optimality tolerance value tol;
step S53: the gradient vector G (x) of f (x) is calculated, and the calculation formula of G (x) is as follows: g (x) =2j (x) T F (x); the Hessian matrix H (x) of f (x) is calculated, and the calculation formula of H (x) is as follows: h (x) =2j (x) T J (x) +2Q (x); wherein,D i (x) For each F i (x) Is a Hessian matrix of (2);
step S54: the algorithm performs an iteration according to the equation: (J (x) k ) T J(x k )+λ k I)d k ＝-J(x k ) T F(x k ) Calculate the search direction d k The direction is the solution of the linear least square problem;
step S55: boundary detection, judging whether the iteration point x is in a constraint boundary, if the iteration point x is out of the boundary, projecting the step to the nearest feasible point by an algorithm, and modifying the iteration point x into P (x) by the algorithm, wherein the updating method of P (x) comprises the following steps: p (x) =lb if x < LB; p (x) =ubif x > UB; p (x) =x other;
step S56: calculating whether the objective function SSE, i.e. f (x), satisfies the algorithm termination condition 1: or termination condition 2: iter>And if not, continuing to add 1 to the Iter, and proceeding to step S54 for iterative computation until reaching a termination condition, and outputting a final result.
Preferably, the embodiment provides a mutation point detection algorithm for detecting the waveform mutation time of the dynamic working point global maximum power point tracking process, converts the waveform corresponding to GMPPT into a static critical section I-V characteristic curve, and provides a novel hybrid method, namely a photovoltaic model on-line parameter identification method based on a quantum particle swarm algorithm and a Levenberg-Marquardt algorithm.
The photovoltaic online parameter identification method based on the global maximum power point tracking and hybrid optimization algorithm (QPSO-LM) comprises the following steps: step S1: according to the time sequence voltage and current waveforms of the dynamic working points of the photovoltaic inverter, identifying the maximum power point tracking step by adopting a mutation point detection algorithm, recording waveform mutation time, and extracting the voltage and current waveforms in a steady state; step S2: according to the voltage step length change characteristics in the voltage and current waveforms, identifying the waveform of a global maximum power point tracking process (GMPPT), and extracting a key segment I-V characteristic curve of the waveform corresponding to the GMPPT; step S3: tracking and extracting the obtained I-V characteristic curve and the number N of solar cells connected in series and in parallel of the photovoltaic array according to the global maximum power point s And N p Selecting a proper photovoltaic equivalent model and a searching range of model electrical parameters; step S4: performing global search on photovoltaic model parameters by adopting a quantum particle swarm intelligent optimization algorithm, and acquiring an optimal photovoltaic model parameter initial value vector; step S5: adopting a Levenberg-Marquardt algorithm, and carrying out further local search by using the optimal photovoltaic model parameter vector obtained in the step S4 as an initial search parameter; step S6: and extracting parameters of a single diode model of two types of photovoltaic data (static complete I-V characteristic curves under different actual measurement conditions) through the hybrid optimization algorithm, wherein the dynamic working point is a key segment I-V curve extracted in the global maximum power point tracking process.
The specific flow diagram is shown in figure 1. Fig. 2 is a single diode equivalent circuit model of the photovoltaic array module or the battery of the present embodiment, wherein fig. 2a is a single diode module equivalent model, and fig. 2b is a single diode array or string equivalent model. The abrupt change point detection refers to the moment of detecting voltage abrupt change in the dynamic working point GMPPT scanning process; the photovoltaic model refers to a single diode five-parameterA numerical model; the photovoltaic model parameters refer to photocurrent I ph Diode reverse saturation current I s Diode idealization factor n, equivalent series resistance R s And equivalent parallel resistance R p 。
The mathematical model of the single diode photovoltaic module is shown as follows:
the mathematical model of a single diode photovoltaic array or string is as follows:
wherein I is t And V t Current and voltage values in the measured I-V curve; k is Boltzmann constant (1.3806503 ×10) -23 J/K), q is the basic charge (1.60217646 ×10) -19 C) The method comprises the steps of carrying out a first treatment on the surface of the Five parameter vectors of single diode photovoltaic model are [ I ] ph ,I s ,n,R s ,R p ]Wherein I ph Is photocurrent, I s Is the reverse saturation current of a single diode, n is the idealized factor of the single diode, R s Equivalent series resistance R p Is equivalent to parallel resistance N s And N p The number of cells in series and parallel, respectively. The algorithm proposed in this embodiment finds an optimal set of parameter vectors X within the search range of a given parameter vector so that the following formula is shown:
in this embodiment, the Sum of Squares Error (SSE) is used as an objective function of the hybrid optimization algorithm, and when the value of the Sum of Squares Error (SSE) is minimum, the measured I-V curve and the calculated simulation I-V curve are optimally fitted, that is, the best fit between the measured I-V curve and the calculated simulation I-V curve is represented.
In the step S1, the dynamic operating point voltage and current waveform is first low-pass filtered to filter out the 50Hz power frequency interference signal, then each continuous 200 photovoltaic array operating voltages are used as a set according to the acquisition frequency of the dynamic operating point data, the set is randomly divided into two parts by selecting one point, the residual error of the average value of each point and each part on both sides is calculated, when the total residual error reaches the minimum value, the mutation point can be found, and the mutation point detection algorithm is shown in the following formula: when J takes the minimum value, record V r＝k The time node is the moment of abrupt change of the voltage and current waveform of the dynamic working point; extracting a voltage-current waveform in a steady state;
step S2: the global maximum power point scanning process is a disturbance observation method adopted by the operation of an inverter, namely, a GMPPT process realized by disturbing the output voltage of a photovoltaic array according to a voltage step length between the lowest operating voltage of the inverter and the normal operating voltage of the photovoltaic array and recording corresponding voltage and current at the moment of a sudden change point, and extracting an 80V-120V key section I-V curve;
step S3: the specific model parameters and the algorithm super-parameter range are set as follows, and for the reference data of the static complete I-V curve, the single diode battery model is as follows: i ph ∈[0,1],I s ∈[0,1],n∈[1,2],R s ∈[0,0.5],R p ∈[0,100]. Single diode photovoltaic module: i ph ∈[0,2],I s ∈[0,50],n∈[1,50],R s ∈[0,2],R p ∈[0,2000]. Number of bees N of QPSO algorithm p =15, maximum iteration number maxiter=15, maximum objective function calculation of lm algorithmThe value maxfunevals=780, the maximum iteration number maxiter=200. PROVA-1011 of model number of static complete actual measurement I-V curve testing instrument is model number of Gudwei GW3000-NS of key section I-V curve testing instrument of voltage and current waveform extraction in actual measurement dynamic working point global maximum power point tracking process, and the photovoltaic model parameter searching range of experimental data is I ph :[05N P ](A),I s :[01N p ](μA),n:[1N s 2N s ],R s :[01N s /N p ](Ω),R p :[0N s *100](Ω), where N s The number of solar cells connected in series for the photovoltaic model (photovoltaic array/module string/module), N p The number of the solar cells connected in parallel in the photovoltaic array/component string/component is the number of the solar cells connected in parallel in the photovoltaic array/component string/component.
Step S4: and performing global search on the photovoltaic model parameters by adopting a quantum particle swarm optimization algorithm, and obtaining an optimal initial value vector of the photovoltaic model parameters.
Step S41: according toIs the D-dimensional parameter vector of the search space, D is the number of parameters to be optimized, LB j ,UB j Respectively the upper and lower bounds of the parameters;
step S42: setting population number N of particles p And calculating the fitness value of each particle in the particle swarm by the maximum iteration number IterMax;
step S43: initializing the iteration number Iter of particles to be 1, and entering an iterative search process;
step S44: calculating a particle historical best position average value m best The calculation formula is as follows:
m is the population size, p best_i An extremum representing the ith particle of the current iteration;
step S45: updating a formula according to the particle position:
calculating the optimal solution of the current particle in the search space;
step S46: calculating fitness values of the population and the individuals, judging whether the iteration number Iter is smaller than or equal to the maximum iteration number IterMax set by the algorithm, if so, adding 1 to the current iteration number Iter, and re-entering step S44; otherwise, sorting the fitness value obtained by the last iterative calculation and outputting an optimal initial solution vector X 0 。
Step S5: adopting a Levenberg-Marquardt algorithm, and carrying out further local search by using the optimal photovoltaic model parameter vector obtained in the step S4 as an initial search parameter;
step S51: the optimal initial solution vector X obtained according to step S4 0 Calculating an objective function SSE and recording as F (X), wherein a recording vector F (X) is the difference between an actual measurement value and a calculated value at a solution vector X, and a Jacobian matrix J (X) of the F (X) is calculated;
step S52: setting an iteration parameter Iter=0, a maximum objective function calculation value MaxFunEvals and a maximum iteration number MaxIter; an optimality tolerance value tol;
step S53: the gradient vector G (x) of f (x) is calculated, and the calculation formula of G (x) is as follows: g (x) =2j (x) T F (x); the Hessian matrix H (x) of f (x) is calculated, and the calculation formula of H (x) is as follows: h (x) =2j (x) T J (x) +2Q (x); wherein,D i (x) For each F i (x) Is a Hessian matrix of (2);
step S54: the algorithm performs an iteration according to the equation: (J (x) k ) T J(x k )+λ k I)d k ＝-J(x k ) T F(x k ) Calculate the search direction d k The direction is the solution of the linear least square problem;
step S55: boundary detection, judging whether the iteration point x is in a constraint boundary, if the iteration point x is out of the boundary, projecting the step to the nearest feasible point by an algorithm, and modifying the iteration point x into P (x) by the algorithm, wherein the updating method of P (x) comprises the following steps: p (x) =lb if x < LB; p (x) =ubif x > UB; p (x) =x other;
step S56: calculating whether the objective function SSE, i.e. f (x), satisfies the algorithm termination condition 1:or termination condition 2: iter>And if not, continuing to add 1 to the Iter, and proceeding to step S54 for iterative computation until reaching a termination condition, and outputting a final result.
Preferably, in this example, two types of I-V curves (static complete I-V characteristic curves under different actual measurement conditions, and key segment I-V curves extracted from voltage and current waveforms in the process of tracking the global maximum power point of the dynamic operating point) of the actually measured photovoltaic array under different illuminance and temperature are fitted to extract parameters of the single-diode photovoltaic model, and the results are shown in tables 1-2, fig. 5 and fig. 8. As can be seen from the Root Mean Square Error (RMSE) of the curve fitting in tables 1-2, the method proposed in this embodiment can perform accurate curve fitting, and fully embody the accuracy of this embodiment.
TABLE 1 static complete I-V Curve fitting and parameter extraction results at different illuminance and temperature
TABLE 2 Key segment I-V Curve fitting and parameter extraction results in the dynamic operating Point GMPPT Process at different illuminance and temperature
Table 3 comparison table of photovoltaic module parameter extraction results under single diode model based on reference data for the present embodiment and the existing algorithm
The foregoing description is only of the preferred embodiments of the application, and all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (3)
1. A photovoltaic online parameter identification method based on a global maximum power point tracking and hybrid optimization algorithm is characterized by comprising the following steps of: the method comprises the following steps:
step S1: according to the time sequence voltage and current waveforms of the dynamic working points of the photovoltaic inverter, identifying the maximum power point tracking step by adopting a mutation point detection algorithm, recording waveform mutation time, and extracting the voltage and current waveforms in a steady state;
step S2: according to the voltage step length change characteristics in the voltage and current waveforms, identifying a global maximum power point tracking process, namely a waveform of GMPPT, and extracting a key segment I-V characteristic curve of the waveform corresponding to the GMPPT;
step S3: tracking and extracting the obtained I-V characteristic curve and the number N of solar cells connected in series and in parallel of the photovoltaic array according to the global maximum power point s And N p Selecting a single diode five-parameter model for parameter identification, and extracting five electrical parameters of photocurrent, single diode reverse saturation current, ideal factors, equivalent series resistance and equivalent parallel resistance in the model according to the search range of the electrical parameters of the model;
step S4: performing global search on parameters of the photovoltaic model by adopting a quantum particle swarm intelligent optimization algorithm, and acquiring an optimal initial value vector of the parameters of the photovoltaic model;
step S5: adopting a Levenberg-Marquardt algorithm, and carrying out further local search by using the optimal photovoltaic model parameter vector obtained in the step S4 as an initial search parameter;
step S6: carrying out equivalent single diode model parameter extraction on two types of photovoltaic data through a mixed quantum particle swarm algorithm and a Levenberg-Marquardt algorithm, namely a QPSO-LM algorithm; the two types of photovoltaic data are static complete I-V characteristic curves and key segment I-V curves extracted in the dynamic working point global maximum power point tracking process under different actual measurement conditions;
in the step S3, the photovoltaic model is a single-diode five-parameter model; the mathematical model of the single diode photovoltaic module is shown as follows:
the mathematical model of a single diode photovoltaic array is as follows:
wherein I is t And V t Current and voltage values in the measured I-V curve; k is Boltzmann constant 1.3806503 ×10 -23 J/K, q is the basic charge 1.60217646 ×10 -19 C, performing operation; five parameter vectors of single diode photovoltaic model are [ I ] ph ,I s ,n,R s ,R p ]Wherein I ph Is photocurrent, I s Is the reverse saturation current of a single diode, n is the idealized factor of the single diode, R s Equivalent series resistance R p Is equivalent to parallel resistance N s And N p The number of cells connected in series and in parallel, respectively;
the algorithm in steps S4 and S5 finds an optimal set of parameter vectors X within the search range of a given parameter vector so that the following formula is shown:
taking the sum variance SSE as an objective function of a hybrid optimization algorithm, namely a QPSO-LM algorithm, and when the value of the sum variance SSE is minimum, enabling the actually measured I-V curve to be optimally fit with the calculated simulation I-V curve, namely representing the optimally fit of the actually measured I-V curve and the calculated simulation I-V curve;
hybrid optimization algorithmThe QPSO-LM algorithm works by ranging the parameter vector of its single diode cell model for reference data over a given range of parameter vectors: i ph ∈[0,1],I s ∈[0,1],n∈[1,2],R s ∈[0,0.5],R p ∈[0,100]The method comprises the steps of carrying out a first treatment on the surface of the Single diode photovoltaic module: i ph ∈[0,2],I s ∈[0,50],n∈[1,50],R s ∈[0,2],R p ∈[0,2000]；
The step S4 specifically comprises the following steps:
step S41: according to Is the D-dimensional parameter vector of the search space, D is the number of parameters to be optimized, LB j ,UB j Respectively the upper and lower bounds of the parameters;
step S42: setting population number N of particles p And the maximum iteration number IterMax, calculating the fitness value of each particle in the particle swarm, wherein a calculation formula of the fitness value is an objective function, namely the value of a calculation sum variance SSE;
step S43: initializing the iteration number Iter of particles to be 1, and entering an iterative search process;
step S44: calculating a particle historical best position average value m best ，
The calculation formula is as follows:
m is the population size, p best_i An extremum representing the ith particle of the current iteration;
step S45: updating a formula according to the particle position:
calculating the optimal solution of the current particle in the search space;
step S46: calculating fitness values of the population and the individuals, judging whether the iteration number Iter is smaller than or equal to the maximum iteration number IterMax set by the algorithm, if so, adding 1 to the current iteration number Iter, and re-entering step S44; otherwise, sorting the fitness value obtained by the last iterative calculation and outputting an optimal initial solution vector X 0 ；
The step S5 specifically includes the following steps:
step S51: the optimal initial solution vector X obtained according to step S4 0 Calculating an objective function SSE and recording as F (X), wherein a recording vector F (X) is the difference between an actual measurement value and a calculated value at a solution vector X, and a Jacobian matrix J (X) of the F (X) is calculated;
step S52: setting an iteration parameter Iter=0, a maximum objective function calculation value MaxFunEvals and a maximum iteration number MaxIter; an optimality tolerance value tol;
step S53: the gradient vector G (x) of f (x) is calculated, and the calculation formula of G (x) is as follows: g (x) =2j (x) T F (x); the Hessian matrix H (x) of f (x) is calculated, and the calculation formula of H (x) is as follows: h (x) =2j (x) T J (x) +2Q (x); wherein,D i (x) For each F i (x) Is a Hessian matrix of (2);
step S54: the algorithm performs an iteration according to the equation: (J (x) k ) T J(x k )+λ k I)d k ＝-J(x k ) T F(x k ) Calculate the search direction d k The direction is the solution of the linear least square problem;
step S55: boundary detection, judging whether the iteration point x is in a constraint boundary, if the iteration point x is out of the boundary, projecting the step to the nearest feasible point by an algorithm, and modifying the iteration point x into P (x) by the algorithm, wherein the updating method of P (x) comprises the following steps: p (x) =lb if x < LB; p (x) =ubif x > UB; p (x) =x other;
step S56: calculating whether the objective function SSE, i.e. f (x), satisfies the algorithm termination condition 1: i x-Or termination condition 2: iter>And if not, continuing to add 1 to the Iter, and proceeding to step S54 for iterative computation until reaching a termination condition, and outputting a final result.
2. The photovoltaic online parameter identification method based on the global maximum power point tracking and hybrid optimization algorithm according to claim 1, wherein the method is characterized by comprising the following steps of:
in the step S1, according to the collection frequency of the dynamic working point data, the working voltage of each continuous 200 photovoltaic arrays is used as a set, a point is randomly selected to divide the set into two parts, residual errors of average values of points at two sides and each part are calculated, when the total residual error reaches the minimum value, the mutation point can be found, and the mutation point detection algorithm is shown in the following formula: when J takes the minimum value, record V r＝k And the time node is the moment of the abrupt change of the voltage and current waveform of the dynamic working point.
3. The photovoltaic online parameter identification method based on the global maximum power point tracking and hybrid optimization algorithm according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S2, the global maximum power point tracking process is a disturbance observation method adopted for the operation of the inverter, and the value range of the disturbance voltage step is 0.2V to 4V; the output voltage of the photovoltaic array is disturbed according to the voltage step length of 4V between the lowest working voltage 80V of the inverter and the normal working voltage 120V of the photovoltaic array, the GMPPT process is realized, the corresponding voltage and current are recorded according to the abrupt point moment, and the key section I-V curve of the GMPPT process is extracted.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110703847A (en) * | 2019-10-29 | 2020-01-17 | 特变电工西安电气科技有限公司 | Photovoltaic global maximum power point tracking method of improved particle swarm-disturbance observation method |
| CN111275160A (en) * | 2020-01-21 | 2020-06-12 | 河海大学常州校区 | Photovoltaic array parameter identification method based on population optimization improved particle swarm algorithm |
| CN111446732A (en) * | 2019-04-15 | 2020-07-24 | 国网甘肃省电力公司电力科学研究院 | Maximum power estimation method for photovoltaic power generation system |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107103154A (en) * | 2017-05-17 | 2017-08-29 | 南京南瑞继保电气有限公司 | A kind of photovoltaic module model parameter identification method |
| CN111446732A (en) * | 2019-04-15 | 2020-07-24 | 国网甘肃省电力公司电力科学研究院 | Maximum power estimation method for photovoltaic power generation system |
| CN110703847A (en) * | 2019-10-29 | 2020-01-17 | 特变电工西安电气科技有限公司 | Photovoltaic global maximum power point tracking method of improved particle swarm-disturbance observation method |
| CN111275160A (en) * | 2020-01-21 | 2020-06-12 | 河海大学常州校区 | Photovoltaic array parameter identification method based on population optimization improved particle swarm algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 自适应差分进化算法在光伏组件模型参数辨识中的应用;武涛;简献忠;应怀樵;郭强;;电力科学与工程(第04期);全文 * |
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