CN102364866A - Maximum power point tracking method for photovoltaic power system - Google Patents

Maximum power point tracking method for photovoltaic power system Download PDF

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CN102364866A
CN102364866A CN2011103647791A CN201110364779A CN102364866A CN 102364866 A CN102364866 A CN 102364866A CN 2011103647791 A CN2011103647791 A CN 2011103647791A CN 201110364779 A CN201110364779 A CN 201110364779A CN 102364866 A CN102364866 A CN 102364866A
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voltage
maximum power
power point
point tracking
photovoltaic
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袁晓玲
范发靖
周素梅
张叶
李世军
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Hohai University HHU
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Hohai University HHU
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The invention discloses a maximum power point tracking method for a photovoltaic power system. A maximum power point tracking algorithm is realized in a digital signal processor (DSP) by the current and voltage of a photovoltaic panel from a sampling unit and a current conditioning circuit to control pulse width modulation (PWM) output waveforms to realize power regulation. The maximum power point tracking of a photovoltaic array is realized by adopting a single-neuron adaptive proportion integration differentiation (PID) algorithm comprising the following steps of: initializing etaI, etaP, etaD, a and b, sampling the output voltage and current of the photovoltaic panel, calculating the derivative of power of a photovoltaic array over the voltage at a current moment, calculating an error and adaptively regulating a weight coefficient and a K value according to the magnitude of the error. Compared with the conventional maximum power point tracking algorithm, the invention improves maximum power point tracking accuracy and stability and increases maximum power point tracking speed.

Description

A kind of maximum power point tracing method of photovoltaic generating system
Technical field
The present invention relates to a kind of maximum power tracking method of photovoltaic cell.The present invention relates to photovoltaic cell.The present invention relates to grid-connected photovoltaic system.The present invention relates to the photovoltaic off-grid electricity generation system.
Background technology
As everyone knows, in the today of pursuing low-carbon (LC) society, solar energy more and more receives the attention of countries in the world as a kind of regenerative resource of cleaning.Under the support energetically of national governments, the average annual increasing value of the solar-energy photo-voltaic cell output in the whole world is 60%.Report according to the authority Solarbuzz of the research institution issue of photovoltaic generation market shows that global photovoltaic generation market scale will reach 15GW in 2010, and the whole world will have 9 national photovoltaic generation market scales to surpass 250MW.China's solar energy resources is very abundant, and theoretical reserves reach annual 17000 hundred million tons of standard coals, and the potentiality of solar energy resources development and use are boundless.China's photovoltaic generation industry is in the seventies in 20th century starting, and the mid-90 gets into stable development period.In recent years; Under the strong pulling in national project such as " bright engineering " guide's project and " every township will have electricity " engineering and photovoltaic generation market, the world; Especially the appearance of " the medium-term and long-term development plan of regenerative resource " and " plan of solar energy roof ", " golden sun engineering ", the photovoltaic generation industry of China has obtained fast development." the medium-term and long-term development plan of regenerative resource " clearly by 2010, the photovoltaic generation total capacity will reach 300MW, and the year two thousand twenty reaches 1800MW.For carrying out the Party Central Committee, State Council about accelerating the strategic plan of cultivation and development strategy property new industry, must deepen the understanding, hold on to the period of strategic opportunity of photovoltaic generation industry development, further promote domestic photovoltaic generation scale and use.
Photovoltaic generating system efficient just is one of most important index of weighing photovoltaic system; Because it is significantly non-linear that the photovoltaic cell output characteristic has, this non-linear influence that receives own technical indicators such as multiple external environmental factor such as intensity of sunshine, temperature, load and output impedance.So the photoelectric conversion rate of present photovoltaic cell is lower, in order to improve the efficient of photovoltaic generation, need carry out MPPT maximum power point tracking (MPPT) to photovoltaic generating system.At present, MPPT method commonly used has constant-voltage method, disturbance observation, conductance increment method, fuzzy control method or the like.But there are some problems in the maximum power point tracing method of the photovoltaic cell that these are conventional in application, and is low like accuracy of detection, response speed is slow, and bad adaptability is with to follow the tracks of efficient low etc.This patent is realized a kind of new maximum power point tracing method, can solve some problems that exist in the existing algorithm.
Summary of the invention
The present invention provides a kind of MPPT maximum power point tracking algorithm of fast and stable, solves the deficiency and the problem that exist in the present conventional algorithm.
The MPPT maximum power point tracking algorithm that the present invention adopted is a maximum power point of realizing tracking photovoltaic cell in fast and stable ground according to the thought of ANN Control.
Technical scheme of the present invention is following:
The first step: initialization system ,
Figure 435753DEST_PATH_IMAGE004
,
Figure 277807DEST_PATH_IMAGE006
, a, b,
Figure 233868DEST_PATH_IMAGE002
,
Figure 110557DEST_PATH_IMAGE004
,
Figure 995336DEST_PATH_IMAGE006
Be respectively the learning rate of integration, ratio, differential, bBe the weight coefficient of error, a>0;
Second step: gather the photovoltaic battery panel voltage and current through voltage sensor, Hall element, the voltage and current that collects through the current regulating circuit, is delivered among the DSP, in DSP, pass through P=U*ICalculate power, and calculate the derivative of current time power to voltage, wherein power can adopt two moment of front and back power deviations to find the solution the derivative of power to voltage divided by front and back two moment voltage deviation to the derivative of voltage;
The 3rd step: the error of calculation
Figure 2011103647791100002DEST_PATH_IMAGE008
=-
Figure 2011103647791100002DEST_PATH_IMAGE010
Figure 2011103647791100002DEST_PATH_IMAGE012
In the formula,
Figure 2011103647791100002DEST_PATH_IMAGE014
,
Figure 2011103647791100002DEST_PATH_IMAGE016
, ,
Figure 2011103647791100002DEST_PATH_IMAGE020
, neuronic proportionality coefficient , a>0 is used for guaranteeing K>0, bIt is the weight coefficient of error;
The 4th step: the value of calculating ;
Figure 2011103647791100002DEST_PATH_IMAGE026
utilizes the DSP task manager to produce pwm signal and gives switching tube.
The present invention has following beneficial effect: the present invention is based on the thought of ANN Control, having adopted has the Hebb of supervision learning rules to adjust weight coefficient adaptively, adjusts through the error size online adaptive KValue, control photovoltaic array power output is stabilized in maximum power point.The present invention can change by fast adaptation photovoltaic array operational environment, improves the speed and the lasting accuracy of MPPT maximum power point tracking, reduces system oscillation, satisfies the requirement of system's maximal power tracing.
Description of drawings
Fig. 1 MN self-adaptive PID structure chart.
Fig. 2 MPPT maximum power point tracking algorithm flow chart.
Embodiment
Below in conjunction with accompanying drawing technology contents of the present invention is explained:
As shown in Figure 1, in the photovoltaic generating system, the power output of photovoltaic cell, the relation of voltage and current are:<i >P=U*I</i>, under the normal operation, the power voltage curve of photovoltaic cell is single protruding peak shape, has a unique maximum power point.When photovoltaic system is operated in the maximum power point place, slope <img file=" 2011103647791100002DEST_PATH_IMAGE028.GIF " he=" 20 " img-content=" drawing " img-format=" jpg " inline=" no " orientation=" portrait " wi=" 57 " />=0; When the maximum power point left side <img file=" 511637DEST_PATH_IMAGE028.GIF " he=" 20 " img-content=" drawing " img-format=" jpg " inline=" no " orientation=" portrait " wi=" 57 " >0, <img file=" 772854DEST_PATH_IMAGE028.GIF " he=" 20 " img-content=" drawing " img-format=" jpg " inline=" no " orientation=" portrait " wi=" 57 " /> < 0 when maximum power point the right.Therefore, design closed-loop control system, control<img file="2585DEST_PATH_IMAGE028.GIF" he="20" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="57" />=0, having adopted has the Hebb of supervision learning rules to adjust weight coefficient adaptively, through the online adjustment of error size<i >K</i>The MN Adaptive PID Control algorithm of value is realized the MPPT maximum power point tracking of photovoltaic generating system.
The MN adaptive controller is to realize self adaptation, self organizing function through the adjustment to weight coefficient, and the weight coefficient adjustment is to realize by the Hebb learning rules that supervision is arranged.Learning algorithm and control algolithm are following:
（1）
（2）
Figure 674241DEST_PATH_IMAGE024
（3）
In the formula, ,
Figure 253569DEST_PATH_IMAGE016
,
Figure 557511DEST_PATH_IMAGE018
,
Figure 894952DEST_PATH_IMAGE020
, ,
Figure 559075DEST_PATH_IMAGE004
,
Figure 350313DEST_PATH_IMAGE006
Be respectively the learning rate of integration, ratio, differential, KBe neuronic proportionality coefficient, K>0. KThe selection of value is extremely important, KBig more, then rapidity is good more, but overshoot is big, KBe worth when excessive, even possibly make system unstable. KValue is selected too small, and the rapidity of system is relatively poor.Therefore, employing is according to the big or small online adjustment of systematic error KValue.
Figure 992910DEST_PATH_IMAGE022
（4）
In the formula (4) a>0 is used for guaranteeing K>0, bBe the weight coefficient of error, guarantee KValue can be not excessive can be not too small yet.When error is big, KValue also increases accordingly, increases the response speed of system; When error reduces to smaller, KValue also reduces simultaneously, reduces the overshoot of system.
The maximum power tracking method practical implementation of a kind of photovoltaic cell of the present invention is following:
The first step: according to structure chart shown in Figure 2 and formula (1)-Shi (4), write the DSP program, and in the DSP program initialization system
Figure 553204DEST_PATH_IMAGE002
,
Figure 387168DEST_PATH_IMAGE004
,
Figure 400123DEST_PATH_IMAGE006
, a, b
Second step: gather the photovoltaic battery panel voltage and current through voltage sensor, Hall element, the voltage and current that collects through the current regulating circuit, is delivered among the DSP, in DSP, pass through P=U*ICalculate power, and calculate the derivative of current time power to voltage, wherein power can adopt two moment of front and back power deviations to come the derivative of approximate solution power to voltage divided by front and back two moment voltage deviation to the derivative of voltage.
The 3rd step: calculate
Figure 843481DEST_PATH_IMAGE008
,
Figure 2011103647791100002DEST_PATH_IMAGE030
,
Figure 2011103647791100002DEST_PATH_IMAGE032
,
Figure 2011103647791100002DEST_PATH_IMAGE034
And K
The 4th step: the value of calculating
Figure 2011103647791100002DEST_PATH_IMAGE036
, utilize the DSP task manager to produce pwm signal and give switching tube.
MPPT maximum power point tracking algorithm of the present invention sends photovoltaic battery panel electric current and voltage through sampling unit and current regulating circuit, in digital signal processor (DSP), realizes the MPPT maximum power point tracking algorithm, and control PWM output waveform realizes power adjustments.At first initialization
Figure 9014DEST_PATH_IMAGE002
,
Figure 13879DEST_PATH_IMAGE004
,
Figure 514130DEST_PATH_IMAGE006
, a, b, sample then photovoltaic battery panel output voltage, electric current calculate the derivative of photovoltaic array current time power to voltage, the error of calculation
Figure 2011103647791100002DEST_PATH_IMAGE038
, according to the size of error, adaptive adjustment weight coefficient reaches KThe MN self-adaptive PID algorithm of value realizes the MPPT maximum power point tracking of photovoltaic array.

Claims (1)

1. the maximum power point tracing method of a photovoltaic generating system is characterized in that comprising the steps:
The first step: initialization system
Figure 2011103647791100001DEST_PATH_IMAGE002
,
Figure 2011103647791100001DEST_PATH_IMAGE004
,
Figure 2011103647791100001DEST_PATH_IMAGE006
, a, b,
Figure 196100DEST_PATH_IMAGE002
,
Figure 730987DEST_PATH_IMAGE004
, Be respectively the learning rate of integration, ratio, differential, bBe the weight coefficient of error, a>0;
Second step: gather the photovoltaic battery panel voltage and current through voltage sensor, Hall element, the voltage and current that collects through the current regulating circuit, is delivered among the DSP, in DSP, pass through P=U*ICalculate power, and calculate the derivative of current time power to voltage, wherein power can adopt two moment of front and back power deviations to find the solution the derivative of power to voltage divided by front and back two moment voltage deviation to the derivative of voltage;
The 3rd step: the error of calculation
Figure 2011103647791100001DEST_PATH_IMAGE008
=-
Figure 2011103647791100001DEST_PATH_IMAGE012
In the formula,
Figure 2011103647791100001DEST_PATH_IMAGE014
,
Figure 2011103647791100001DEST_PATH_IMAGE016
,
Figure 2011103647791100001DEST_PATH_IMAGE018
,
Figure 2011103647791100001DEST_PATH_IMAGE020
, neuronic proportionality coefficient
Figure 2011103647791100001DEST_PATH_IMAGE022
, a>0 is used for guaranteeing K>0;
The 4th step: the value of calculating ;
Figure 2011103647791100001DEST_PATH_IMAGE026
utilizes the DSP task manager to produce pwm signal and gives switching tube.
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Cited By (10)

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CN102611141A (en) * 2012-03-30 2012-07-25 南京大学 MPPT (maximum power point tracking) control device and method of photovoltaic inverter based on perturbation method
CN102759945A (en) * 2012-05-23 2012-10-31 浙江大学 Extreme searching control (ESC)-based photovoltaic solar panel maximum power point tracking method in photovoltaic power generation system
CN102799208A (en) * 2012-07-20 2012-11-28 黄克亚 Photovoltaic power generation maximum power point tracking fuzzy proportion integration differentiation (PID) control method
CN102854911A (en) * 2012-09-11 2013-01-02 江苏禾力清能电气有限公司 Photovoltaic battery maximum power tracking method
CN106527568A (en) * 2016-12-15 2017-03-22 中国人民解放军后勤工程学院 Maximum power tracker control method based on generalized dynamic fuzzy neural network
CN108227476A (en) * 2017-09-04 2018-06-29 河南森源电气股份有限公司 A kind of control method of AGV trolleies
CN110488907A (en) * 2019-08-07 2019-11-22 上海电机学院 A kind of mixing step-length maximum power tracking and controlling method for photovoltaic power generation
CN110995066A (en) * 2019-12-21 2020-04-10 中国特种设备检测研究院 Double-servo motor control method for amusement facility track detection device
CN111158282A (en) * 2019-12-27 2020-05-15 吉林大学 Single-neuron FPGA control method and system for crosslinked cable production line
CN114336742A (en) * 2021-12-22 2022-04-12 浙江大学 Solar photovoltaic controller based on self-adaptive neural network

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102611141A (en) * 2012-03-30 2012-07-25 南京大学 MPPT (maximum power point tracking) control device and method of photovoltaic inverter based on perturbation method
CN102759945A (en) * 2012-05-23 2012-10-31 浙江大学 Extreme searching control (ESC)-based photovoltaic solar panel maximum power point tracking method in photovoltaic power generation system
CN102759945B (en) * 2012-05-23 2014-06-04 浙江大学 Extreme searching control (ESC)-based photovoltaic solar panel maximum power point tracking method in photovoltaic power generation system
CN102799208A (en) * 2012-07-20 2012-11-28 黄克亚 Photovoltaic power generation maximum power point tracking fuzzy proportion integration differentiation (PID) control method
CN102854911A (en) * 2012-09-11 2013-01-02 江苏禾力清能电气有限公司 Photovoltaic battery maximum power tracking method
CN106527568A (en) * 2016-12-15 2017-03-22 中国人民解放军后勤工程学院 Maximum power tracker control method based on generalized dynamic fuzzy neural network
CN108227476A (en) * 2017-09-04 2018-06-29 河南森源电气股份有限公司 A kind of control method of AGV trolleies
CN110488907A (en) * 2019-08-07 2019-11-22 上海电机学院 A kind of mixing step-length maximum power tracking and controlling method for photovoltaic power generation
CN110995066A (en) * 2019-12-21 2020-04-10 中国特种设备检测研究院 Double-servo motor control method for amusement facility track detection device
CN111158282A (en) * 2019-12-27 2020-05-15 吉林大学 Single-neuron FPGA control method and system for crosslinked cable production line
CN114336742A (en) * 2021-12-22 2022-04-12 浙江大学 Solar photovoltaic controller based on self-adaptive neural network
CN114336742B (en) * 2021-12-22 2023-10-27 浙江大学 Solar photovoltaic controller based on self-adaptive neural network

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Application publication date: 20120229