CN106100582B - Based on the recursive least-squares photovoltaic cell parameter identification method with forgetting factor - Google Patents
Based on the recursive least-squares photovoltaic cell parameter identification method with forgetting factor Download PDFInfo
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- CN106100582B CN106100582B CN201610531729.0A CN201610531729A CN106100582B CN 106100582 B CN106100582 B CN 106100582B CN 201610531729 A CN201610531729 A CN 201610531729A CN 106100582 B CN106100582 B CN 106100582B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/10—Photovoltaic [PV]
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a kind of based on the recursive least-squares photovoltaic cell model parameter identification method with forgetting factor, comprise the following steps：The recursive least-squares model form of photovoltaic cell is established, determines parameter to be identified；Initiation parameter valuation, forgetting factor and covariance matrix；Obtain the real-time output voltage electric current of photovoltaic cell, undated parameter valuation and target function value；Object function meets the parameter estimation of output now, as photovoltaic cell parameter optimal value during predetermined threshold value.The four parameter model of photovoltaic cell is converted into recursive least-squares model form by the present invention, shields the influence of photovoltaic cell internal structure and systematic error to model, optimized parameter value is obtained by iteration.It is realized simply, can reduce amount of calculation, is reduced the internal memory that data take in a computer, is improved identification speed.Forgetting factor can emphasize the effect of new data, gradually forget the effect of legacy data, model is had higher precision, better numerical value stability.
Description
Technical field
It is especially a kind of minimum based on the recursion with forgetting factor the present invention relates to a kind of photovoltaic cell parameter identification method
Two multiply photovoltaic cell model parameter identification method, belong to technical field of photovoltaic power generation.
Background technology
The utilization of solar energy and the research of photovoltaic cell characteristic turn into focus, domestic as research is constantly goed deep into
Outer scholar proposes the photovoltaic cell model of different description I-V curves.I-V curve is the macroscopical description of photovoltaic cell characteristic, its
In parameter be model intrinsic characteristic reflection.I-V equations can be not only determined by recognizing photovoltaic cell parameter, using trying to achieve
I-V prediction equation photovoltaic arrays power output；And the change by analyzing these parameters can further study photovoltaic
The cause of cell malfunctions.Therefore the identification for carrying out photovoltaic cell inner parameter is to have very much for studying and improving its characteristic
Meaning.
At present, the parameter identification method of photovoltaic cell is broadly divided into parameter Approximate Solution and the parameter based on optimized algorithm
Method of estimation.The characteristic equation of photovoltaic cell model is one and complicated surmounts nonlinear function, it is impossible to straight by simple computation
Solution is connect, parameter Approximate Solution is exactly to handle I-V characteristic equation using mathematical methods such as differential derivation and simplified models, in the hope of
Obtain parameter approximation.Although the method using mathematical analysis approximate solution parameter is intuitively simple, the ginseng that this method is tried to achieve
Number approximation error is larger, is not applied to when required precision is higher.Method for parameter estimation based on optimized algorithm mainly utilizes
Intelligent algorithm carries out parameter identification to photovoltaic cell.For example, some scholars propose genetic algorithm applying to photovoltaic cell parameter
Identification field, on the premise of identification precision is ensured, the multigroup result obtained after photovoltaic cell parameter identification is converged to one group
Parameter value.And minimal gradient searching method is used in traditional genetic algorithm, it is formed improving blending inheritance algorithm, can
Improve the accuracy and speed of parameter identification.For another example, consider the difference of cloud amount in photovoltaic cell actual working environment, introduce adaptive
Chaos particle swarm optimization algorithm (SA-CPSO) is answered, photovoltaic cell model in the case of normal condition and different shades can be picked out
Parameter.In addition, some scholars utilize the features such as randomness, regularity, ergodic of chaos algorithm, population calculation is incorporated into
Method, Chaos Search control algolithm is formed to carry out parameter identification, ability of searching optimum is increased.In a word, compared to parameter
Approximate Solution, significant advantage, but most of intelligence are had in terms of precision and reliability based on the parameter Estimation of optimized algorithm
In the presence of optimizing, the time is long, it is precocious to be easily absorbed in, realizes the problems such as cumbersome for algorithm.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of based on the recursive least-squares photovoltaic cell with forgetting factor
Parameter identification method.
The present invention uses following technical proposals：
It is a kind of based on the recursive least-squares photovoltaic cell parameter identification method with forgetting factor, methods described is used to recognize
Photovoltaic cell parameter in grid-connected photovoltaic system in the photovoltaic arrays of m strings n simultaneously；It is characterized in that：Comprise the following steps：
Step 1：The recursive least-squares model of photovoltaic cell is established, determines parameter θ to be identified：
The recursive least-squares model of the photovoltaic cell is：
And
Δ I=Isc-IL (2)
In formula, θT=(a1, a2, a3) it is parameter to be identified, IL、ULThe respectively real-time output voltage and reality of photovoltaic cell
When output current, IscFor photogenerated current；
Step 2：Initialization：By forming step by step in detail below：
Step 2-1：The length n and forgetting factor λ of definition input observed quantity, 0<λ≤1；
Step 2-2：The initial time value of parameter estimation to be identified is setCovariance matrix P (t) is set
Value P (0)=aI of initial timen, a real numbers, InFor n × n unit matrix；
Step 3：Obtain the real-time output voltage U of photovoltaic cellLWith real-time output current IL：
UL=UL_array/m (3)
IL=IL_array/n (4)
In formula, UL_arrayAnd IL_arrayThe real-time output voltage of respectively described photovoltaic array and real-time output current；
Step 4：Produce observing matrixAnd its transposed matrix
Wherein, y (t) is the observation at photovoltaic cell output voltage current time, and y (t-1) is photovoltaic cell output voltage
The observation of previous moment, y (t-n) are the observation at n moment before photovoltaic cell output voltage；
Step 5：Calculate current time observing matrixCovariance matrix P (t)：
Step 6：The gain matrix F (t-1) of last moment is calculated,
Step 7：Update the parameter estimation of photovoltaic cell
Step 8：Calculate the prediction residual at current time：
And the object function value for calculating current time is：
Step 9：Judge the object function J at current timetWhether (θ) is less than predetermined threshold value, if not, turning to step 3, such as
Fruit is to turn to step 10；
Step 10：Output parameter valuationAs the identification result of photovoltaic cell parameter θ.
It is using beneficial effect caused by above-mentioned technical proposal：
1st, the present invention carries out parameter identification using the least square method of recursion with forgetting factor to photovoltaic cell, by photovoltaic electric
The four parameter model in pond turns to the form of recursive least-squares model, shields photovoltaic cell internal structure and systematic error to mould
The influence of type, photovoltaic cell output data is directly obtained, optimized parameter value is obtained by iteration, is photovoltaic cell parameter identification
Provide new thinking.It synchronously can disposably pick out whole parameters, it is not necessary to substep identification parameters.
2nd, the least square method of recursion algorithm that the present invention uses is simple, can reduce amount of calculation, reduces data in a computer
The internal memory of occupancy, improve identification speed.
3rd, the present invention adds forgetting factor in traditional least square method of recursion, emphasizes the effect of new data, gradually loses
The effect for data of forgeting old friends, the real-time dynamic on-line identification system for the big data quantity that is particularly suitable for use in, the introducing of forgetting factor also make mould
Type has higher precision, better numerical value stability, has higher engineering application value.
4th, the present invention is easy to test under laboratory condition, is equally applicable in general photovoltaic generating system, versatile.
Brief description of the drawings
Fig. 1 is photovoltaic array equivalent circuit；
Fig. 2 is the recursive least-squares photovoltaic cell parameter identification method flow chart with forgetting factor；
Embodiment
The present invention is further detailed explanation with reference to the accompanying drawings and detailed description.
As shown in figure 1, by a number of photovoltaic cell string arranged in parallel on fixed support i.e. obtain photovoltaic array.It is false
If each photovoltaic cell for forming photovoltaic array has preferable uniformity, wherein have m series component, n parallel component.
It is as shown in Fig. 2 a kind of based on the recursive least-squares photovoltaic cell parameter identification method with forgetting factor, the side
Method is used to recognize the photovoltaic cell parameter in the photovoltaic arrays of m strings n simultaneously in grid-connected photovoltaic system；It is characterized in that：Including
Following steps：
Step 1：The recursive least-squares model of photovoltaic cell is established, determines parameter θ to be identified：
The recursive least-squares model of the photovoltaic cell is：
And
Δ I=Isc-IL (2)
In formula, θT=(a1, a2, a3) it is parameter to be identified, IL、ULThe respectively real-time output voltage and reality of photovoltaic cell
When output current, IscFor photogenerated current；
Step 2：Initialization：By forming step by step in detail below：
Step 2-1：The length n and forgetting factor λ of definition input observed quantity, 0<λ≤1；
Step 2-2：The initial time value of parameter estimation to be identified is setCovariance matrix P (t) is set
Value P (0)=aI of initial timen, a real numbers, InFor n × n unit matrix；
Step 3：Obtain the real-time output voltage U of photovoltaic cellLWith real-time output current IL：
UL=UL_array/m (3)
IL=IL_array/n (4)
In formula, UL_arrayAnd IL_arrayF is respectively the real-time output current of real-time output voltage of the photovoltaic array；
Step 4：Produce observing matrixAnd its transposed matrix
Wherein, y (t) is the observation at photovoltaic cell output voltage current time, and y (t-1) is photovoltaic cell output voltage
The observation of previous moment, y (t-n) are the observation at n moment before photovoltaic cell output voltage；
Step 5：Calculate current time observing matrixCovariance matrix P (t)：
Step 6：The gain matrix F (t-1) of last moment is calculated,
Step 7：Update the parameter estimation of photovoltaic cell
Step 8：Calculate the prediction residual at current time：
And the object function value for calculating current time is：
Step 9：Judge the object function J at current timetWhether (θ) is less than predetermined threshold value, if not, turning to step 3, such as
Fruit is to turn to step 10；
Step 10：Output parameter valuationAs the identification result of photovoltaic cell parameter θ.
Claims (1)
1. a kind of based on the recursive least-squares photovoltaic cell parameter identification method with forgetting factor, methods described is used to recognize light
Photovoltaic cell parameter in photovoltaic grid-connected system in the photovoltaic arrays of m strings n simultaneously；It is characterized in that：Comprise the following steps：
Step 1：The recursive least-squares model of photovoltaic cell is established, determines parameter θ to be identified：
The recursive least-squares model of the photovoltaic cell is：
And
Δ I=Isc-IL (2)
In formula, θT=(a 1,a 2,a 3) it is parameter to be identified, IL、ULRespectively the real-time output voltage of photovoltaic cell and in real time it is defeated
Go out electric current, IscFor photogenerated current；
Step 2：Initialization：By forming step by step in detail below：
Step 2-1：The length n and forgetting factor λ of definition input observed quantity, 0<λ≤1；
Step 2-2：The initial time value of parameter estimation to be identified is setSet covariance matrix P (t) initial
Value P (the 0)=aI at momentn, a real numbers, InFor n × n unit matrix；
Step 3：Obtain the real-time output voltage U of photovoltaic cellLWith real-time output current IL：
UL=UL_array/m (3)
IL=IL_array/n (4)
In formula, UL_arrayAnd IL_arrayThe real-time output voltage of respectively described photovoltaic array and real-time output current；
Step 4：Produce observing matrixAnd its transposed matrix
Wherein, y (t) is the observation at photovoltaic cell output voltage current time, and y (t-1) is that photovoltaic cell output voltage is previous
The observation at moment, y (t-n) are the observation at n moment before photovoltaic cell output voltage；
Step 5：Calculate current time observing matrixCovariance matrix P (t)：
Step 6：The gain matrix F (t-1) of last moment is calculated,
Step 7：Update the parameter estimation of photovoltaic cell
Step 8：Calculate the prediction residual at current time：
And calculate the object function at current time：
Step 9：Judge the object function J at current timetWhether (θ) is less than predetermined threshold value, if not, step 3 is turned to, if it is,
Turn to step 10；
Step 10：Output parameter valuationAs the identification result of photovoltaic cell parameter θ.
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| CN109919797A (en) * | 2019-01-11 | 2019-06-21 | 中国电力科学研究院有限公司 | A kind of parameter Estimation and bearing calibration of power supply transien process |
| CN109884550B (en) * | 2019-04-01 | 2020-01-17 | 北京理工大学 | Online parameter identification and backtracking method for power battery system |
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| CN102999700A (en) * | 2012-11-27 | 2013-03-27 | 华北电力大学 | Photovoltaic cell output characteristic modeling method |
| CN103399491A (en) * | 2013-08-06 | 2013-11-20 | 清华大学 | Parameter identification method for photovoltaic module mechanism model of photovoltaic power generation system |
| CN103973221A (en) * | 2014-05-20 | 2014-08-06 | 河海大学 | Photovoltaic array parameter identification method based on measured data |
| CN104850914A (en) * | 2015-05-29 | 2015-08-19 | 东南大学 | Feature modeling based new energy power generation capacity forecasting method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102999700A (en) * | 2012-11-27 | 2013-03-27 | 华北电力大学 | Photovoltaic cell output characteristic modeling method |
| CN103399491A (en) * | 2013-08-06 | 2013-11-20 | 清华大学 | Parameter identification method for photovoltaic module mechanism model of photovoltaic power generation system |
| CN103973221A (en) * | 2014-05-20 | 2014-08-06 | 河海大学 | Photovoltaic array parameter identification method based on measured data |
| CN104850914A (en) * | 2015-05-29 | 2015-08-19 | 东南大学 | Feature modeling based new energy power generation capacity forecasting method |
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