an intelligent fuzzy method for mppt of photovoltaic arrays ieee

7/31/2019 An Intelligent Fuzzy Method for MPPT of Photovoltaic Arrays IEEE

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(a)

(b) Fig.1 Photovoltaic array characteristics(a) Maximum power varies with different insolation at normal

temperature(at 2 )

(b)Maximum power varies with different cell temperature at the

same insolation (1000W/m2 )

An Intelligent Fuzzy Method for MPPT of Photovoltaic arrays

Guohui Zeng, Qizhong Liu

College of Electrical and Electronic Engineering,Shanghai University of Engineering Science,

Shanghai, P. R. China 201620.Email: [email protected]

Abstract —An intelligent fuzzy method for maximum power

point tracking (MPPT) of photovoltaic (PV) systems is

presented in this paper. In this method, fuzzy inference process

can be finished by an equation with an adaptive factor instead

of fuzzy rules lookup table, which saves memory space and

accelerates operation process. It is simple to be implemented

on single chip. The availability and adjustability of this method

is validated by experiments using PV arrays, boost converterand single-phase grid-connected inverter. The simulation and

experiment results showed its attractive features such as

simplicity, fast response, well dynamic performance and that it

can increase output power extracted from PV arrays.

Keywords-MPPT; PV; fuzzy inference;adaptive factor

I. I NTRODUCTION

With the increasing demand on electric power and thedecreasing reserve of oil and coal, more and more countriesare paying great attention to sustainable energy such as windand solar energy. Although solar energy is abundant and freeof cost, the high initial investment on PV systems and non-

linearity of PV cell output characteristic counteract its widecommercialization. The most important issues for grid-tiedPV systems to gain wide acceptance are low cost, highefficiency and good output performance.

The PV array has an optimum operating point togenerate the maximum power under some insolation level atsome temperature, showed as in Fig.1. To draw maximum power from PV arrays, MPPT controller is required in astand-alone or grid-connected PV system. However, theoutput characteristic of PV arrays is nonlinear. Thegenerating power varies with different solar insolation leveland different cell temperature. Therefore, it is not easy totrack the maximum power point of the PV cell quickly andeffectively in the real application. To solve this problem,

many tracking control methods have been proposed such asconstant voltage tracking (CVT), perturb and observealgorithm (P&O), incremental conductance algorithm (ICA), parasitic capacitance, hill climbing algorithm, neuralnetwork, fuzzy logic control (FLC), and so on [1]-[6].Among them, fuzzy logic control requires no mathematicalmodel, and it is easy to be implemented in real controlsystem. Both merits provide it with a promising applicationin MPPT of PV arrays. However, fuzzy inference rules wereconstructed based on experts’ knowledge or operationexperiences. Sometimes they cannot provide satisfied

control effect in the real operational system. On the other hand, operating conditions of grid-connected PV systemvaries with time and season. It is hard to get quick andefficient tracking performance in transient conditions withtraditional fuzzy logic controller. Therefore, the fuzzy rulesshould be adjusted on-line at real time to conform tooperating environment change.

In this paper, an improved intelligent fuzzy method for MPPT of PV arrays is presented, which improves in thefollowing two stages. In the first stage, fuzzy inference proceeds with an equation instead of fuzzy rules lookup table,which saves memory space and accelerates inference process.In the second stage, adaptive factor applied to theinference equation can change the weights of error E anderror change EC to control outputU and modify fuzzy ruleson-line automatically to improve system control effect. There

2009 Second International Symposium on Computational Intelligence and Design

978-0-7695-3865-5/09 $26.00 © 2009 IEEE

DOI 10.1109/ISCID.2009.235

356

2009 Second International Symposium on Computational Intelligence and Design

978-0-7695-3865-5/09 $26.00 © 2009 IEEE

DOI 10.1109/ISCID.2009.235

356



Fig.2 The frame of grid-tied PV system Fig.3 The configuration of the improved fuzzy logic

controller based on the presented method

are many good features for MPPT controller with the presented method: low cost, quick-tracking, and no output power fluctuation, etc. It is simple to be implemented onsingle chip and need no memory space for fuzzy rules.Moreover, adaptive factor can improve system controleffect by adjusting parameters of fuzzy inference equationon-line. The availability and adjustability of this method is

validated by experiments using PV arrays, boost converter and single-phase grid-connected inverter.

II. THE I NTELLIGENT FUZZY METHOD

Inference engine is the most important part of a fuzzycontroller. It decides fuzzy controller’s effect and availability.Generally, fuzzy inference is carried out with ‘If…Then…’rules or by looking up fuzzy rules table. Traditional fuzzylogic control requires the expert knowledge of the processoperation for fuzzy inference rules setting. Fuzzy logiccontrol with fixed rules is inadequate in application wherethe operating conditions change in a wide range and theavailable expert knowledge is not reliable. Still, memoryspace is necessary for these fuzzy controllers to save fuzzy

inference rules. At the same time, it is hard to adjust thesefuzzy rules with operating conditions after being established.To improve the fuzzy logic control performance [7], anequation is developed to replace these rules:

[( ) / 2]U INT E EC (1)where the domains of error E , error change EC and controloutputU can be expressed with integers:

{ } { } { } { , 1, , 1,0,1, , } E EC U N N N And the domain expressed with integers is equivalent to thatwith fuzzy language variables. One example applied in this paper is shown in the following:

{ 3, 2, 1,0,1,2,3} { , , , , , , } NB NM NS ZO PS PM PB With (1), the domain of control output can be produced

directly from the domains of error E and error change EC .

For example: IF E is NB AND EC is PS THEN U is NS .

which is equivalent to the following rule: IF E is 3 AND EC is 1 THEN U is 1 .

which also means” If operating point is far from maximum power point at its right side and is moving to it slightly,then decrease duty cycle slightly”. Apparently, this resultcan also be got from (1) quickly and easily. Of course,tracking would be done more quickly if U is ( 2) NM or

U is ( 3) NB in this example.From the above example, it is also known that results

from (1) sometimes may not be as good as we expect. Theweights of both input variables in the equation (1) are fixed

on 0.5. In fact, the perfect solution is that when E is big,

bigger U is expected to accelerate tracking process; while

when E is small and EC is big, smaller U is expected to

avoid over-tuning. To get this expected control performance,

adaptive factor is introduced into (1) that can change the

weights of error E and error change EC to control output

U and modify fuzzy rules on-line automatically to improve

the system control effect. Therefore, an intelligent fuzzylogic control method is presented in this paper, as expressed

in (2).

[ (1 ) ]

[0,1]

U INT E EC

(2)

Adaptive factor will change the weights of

error E and error change EC in determining the controloutput U according to different operation conditions.

Compared to other conventional fuzzy control methods, the presented one has the following merits while being applied

to MPPT of PV arrays:(a) Simple calculating and easy implementation.(b) Swift output response and quick tracking.(c) No need of memory space for fuzzy rules lookup table.(d) High control precision and good adjustability.

III. MPPT WITH THE I NTELLIGENT FUZZY METHOD

A. The System Scheme

In order to explain the feasibility of MPPT with the

presented method, the grid-tied PV system is constructedwith boost DC DC and single phase DC AC , asshown in Fig.2.

The controller’s output changes PWM pulses to controlMOSFET switch [4]. MPPT can be finished by varying theduty cycle of MOSFET in the boost converter.

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Fig.4 Fuzzy control membership functions of error E , error

change EC and output U Fig.5 The control output surface for one example

B. MPPT with the Intelligent Fuzzy Method

To track and extract maximum power from the PV arraysat a given solar insolation level and cell temperature, a novelfuzzy logic controller is proposed based on the intelligentfuzzy logic method. It consists of four parts: fuzzification,inference engine, defuzzification and adaptive set, as shownin Fig.3.1) Fuzzification:

The error E and error change EC can be calculated from(3) and (4):

( ) ( 1)( )

( ) ( 1)

ph ph

ph ph

P k P k E k

i k i k

(3)

( ) ( ) ( 1) EC k E k E k (4)

where ( ) ph P k and ( ) phi k are the power and current of thePV array, respectively. Therefore, ( ) E k is zero at themaximum power point of PV array.

To simplify the control calculation, the values of error E and error change EC can be normalized with (5) before fuzzification process:

1,

/ ,| |

1,

s

X X

X X X X X

X X

(5)

where max X X ,so the scopes of error E and error change EC will be [ 1,1] . Then the membership functionsexpressed with triangular function can be shown in the Fig.4

2) Fuzzy Inference:The control output can be gained by calculating (2) simply,

different from the conventional fuzzy logic controller, whichusually by looking up the fuzzy rules table. Apparently, itcan get the control output quickly and need no memoryspace for the fuzzy rules table. Moreover, with adaptivefactor , inference engine can produce abundant control

rules that comform to operating conditions. The controloutput surface for one example is shown in Fig.5.3)Defuzzification:

The output of this fuzzy controller is a fuzzy subset of control. To get a nonfuzzy value of control, a defuzzficationstage is necessary. Defuzzification for this system can be performed by height method [5][8]. The nonfuzzy value of control output can be gained with the height method simplyand quickly. The height defuzzification method can be donewith (6):

1 1

m mk

k k

k k

du u

(6)

With the output of controller, pulse generated from PWM

can switch MOSFET to change the duty cycle of the boost DC DC . Then the PV array output voltage can beadjusted to track the maximum power.4) Adaptive Set:

One of the most important merits of the presented methodis that it can optimize the fuzzy inference rules on-line toimprove the system performance according to the operatingconditions. As we know, when the system is far away fromthe maximum power point (MPP), a quick tracking would be expected. On the other hand, when the system works inthe vicinity of MPP, stable output power without over-tuning would be more valuable. Therefore, adaptive factor can be calculated with the following equation (7) on-line.

0 0(1 )

E

N (7)

where0

[ ,1] ,0 and N can be set as any number. One

example here is 0.3 and 3. If max

E E , then 1 , error E in (3) has the largest weight, and error can be eliminatedin the quickest way. On the other hand, if 0 , is thesmallest value (0.3) and error change EC has the largest

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Fig.6 Simulation results depicting the change of Power, voltage and V v

Fig.7 Output power generated by the systems adopting intelligent

FLC, traditional FLC and P& O respectively for a typical day in June.

weight (0.7), which prevents the system from over-tuningand stabilizes the output power.

IV. SIMULATION AND EXPERIMENT R ESULTS

This presented method can be simulated with MATLAB program [9]. The simulation results in Fig.6 showed thatoutput power of PV module could be maximized following

insolation change quickly. All devices of boost converter and single-phase inverter are assumed to be ideal.

The presented method is simple and has also beenimplemented in the MCU chip of PIC16C5X. Experimentshave been done using boost DC DC , 500W single phaseinverter and 12 PV modules from Topsolar. The maincharacteristics of a PV module are indicated below(1000W/m2at 2).

Product type: TSM-50(734x651x40mm)Open circuit voltage: 21.7VShort circuit current: 3.4AMaximum power voltage: 17.5VMaximum power current: 3.05APeak power: 50Wp

Output power generated by the system adopting the presented method, conventional fuzzy control method andPerturb & Observe respectively is recorded at different timesfor a typical day in June in Shanghai, China, shown in Fig.7.

V. CONCLUSIONS

An intelligent fuzzy method for MPPT of PV arrays is presented in this paper on the base of fuzzy logic controlalgorithm. A quick tracking can be done by inference engineexpressed by equations with adaptive factor while nomemory space for fuzzy rules table is required.

The simulation results show that the improved fuzzycontroller with the presented method has the merits such assimplicity, fast response, low over-tuning, high control

precision, and easy implementation. In addition, theexperiment results show that more power has been extractedfrom the PV arrays with this presented method and outputholds stable, though its hardware cost is low comparing tothose with other methods. All these advantages will do goodto the commercialization of PV systems.

ACKNOWLEDGMENT

This paper was supported by Shanghai EducationCommission under Project (“Excellent Youth Teacher”Project GJD-07005) .

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[4] H. Matsuo and F. Kurokawa., “New solar cell power supply systemusing a boost type bidirectional DC-DC converter”, IEEE Trans. Ind. Electron., vol. 31, pp. 51- 55, Feb. 1984.

[5] N. Patcharaprakiti and S. Premrudeepreechacharn, “Maximum Power Point Tracking Using Adaptive Fuzzy Logic Control for Grid-connected Photovoltaic System”, PESW2002, volume 1, PP:372-377,2002.

[6] C. Y. Won, D. H. Kim, et al, “A New Maximum Power Point Tracker of Photovoltaic Arrays Using Fuzzy Controller”, PESC’1994 Record ,PP:396-403, 1994.

[7] D. Q. Feng and S. H. Xie, Fuzzy Intelligent Control. Beijing,

Chemistry-Industry, 1998, Chap. 3, pp:80-81, 1998.[8] L. X. Wang, A Course in Fuzzy System and Control [M]. New

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an intelligent fuzzy method for mppt of photovoltaic arrays ieee

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