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Energy Procedía 90 (2016) 69 - 77

5 th International Conference on Advances in Energy Research, ICAER 2015, 15-17 December

2015, Mumbai, India

A Maximum Power Point Tracking Technique Based on Ripple Correlation Control for Single Phase Photovoltaic System with

Fuzzy Logic Controller

Ch. L. S. Snrnvas, Sreeraj E. S

Department of Electrical and Electronics Engineering, National Institute of Technology Goa, Ponda and 403401, India

Abstract

This paper proposes a novel maximum power point tracking (MPPT) technique by integrating ripple correlation control (RCC) with fuzzy logic controller (FLC) for single-phase, single-stage grid-connected photovoltaic (PV) systems. RCC is suitable for implementing MPPT in single-phase PV systems and it possess advantages of less complexity, low cost of implementation and high rate of convergence. The proposed MPPT technique based on RCC integrated with FLC is working satisfactorily under varying insolation conditions. The effectiveness of the proposed technique has been verified by performing numerical simulation studies. © 2016Publishedby Elsevier Ltd. This isanopenaccess article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of ICAER 2015

Keywords: Power Point Tracking; Photovoltaic; Ripple correlation control; Fuzzy logic Controller.

1. Introduction

The relevance of renewable energy resources (like solar, wind etc.) as sources of electric power has been increased

* Corresponding author. Tel.: +91-8975320849. E-mail address: sreeraj@nitgoa.ac.in

1876-6102 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of ICAER 2015 doi:10.1016/j.egypro.2016.11.171

over the years because of the increasing demand of electricity, limited availability of conventional energy resources, and as a potential alternate to tackle global warming [1]. Among renewable energy sources, solar photovoltaic (PV) system is considered one of the most promising technologies, as electrical energy is available directly and is suitable for distributed generation. It is eco friendly and requires less maintenance and operating cost [2]. In the distributed generation applications, PV system operates in two modes, namely, standalone mode and grid connected mode. In grid connected mode, the operation strategy of PV system is to extract maximum available power from the PV array and feed it to the grid.

Fig. 1. Single phase single stage PV system

A typical Grid connected PV system has more than one stage of power conversion. The first stage consist dc-dc converter to boost the voltage level such that it can extract maximum power from the PV array and the second stage consists a dc-ac converter which supplies the extracted power from the PV array to ac grid. The cost and complexity of PV system can be reduced by adopting single stage topologies. Single-stage PV system is more reliable as compared to multistage PV system owing to its reduced component count and complexity of the system [2-4]. The dc-ac inverter present in the single-stage PV system perform the functions of extracting maximum power from PV array and supplies that power to grid.

Since the v-i characteristics of the PV cell are nonlinear which is dependent on the solar insolation and temperature [5], there is a need for efficient maximum power point tracking (MPPT) techniques. Various MPPT techniques such as Hill climbing, Perturb and Observe, Incremental conductance are reported in the literature [5-8]. Even though these methods are more popular and can track maximum power point (MPP), each method has their own drawbacks. The drawbacks of Perturb and Observe and Hill climbing methods are deviation in the operating point from MPP due to sudden change in the atmospheric conditions like sudden variations in the solar insolation. Also, the correct perturbation size is important for satisfactory performance in dynamic as well as steady state operation [5-9]. The choice of the proper perturbation size affects the performance of Incremental conductance based MPPT technique also [5, 6]. Efforts have been made to solve this problem by providing variable perturbation step size, but it requires costly and complex control circuits [7].

Another method called ripple correlation control (RCC) yields fast and parameter insensitive MPPT of PV system [9, 10]. MPPT based on RCC is suitable for single-phase, single-stage PV system. In single phase PV system, PV array voltage is subjected to the 100 Hz ripple (double the grid frequency) because of the oscillating nature of the power fed to the grid [9-12]. This ripple is used to track the maximum power point (MPP). Since the ripple is naturally available by switching of the inverter in single-phase PV system no artificial perturbation is needed, which is drawback in the aforementioned Perturb and Observe, Hill climbing and Incremental conductance MPPT methods. In this paper a novel RCC based MPPT algorithm along with a fuzzy logic controller (FLC) has been proposed. FLC has been used to improve the dynamic performance of the system specially under varying insolation condition as FLC does not require accurate mathematical description of plant [13, 14]. Fig. 1 shows the typical single phase single stage PV system with FLC. The output of PV array is connected to the dc link capacitor C, which is acting as input to the voltage source inverter (VSI). The output of the VSI is fed to the grid and is controlled by MPPT & FLC and PWM

blocks. MPPT block generates the reference dc link voltage Vref, by taking the PV array voltage and PV array current as inputs. The output of the MPPT block is compared with the dc link voltage and given as an input to the FLC. The output of the FLC is change in load angle S, which is given as an input to the PWM control block which generates the switching signals for the VSI. Extensive numerical simulations are carried out to validate the proposed scheme. The proposed MPPT technique with FLC improves the dynamic performance of the PV system especially under varying insolation conditions.

Following sections of this paper are organized as follows: the operating principle and implementation of MPPT algorithm is discussed in Section 2. Design of the fuzzy logic controller is presented in Section 3. Simulation results of a PV generation system are presented in Section 4 and concluding remarks are presented in Section 5.

2. Operating Principle and Implementation of MPPT Algorithm

For a single-phase system, the value of instantaneous power pg(t) injected into the grid pulsates at twice the grid frequency. This causes the dc-link capacitor voltage to oscillate at 100Hz. For the single-stage system given in Fig. 1, the terminals of the PV array is directly connected across the dc link capacitor, and therefore the output voltage of the PV array, vpv(t) also oscillates at 100 Hz. The PV array current, ipv(t) and the power fed by the PV array, ppv(t) also contains ripple. The ripple content of a general time varying quantity, x(t) can be expressed as:

x(t) •x(t)%x(i) (1)

where, ~x(t) represents the ripple content, and x(t) represents the moving average component. The general quantity, x(t), can be PV array voltage, vpv(t); current, ipv(t); or power, ppv(t).

The power produced by PV arraycan be obtained by finding the product of PV array voltage and PV array current as expressed in (2)

ppv(t) •pv(t)'pv(t) (2)

By expressing vpv(t) and ipv(t) as in (1) and substituting them in (2), the PV array power can be written as

Ppv(t) •vpv(f>pv(f)«iipv(f)v pv(f)Öpv(f) ipJ(t)& pV(t) ipV(t) (3)

The power ripple can be find out by subtracting the average component of PV array power from (3), written as

ppv(t) «pv(t)v pv(t)«Vpv(t)

ipv(t)«V pv(t) ipv(t) (4)

The product of ppv(t)and v pv(t) can be expressed as

i (t) irf

~ppv(t)v~pv(t) *v~pv2(t) Wp„(t)«vpv(t) v~pv (t) HÜ-Ö pv (t) ipv(t) (5)

and also, the derivative of PV array power with respect to PV array voltage can be expressed as

dppv(t) dipV(t)

Fig. 2. PV array power and current versus PV array voltage

Linearization of v-i curve as shown in Fig. 2 at the point (vo,io) gives i&itg!idipv(t) tf-rt~ipv((tt)) (7) dvpv(t)^№vvpv

Based on (6) and (7), ppv(t)v pv(t) can be written as [12]

Ppv(t)v pv(t) *v pv2(t)-ate®-dpdvpvpv((tt))^-mHHv pv2(t) ipv(t) (8)

From (8), following things can be observed, over a cycle, as the average value of v pv2(t) ipv(t) is zero and v pv\t) is always positive, the magnitude of the average value of error signal ppv(t)v pv(t) , henceforth called as e(t), is

directly proportional to magnitude of__Since, e(t) proportional the slope of the curve between PV array power

and PV array voltage, it represents the distance of the operating point from MPP.

When the operating point is far left of MPP (Region 1 in Fig.3), the average value of e(t) is positive. When the operating point is in the left vicinity of MPP (Region 2 in Fig. 3), the average value of e(t) is positive with smaller magnitude and when the operating point is at MPP (Region 3 in Fig. 3), average of the e(t) is zero. In Region 4, the operating point is right of MPP and e(t) is negative. As the average value of error signal indicates the distance of the operating

point from MPP, the operating point can be controlled by passing the average error signal through a PI controller. The

implementation of the proposed MPPT algorithm is shown in Fig. 4. The ripplesv pv(t) and ppv(t) can be obtained by subtracting the average values from the respective signals using LPFs. The product of PV array voltage ripple and PV array power ripple is used as input to a PI controller. The output of the PI controller is considered as reference voltage signal, Vrej(t) to control the dc-link voltage [12]. The reference voltage signal is compared with the dc link voltage .

Region 1 Region 2 Region 3 Region 4 tlnle

Fig. 3. Dc-link voltage, power ripple and product of power and voltage ripple for different operating regions

Fig. 4. Block diagram representation of proposed MPPT algorithm

3. Design of Fuzzy Logic Controller

The control block diagram of fuzzy logic controller (FLC) is shown in Fig. 5. The difference between the Vref and vpv is ev given as a input to the FLC and change in load angle, AS is output of the FLC.

The design of FLC consists of three stages namely, fuzzification, design of rule base and defuzzification. Fuzzification is the process of converting a numerical variable to a linguistic variable. Seven fuzzy levels have been used for high accuracy with Gaussian membership function as shown in Fig. 6. Fuzzy levels are represented by NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), PB (positive big) and these fuzzy levels have been used in both input and output variables. The rule base of FLC is given in Table 1. Based on the rule base FLC produces output for

Fig. 5. Control block diagram of Fuzzy logic controller

Fig. 6. Membership function for (a) input ev ; (b) output AS.

Table 1. Rule base of a fuzzy logic controller.

Sl. No

Rule Base

IF ev is NB THEN AS is PB IF ev is NM THEN AS is PM IF ev is NS THEN AS is PS IF ev is Z THEN AS is Z IF ev is PS THEN AS is NS

6 IF ev is PM THEN AS is NM

7 IF ev is PB THEN AS is NB

corresponding input. Mamdani fuzzy implication has been used for evaluation of individual rules. Defuzzification is the process of converting linguistic variable to real numerical variable, centriod of area (COA) method of defuzzication has been implemented in this paper. 4. Simulation Results

The proposed MPPT algorithm with FLC has been simulated using Matlab/Simulink. The details of the PV system have been given in Table 2. The performance of the PV generation system has been evaluated for varying insolation conditions under transient as well as steady state.

Initially system is simulated with insolation of 1000 W/m2 and it is noted that PV array voltage is 411 V and PV array power 1.45 kW which corresponds to MPP for 1000 W/m2 insolation as shown in Fig. 7(a) and 7(b). Further, a step change of insolation level from 1000 W/m2 to 600 W/m2 is applied at 2 seconds and the PV array voltage and power is settled at 404 V and 0.83 kW which corresponds to MPP for insolation level of 600 W/m2 as shown in Fig. 7(a) and 7(b). From Fig. 7(a) shows that the operating point reaches the MPP less than 0.5 seconds and it is observed that the proposed MPPT technique performs satisfactorily

Table 2. PV generation system parameters.

Parameter Value

Rated power of PV array 1.45 kWp

DC link capacitance, C 1000 [J.F

AC link inductor, L 15.5 mH

PWM carrier frequency 10 kHz

Short circuit current (1000 W/m2, 25 ° C) 3.8 A

Open circuit voltage (1000 W/m2, 25 ° C) 510 V

,„, L_ V I____I___L___1__L_I__

1,9 1 IS 3 1 4 I ' ' s.S

Timr llri't

Fig. 7. Simulated performance during step change in solar insolation. (a) PV array power; (b) Reference voltage and dc link voltage

Fig. 8 shows the grid voltage and grid current at insolation level of 600 W/m2, the PV system has been designed to supply only real power to the grid. From Fig. 8 it can be noticed that the VSI feeding power to the grid with unity power factor. The total harmonic distortion (THD) in grid current has been analyzed and it is observed that THD is 2.54 % and peak value of fundamental component of grid current is 7.274 A for 1000 W/m2 insolation. For 600 W/m2 insolation condition, THD of grid current is 3.58 % and peak value of the fundamental component of grid current is 3.754 A. as shown in Fig. 9. It is observed that the THD of the grid current is within the limits for both 1000 W/m2 and 600 W/m2 insolation conditions.

40D -1-1-I-1-1-I-1-1-1-

3.5 3.51 3.5Ï 3.53 .3.54 3.55; 3,56 .3.57 3.58 3.59 3.6

Time (sec)

Fig. 8. Simulated steady state performance of the system: Grid voltage (100 V/div.) and Grid current (10 A/div.)

Fig. 8 shows the grid voltage and grid current at insolation level of 600 W/m2, the PV system has been designed to supply only real power to the grid. From Fig. 8 it can be noticed that the VSI feeding power to the grid with unity power factor. The total harmonic distortion (THD) in grid current has been analyzed and it is observed that THD is 2.54 % and peak value of fundamental component of grid current is 7.274 A for 1000 W/m2 insolation. For 600 W/m2 insolation condition, THD of grid current is 3.58 % and peak value of the fundamental component of grid current is 3.754 A. as shown in Fig. 9. It is observed that the THD of the grid current is within the limits for both 1000 W/m2 and 600 W/m2 insolation conditions.

—I--r 1 l-r 7 TIT

THD = 2.54* Fundamental - 7.274 (Peak)

i Li. 1 .....

ll ■ 1. .è 1 1 1 1 1 1 1 X I 1 1 1 .

(b) 2 1.8 2 1.6 I 1.4

Î ' ° 0 8 £ w 0 6 a

5" 0 2

THD = 3.58*. Fundamnetal ■ 3.754(Peak)

- I ill

: ; -i.l.i-l.i.i-i.l.i.i.

8 10 12 Harmonic order

8 10 12 Harmonic order

14 16 18 20

Fig. 9. Harmonic Spectrum of the grid current. (a) with 1000 W/m insolation; (b) with 600 W/m insolation

5. Conclusion

A novel MPPT technique based on ripple correlation control for with fuzzy logic controller is proposed for singlephase single-stage grid connected PV systems. The MPPT block uses the product of voltage and power ripple to drive the operating point towards MPP. The proposed algorithm with FLC is working quite satisfactorily under dynamic irradiance conditions. The efficacy of the proposed algorithm has been proved through extensive numerical simulations results. References

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