CN102477943A - Intelligent MPPT (maximum power point tracking) wind energy controller - Google Patents

Intelligent MPPT (maximum power point tracking) wind energy controller Download PDF

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Publication number
CN102477943A
CN102477943A CN201010562503XA CN201010562503A CN102477943A CN 102477943 A CN102477943 A CN 102477943A CN 201010562503X A CN201010562503X A CN 201010562503XA CN 201010562503 A CN201010562503 A CN 201010562503A CN 102477943 A CN102477943 A CN 102477943A
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China
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output
wind
value
module
power
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CN201010562503XA
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Chinese (zh)
Inventor
张世桐
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HUIZHOU SANHUA INDUSTRIAL Ltd
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HUIZHOU SANHUA INDUSTRIAL Ltd
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Priority to CN201010562503XA priority Critical patent/CN102477943A/en
Publication of CN102477943A publication Critical patent/CN102477943A/en
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    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

Abstract

The invention provides an intelligent type MPPT (maximum power point tracking) wind energy controller which comprises a slip form extremum search module and a blur parameter setting module, wherein the parameter of the slip form extremum search module is adjusted according to the output of the blur parameter setting module; the slip form extremum search module comprises a comparing unit, a switching unit and two integrators, and the blur parameter setting module comprises a fuzzy controller; and the parameter of the switching unit is set by the output of the fuzzy controller, and the input of the fuzzy controller is a power tracking error e and wind speed V of a wind power system. The wind energy controller provided by the invention can provide an optima rotation rate reference value for realizing maximize wind energy trapping of the wind power system, and can effectively restrain the neighbouring system disturbance of a maximum power.

Description

Intelligent MPPT wind energy controller
Technical field
The present invention relates to sharp maximum power point follow in (MPPT) control technique, be specifically related to intelligent MPPT wind energy controller.
Background technique
Wind energy is a kind of love knot, renewable energy sources extensive, that development is exceedingly fast distributes; The class renewable sources of energy that also have large-scale development and commercialized development prospect; But; Wind energy also is a kind of randomness, burst, the more intense energy of unstability, and in order to improve utilization ratio of wind energy, the operation that need control blower fan according to change of wind velocity is to catch peak output.
At present, MPPT technology commonly used has tip speed ratio control technique, power signal feedback control and climbing method to seek control technique.
But above-mentioned three kinds of methods more or less will depend on the characteristic that wind field is starved blower fan, can not effectively suppress near the system disturbance of maximum power point.
Summary of the invention
The objective of the invention is to overcome deficiency of the prior art, a kind of intelligent MPPT wind energy controller is provided, can optimum speed reference be provided, effectively suppress near the system disturbance of maximum power point for realizing the wind power system maximal wind-energy capture.
The objective of the invention is to realize: intelligent MPPT wind energy controller through following technological scheme; Comprise sliding formwork extremum search module and the fuzzy parameter module of adjusting, the adjust output of module is adjusted the parameter of sliding formwork extremum search module according to fuzzy parameter; Said sliding formwork extremum search module comprises comparing unit, switch element and two integrators, and the said fuzzy parameter module of adjusting comprises fuzzy controller;
The wind power system peak output P1 that sliding formwork extremum search module is generated is worth as a reference, and subtracts each other output power tracking error e with the real output P2 of wind power system through said comparing unit, according to power tracking error e structure switch element;
Said switch element comprises tri-state switch function and hard-limiting switch function; The peak output reference value P1 that the value conduct that the output Z1 of tri-state switch function generates through integration searches; The value that the output Z2 process integrator of hard-limiting switch function generates is as peak output reference value P1, and the value that the output of hard-limiting switch function process integrator generates is as the pairing optimal velocity reference value of peak output reference value w;
The parameter of said switch element is adjusted by the output of fuzzy controller, said fuzzy controller be input as wind power system power tracking error e and wind speed v.
The module of adjusting said sliding formwork extremum search module, fuzzy parameter realizes on a slice fpga chip; The digital quantity signal that is input as wind power control system output power P2 and wind speed v correspondence of FPGA (programmable gate array) chip is output as optimal velocity with reference to the corresponding digital quantity signal of w.
The present invention compares that existing technology has the following advantages and beneficial effect: the input parameter of controller of the present invention is merely the power output signal and the wind velocity signal of generator, so, less demanding to the custom signal accuracy, avoided the difficulty of highi degree of accuracy measuring wind speed; Simultaneously; Avoided to obtain in the conventional controlling method differential of blower fan model and parameter, rated output and speed; The stability of controller is provided, can optimum speed reference be provided, effectively suppressed near the system disturbance of maximum power point for realizing the wind power system maximal wind-energy capture.
Description of drawings
Fig. 1 is the structural representation of intelligent MPPT wind energy controller shown in the embodiment.
Embodiment
Below in conjunction with embodiment and accompanying drawing the present invention is described in further detail, but mode of execution of the present invention is not limited thereto.
Embodiment
As shown in Figure 1, intelligent MPPT wind energy controller comprises sliding formwork extremum search module and the fuzzy parameter module of adjusting, and the adjust output of module is adjusted the parameter of sliding formwork extremum search module according to fuzzy parameter; Said sliding formwork extremum search module comprises comparing unit, switch element and two integrators, and the said fuzzy parameter module of adjusting comprises fuzzy controller;
The wind power system peak output P1 that sliding formwork extremum search module is generated is worth as a reference, and subtracts each other output power tracking error e with the real output P2 of wind power system through said comparing unit, according to power tracking error e structure switch element;
Said switch element comprises tri-state switch function and hard-limiting switch function; The peak output reference value P1 that the value conduct that the output Z1 of tri-state switch function generates through integration searches; The value that the output Z2 process integrator of hard-limiting switch function generates is as peak output reference value P1, and the value that the output of hard-limiting switch function process integrator generates is as the pairing optimal velocity reference value of peak output reference value w;
The parameter of said switch element is adjusted by the output of fuzzy controller, said fuzzy controller be input as wind power system power tracking error e and wind speed v.
The module of adjusting said sliding formwork extremum search module, fuzzy parameter realizes on a slice fpga chip; The digital quantity signal that is input as wind power control system output power P2 and wind speed v correspondence of FPGA (programmable gate array) chip is output as optimal velocity with reference to the corresponding digital quantity signal of w.
The foregoing description is a preferred implementation of the present invention; But mode of execution of the present invention is not restricted to the described embodiments; Other any do not deviate from change, the modification done under spirit of the present invention and the principle, substitutes, combination, simplify; All should be the substitute mode of equivalence, be included within protection scope of the present invention.

Claims (2)

1. intelligent MPPT wind energy controller is characterized in that: comprise sliding formwork extremum search module and the fuzzy parameter module of adjusting, the adjust output of module is adjusted the parameter of sliding formwork extremum search module according to fuzzy parameter; Said sliding formwork extremum search module comprises comparing unit, switch element and two integrators, and the said fuzzy parameter module of adjusting comprises fuzzy controller;
The wind power system peak output P1 that sliding formwork extremum search module is generated is worth as a reference, and subtracts each other output power tracking error e with the real output P2 of wind power system through said comparing unit, according to power tracking error e structure switch element;
Said switch element comprises tri-state switch function and hard-limiting switch function; The peak output reference value P1 that the value conduct that the output Z1 of tri-state switch function generates through integration searches; The value that the output Z2 process integrator of hard-limiting switch function generates is as peak output reference value P1, and the value that the output of hard-limiting switch function process integrator generates is as the pairing optimal velocity reference value of peak output reference value w;
The parameter of said switch element is adjusted by the output of fuzzy controller, said fuzzy controller be input as wind power system power tracking error e and wind speed v.
2. intelligent MPPT wind energy controller according to claim 1; It is characterized in that: said sliding formwork extremum search module, the fuzzy parameter module of adjusting realizes on a slice programmable gate array chip; The digital quantity signal that is input as wind power control system output power P2 and wind speed v correspondence of programmable gate array chip is output as optimal velocity with reference to the corresponding digital quantity signal of w.
CN201010562503XA 2010-11-26 2010-11-26 Intelligent MPPT (maximum power point tracking) wind energy controller Pending CN102477943A (en)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103032265A (en) * 2012-12-12 2013-04-10 天津市电力公司 Maximum output tracking control method of wind generation unit based on extremum research
CN104410107A (en) * 2014-11-27 2015-03-11 江苏科技大学 Passive integral sliding mode control method for double-fed wind power system
CN105179164A (en) * 2015-06-25 2015-12-23 江苏科技大学 Wind energy converting system sliding mode control method and device based on T-S fuzzy model
CN106026675A (en) * 2016-07-05 2016-10-12 扬州大学 Fuzzy frequency selection sliding mode controller for LLC resonant DC converter
CN107524563A (en) * 2017-08-21 2017-12-29 华南理工大学 A kind of control method based on slip form extremum search
CN109298747A (en) * 2018-09-20 2019-02-01 天津大学 SMESC wind power system MPPT method based on IIWO optimization

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004052649A (en) * 2002-07-19 2004-02-19 Meidensha Corp Output power smoothing control device of wind power generator
CN101054951A (en) * 2007-05-24 2007-10-17 上海交通大学 Large scale wind power machine control method based on maximum energy capture
CN101639038A (en) * 2009-08-14 2010-02-03 江南大学 FPGA-based maximum power tracking controller of wind power system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004052649A (en) * 2002-07-19 2004-02-19 Meidensha Corp Output power smoothing control device of wind power generator
CN101054951A (en) * 2007-05-24 2007-10-17 上海交通大学 Large scale wind power machine control method based on maximum energy capture
CN101639038A (en) * 2009-08-14 2010-02-03 江南大学 FPGA-based maximum power tracking controller of wind power system

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103032265A (en) * 2012-12-12 2013-04-10 天津市电力公司 Maximum output tracking control method of wind generation unit based on extremum research
WO2014089955A1 (en) * 2012-12-12 2014-06-19 天津市电力公司 Maximum output tracking control method of wind generation unit based on extremum research
CN103032265B (en) * 2012-12-12 2014-11-05 天津市电力公司 Maximum output tracking control method of wind generation unit based on extremum research
US9657718B2 (en) 2012-12-12 2017-05-23 State Grid Tianjin Electric Power Company Extremum seeking-based control method for maximum output tracking of a wind turbine generator
CN104410107A (en) * 2014-11-27 2015-03-11 江苏科技大学 Passive integral sliding mode control method for double-fed wind power system
CN105179164A (en) * 2015-06-25 2015-12-23 江苏科技大学 Wind energy converting system sliding mode control method and device based on T-S fuzzy model
CN105179164B (en) * 2015-06-25 2018-11-09 江苏科技大学 Wind-energy changing system sliding-mode control and device based on T-S fuzzy models
CN106026675A (en) * 2016-07-05 2016-10-12 扬州大学 Fuzzy frequency selection sliding mode controller for LLC resonant DC converter
CN106026675B (en) * 2016-07-05 2020-01-03 扬州大学 Fuzzy frequency-selecting sliding mode controller of LLC resonant DC converter
CN107524563A (en) * 2017-08-21 2017-12-29 华南理工大学 A kind of control method based on slip form extremum search
CN109298747A (en) * 2018-09-20 2019-02-01 天津大学 SMESC wind power system MPPT method based on IIWO optimization
CN109298747B (en) * 2018-09-20 2020-06-05 天津大学 IIWO optimization-based SMESC wind power system MPPT method

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Application publication date: 20120530