fsm model of a simple photovoltaic system · power engineering and electrical engineeringvolume: 13...

POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 13 | NUMBER: 3 | 2015 | SEPTEMBER

FSM Model of a Simple Photovoltaic System

Martina LATKOVA, Michal BAHERNIK, Marek HOGER, Peter BRACINIK

Department of Power Electrical Systems, Faculty of Electrical Engineering, University of Zilina,Univerzitna 1, 010 26 Zilina, Slovakia

[email protected], [email protected], [email protected],[email protected]

DOI: 10.15598/aeee.v13i3.1411

Abstract. The paper describes a simulation model of asimple photovoltaic system intended as a tool for test-ing the use of finite state machines for simulationsrepresenting a long-term operation of renewable energysources. The mathematical model of the photovoltaicsystem is described first. Then it is used to build a fi-nite state machine model that calculates a power outputof the photovoltaic system for changing values of a so-lar irradiance and a temperature. Data measured on areal photovoltaic installation are used to verify model’saccuracy through its comparison with a previously cre-ated and verified Matlab model. The finite state ma-chine model presented in this paper was created usingPtolemy II software.

Keywords

DC boost converter, finite state machine, math-ematical model, photovoltaic array, Ptolemy II.

1. Introduction

Photovoltaic (PV) is a technology that deals with thedirect conversion of solar irradiation energy into elec-trical energy. The development of semiconductor tech-nology in the fifties of the last century, P-N junctionpreparation and knowledge of physical processes in thePN junction created the conditions for the productionof solar cells with a reasonable efficiency. For practicalapplications, a large number of photovoltaic cells areinterconnected and encapsulated into units called pho-tovoltaic panels. The advantage of photovoltaic panelsis that their operation is safe, quiet and they need nofuel, produce no waste, in most installations have nomoving parts and therefore need a minimal mainte-nance [1], [2], [3]. On the other hand, an output ofPV power plant strongly depends on weather condi-

tions (a solar irradiance and a temperature). More-over, these changes can influence the operation of anelectric power network, in which PV systems (powerplants) are installed. Since the changes of PV produc-tion could be very fast, it is necessary to use dynamicmodelling to evaluate PV systems’ influence on net-work’s operation. It could be quite challenging whena larger part of a power system is modelled, especiallyif the influence is analysed from a longer-term pointof view. To solve this problem, it seems to be veryuseful to design PV systems models using finite statemachines (FSM), to represent both steady state as wellas dynamic characteristics in only one model.

2. Mathematical Model of thePV System

The model of PV system consists of mathematicalmodels of a PV array, a DC boost converter loadedby constant resistive load and a maximum power pointtracking (MPPT) controller (Fig. 1).

Fig. 1: A block diagram of modelled photovoltaic system.

c© 2015 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 230


The inputs of the PV array model are solar irradi-ance λ (kW·s−2), temperature T (K) and load currentI(A). They are used to calculate the PV array voltageV that is used as an input for the model of DC boostconverter. Converter calculates the increased outputvoltage Vo and the load current I.

The switching of the DC boost converter is controlledby MPPT controller, which changes the duty cycleD ofthe converter according to the voltage from PV arrayand load current. The MPPT controller’s algorithmdetermines the duty cycle needed to operate the PVarray in a point of its maximum power.

2.1. Mathematical Model of the PVArray

Solar cell is basically a p-n junction and so the Shockleyequation can be used for its mathematical description:

ID = IS ·

eq · VDk · T

− 1

, (1)

where ID is a diode current, IS is a diode satura-tion current, VD is a diode voltage, q is an elec-tron charge (1.6·10−19 C), k is a Boltzman constant(1.38·10−23 J·K−1) and T is a temperature dependenceof the diode saturation current. An equivalent circuitof solar cell based on this equation is shown in Fig. 2.The circuit consists of the current source, a diode, ashunt resistance RSH and a series resistance RS , whichrepresents an internal resistance of the cell [3].

Fig. 2: Equivalent circuit of a photovoltaic cell.

Because a typical PV cell produces approximately2 W, the cells have to be connected in series-parallelconnection to produce enough power. The generalizedequation for the solar array connected of Np paralleland Ns series cell is [4]:

I = Np · IPH −Np · IS ·

eq ·(

VNs

+ I·Rs

Np

)k · T ·A − 1

,

(2)

where A is an ideality factor.

Using Eq. (2) the voltage V can be expressed as:

V = Ns·

·(k · T ·A

q· log

(Np · IPH − INp · IS

+ 1

)− I ·Rs

Np

).

(3)

2.2. Mathematical Model of the DCBoost Converter

A topology of the DC boost converter is shown inFig. 3. It is modelled in two modes of operation, whichare given by the operation state of the switch. The out-put variables are inductor current iL and the capacitorvoltage vC [5].

Fig. 3: Topology of the boost converter.

When the switch is on (closed), the inductor storesthe energy from PV array and the load is supplied onlyby the capacitor [5]:

V = L · diLdt

, (4)

vCR

= C · dvCdt

. (5)

When the switch if off (open), the inductor currentflows to the load and the stored energy of the inductoris transferred to the capacitor and the load [5]:

V = L · diLdt

+ vC , (6)

iL = C · dvCdt

+vCR. (7)

2.3. Algorithm of the MPPTController

The DC boost converter is controlled by MPPT con-troller to achieve maximum power point of the PV ar-ray. Controller changes duty cycle D of the converter.



There are many methods for the MPPT, such asperturb and observe method, incremental conductancemethod, etc. The most common one is perturb andobserve method. Its flowchart is shown in Fig. 4 [6].

Fig. 4: Flowchart of perturb and observe method.

3. FSM Model of the PVSystem

Finite state machines are used to model system be-haviour in many types of engineering and scientific ap-plications. The state of a system is defined as its con-dition at a particular point in time. A state machineis a system whose outputs depend not only on the cur-rent inputs, but also on the current state of the system.The state of a system is a summary of everything thesystem needs to know about previous inputs in order toproduce outputs. It is represented by a state variables ∈

∑, where

∑is the set of all possible states for the

system. A FSM is a state machine where∑

is a finiteset. In a finite state machine, a system’s behaviour ismodelled as a set of states and the rules that governtransitions between them [7].

A FSM model of PV system, created in the soft-ware Ptolemy II, is shown in Fig. 5. The model usesinput values of solar irradiance (lam), temperature Tand their changes (∆lam and ∆T , respectively) to cal-culate the output values of current (Im), the voltage(V m) and the power (Pm) of PV array at the pointof its maximum power (MPP) as well as the outputvoltage of the converter (V o). The FSM model con-sists of two states, a steadystate and a transientstate.The transientstate represents the dynamic of PV sys-tem by calculating the changes of output parametersat each change of the solar irradiance (∆lam) or/andthe temperature (∆T ), expressed by the lower guard in

Fig. 5. If there is no change of any of these input pa-rameters, the FSM model returns to the steadystate,the upper guard in Fig. 5, in which the steady stateoperation of PV system is simply simulated throughthe repeating sending of the last calculated values ofoutput parameters (in transientstate) to the modeloutput ports. Such arrangement significantly reducesmodel’s execution time.

Fig. 5: FSM model of the PV system.

The model is intended for using in simulations repre-senting a longer time of PV system operation. To testa FSM approach for such a modelling, the decision wasmade to simplify the boost converter model. Insteadof using the before mentioned MPPT algorithm andEq. (4) to Eq. (7), the direct calculation of the current(IM ) and the voltage (VM ) at the point of maximumpower using a fill factor of PV array was applied. It ofcourse influences the model’s dynamic, but the modelis still able to represent the changes in PV system op-eration.

Fig. 6: Simplified model of the transient state.

Fill factor (FF) is a parameter, which in conjunctionwith the open circuit voltage (Voc) and the short-circuitcurrent (Isc), determines the maximum power of a so-lar cell. The FF is defined as the ratio of the max-imum power to the product of the open circuit volt-age and the short-circuit current [8]. The maximumpower is calculated as a multiplication of the fill fac-tor, the short-circuit current obtained from Eq. (2) andthe open circuit voltage obtained from Eq. (3). Then



a set of equations, consisting of following equation formaximum power:

PM = VM · IM , (8)

and Eq. (2), are used to calculate PV array voltage(VM ) and current (IM ) in MPP.

The model of DC boost converter in Fig. 6 representsan ideal DC boost converter and its mathematical rep-resentation consists of following equations:

IM =VM

R · (1−D2), (9)

Vo =VM

1−D. (10)

The duty cycle D is derived from Eq. (9) and thenused in Eq. (10) to calculate the converter’s outputvoltage Vo.

4. Verification of the CreatedModel

A dataset representing 24 hours long measurement ofthe solar irradiance and the temperature, measured ona real PV installation with one minute resolution, wasused to test the accuracy of created FSM model. ThePV array of FSM model was parametrized to representa real installation of two series connected PV panels.Their parameters are presented in Tab. 1. The averagevalue of panels’ fill factor, calculated from the dataset,was FF = 0.74.

A static data flow (SDF) director (Fig. 6) was usedinstead of a continuous time (CT) director [7], in orderto reduce the simulation’s execution time.

The current (I) and voltage (V ) curves of simulatedPV array are shown in Fig. 7 and Fig. 8, respectively.The curve of the DC boost converter’s output voltage(V o) is shown in Fig. 9.

Fig. 7: The current curve of the PV array per 24 hours.

The power curve calculated by the FSM model of thePV system (Fig. 10) was compared to the power curvecalculated by mathematical nonlinear model created inMatlab (Fig. 11). The Matlab model is more describedin [9]. It determines the maximum of power curve cal-culated in each simulation step for given values of the

Fig. 8: The voltage curve of the PV array per 24 hours.

Fig. 9: The output voltage curve of the converter per 24 hours.

solar irradiance and the temperature. Its accuracy wasproved through the comparison with data measured onthe real PV installation [9].

Fig. 10: PV system’s power curve calculated by FSM model.

The comparison of power curves calculated by dif-ferent models is shown in Fig. 12. It shows the errorcalculated as:

ε =PMatlab − PFSM

PMatlab· 100 (%) , (11)

where PMatlab is an output power calculated by theMatlab model and PFSM is an output power calculatedby the FSM model.

Fig. 11: PV system’s power curve calculated by Matlab model.

Both models show a very good correlation of calcu-lated results, especially when compared from a gener-ated energy point of view. The error’s average value ofthe PV system power output calculated by FSMmodel,when compared to the Matlab model, is 3.41 %. Errordeviations are caused by two reasons. The first one isthe use of the average value of the PV arrays fill factorhence it does not represent properly the shape of PV



Tab. 1: Technical parameters of used PV panels.

Parameter Parameter name Parameter valueIsc Short-circuit current 7.71 [A]

KI Cell temperature coefficient 0.11 [mA/C]

K Boltzmann’s constant 1.38065·10−23 [J/K]

Tr Thermodynamic reference temperature 298.15 [K]

q Electron charge 1.6·10−19 [C]

Eg Bandgap voltage for silicon 1.11 [eV]

A Ideality factor 1.3 [-]

Voc Open-circuit voltage 0.589 [V]

Rs Serial resistance 0.01136 [Ω]

Rsh Parallel resistance 116.8415 [Ω]

Fig. 12: The comparison of models’results.

system power curve for different values of solar irradi-ation. The other is the difference in how the voltagesand currents representing the point of maximum powerare calculated in the models. The Matlab model usesmore precise method, but it needs two and a half longerexecution time than FSM model.

5. Conclusion

The bigger integration of renewable energy sourcesto electric power network is based on better anddeeper understanding of their operation, which de-pends strongly on weather conditions and can changewithin different time scales. That’s the reason whytheir influence on power system operation needs to beanalysed with respect to different time scales as well.Therefore there is a need for such simulation modelsthat can properly represent both their steady state aswell as transient operation.

The use of finite state machine seems to be a goodapproach for this kind of modelling. Models based onfinite states machines consist of different states, repre-sented by submodels, each of them suitable for mod-elling different operating conditions, but still cooper-ating together. Presented FSM model proved a goodability and accuracy to model one-day PV system op-eration, even at sudden changes of solar irradiance. Asthe FSM model was tested on a real data set, the as-

sumption can be made that it would be able to modelcorrectly a longer period of PV system operation aswell.

Therefore FSM models seem to be a good solutionfor simulating long-term operation of renewables, orany other part of electric power system, with definedaccuracy and acceptable simulations’ execution times.

Acknowledgment

This paper has been supported by the Educationalgrant agency (KEGA) Nr: 030ZU-4/2014: The inno-vation of technology and education methods orientedto area of intelligent control of power distribution net-works (Smart Grids).

References

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[2] SHAHAT, A. PV Cell Module Modelling & ANNSimulation for Smart Grid Applications. Journalof Theoretical & Applied Information Technology.2010, vol. 16, iss. 1/2, pp. 9–20. ISSN 1992-8645.

[3] SAID, S., A. MASSOUD, M. BENAMMAR and S.AHMED. A Matlab/Simulink-Based PhotovoltaicArray Model Employing SimPowerSystem Tool-box. Journal of Energy and Power Engineering.2012, vol. 6, no. 12, pp. 1965–1975. ISSN 2327-5901.

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About Authors

Martina LATKOVA was born in Czechoslovakia.She received her M.Sc. from University of Zilina inElectrical Power Systems. She is with the Departmentof Power Electrical Systems as a Ph.D. student. Herresearch interests include distribution networks andrenewable energy sources.

Michal BAHERNIK was born in Czechoslo-vakia. He received his M.Sc. from University ofZilina in Electrical Power Systems. He is with theDepartment of Power Electrical Systems as a Ph.D.student. His research interests include distributionnetworks and renewable energy sources.

Marek HOGER was born in Czechoslovakia.He received his M.Sc. as well as Ph.D. from Universityof Zilina in Electrical Power Systems. He is withthe Department of Power Electrical Systems as anassistant professor. His research interests includedistribution networks, fault location and operation ofmicrogrids.

Peter BRACINIK was born in Czechoslovakia. Hereceived his M.Sc. as well as Ph.D. from Universityof Zilina in Electrical Power Systems. He is withthe Department of Power Electrical Systems as anassociate professor. His research interests includedistribution networks, fault location and operation ofsmart regions.


http://dx.doi.org/10.1109/ICIC.2009.330

http://dx.doi.org/10.1109/INDCON.2011.6139513

http://dx.doi.org/10.1016/0038-1101(81)90062-9

http://dx.doi.org/10.1109/ELEKTRO.2014.6848897

fsm model of a simple photovoltaic system · power engineering and electrical engineeringvolume: 13...

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