Maximum Power Point Tracking Using Neural Network in Flyback MPPT inverter for PV systems

Poom Konghuayrob Faculty of Engineering,

King Mongkut’s Insitute of Technology Ladkrabang Thailand.

poom.konghuayrob@gmail.com

Somyot Kaitwanidvilai Faculty of Engineering,

King Mongkut’s Insitute of Technology Ladkrabang Thailand.

drsomyotk@gmail.com

Abstract— Generally, perturb and observe (P&O) technique is widely adopted in photovoltaic (PV) system to maximize the output power. In flyback inverter, the modulation index needs to be adjusted based on the P&O algorithm. However if the changing step size of modulation index (∆ma) is too large, the fast MPP (Maximum Power Point) tracking can be achieved but the power oscillation around the MPP will be large. In contrary, the small changing step size results in long tracking time and small oscillation. Consequently, this paper proposes a technique to adjust the changing step size (∆ma) of Flyback inverter to achieve both acceptable tracking time and low power oscillation. In the proposed technique, irradiance is adopted as the input of a neural network which is used to estimate the appropriate modulation step size. Simulation results confirm that the proposed neural network based inverter can find the appropriate changing step size (∆ma) which is adequate for any irradiance conditions.

Keywords- flyback; MPPT; optimization; neural

I. INTRODUCTION Due to the growing demand of electrical energy and

environmental issues such as pollution and global warming effect, solar energy is considered as a technological option for generating clean energy. Among them, photovoltaic (PV) system has received a great attention as it appears to be one of the most promising renewable energy sources. Recently, due to its development and cost reduction, PV system becomes an efficient solution to the environmental problem [1].

The output characteristics of photovoltaic arrays are nonlinear with the temperature and solar irradiance. For a given condition, there is a unique point in which the array can produce the maximum output power [2]. This point is called maximum power point (MPP) which depends on temperature and the irradiation level. To obtain the maximum power from a photovoltaic array, a maximum power point tracker (MPPT) is usually adopted. The issue of maximum power point tracking (MPPT) has been addressed in different ways in the literatures [3-7]; several techniques such as fuzzy logic control (FLC), artificial intelligence (AI), neural networks, pilot cells, incremental conductance (INC) and DSP based implementations have been proposed. Among the various techniques, the Perturb and Observe (P&O) algorithm is one of the most common methods due to its ease of implementation. However,

the choosing of appropriate step size of perturbation is an interesting problem in this technique.

In PV-flyback inverter system, the operating voltage of the PV array is perturbed by changing the modulation index in a given direction (increase or decrease) and the power drawn from PV is then be observed. If the power increases, the operating modulation is further perturbed in the same direction. In contrary, if the power decreases, the direction of perturbation is opposite [3,6]. Nevertheless, to obtain more adequate MPPT behavior, it is necessary to select the appropriate modulation step size (∆ma) for adjusting the power in each iteration of P&O technique [8, 9]. The performance of MPP tracking can be measured by the tracking time and the oscillation at the MPP. Unfortunately, the adequate step size at each irradiance condition is not the same. In addition, there is no general rule for identification of the optimal value of step size in P&O technique. Most of researchers choose the value by trial and error test. To solve this problem, this paper presents the adjustment of modulation step size technique of flyback inverter by finding the intersection point between rescaled tracking time and power oscillation at each selected irradiance. Moreover, we adopted the neural network to estimate the modulation step size based on the intersection points. The simulation study in this paper was performed on MATLAB and Simulink [10].

II. OPTIMIZATION ∆MA BASED ON P&O TECHNIQUE A. Perturb and Observation (P&O)

The main concept of "perturb and observe" (P&O) method is the modification of operating voltage or current of the photovoltaic panel to obtain the maximum power. Following equation describes the change of power which defines the strategy of the P&O technique [3].

∆P = Pk-Pk-1 (1)

If the change of power defined in (1) is positive, the system will keep the direction of the incremental current as the same direction. In contrary, if the change is negative, the system will change the direction of incremental current command to the opposite direction. This method is able to efficiently work in the steady state condition (the radiation and

SCIS-ISIS 2012, Kobe, Japan, November 20-24, 2012

978-1-4673-2743-5/12/$31.00 ©2012 IEEE 1504

temperature conditions change slowly). However, the major drawback of the P&O method is the power obtained oscillates around the maximum power point in steady state operation. Also, it may track in the wrong direction under rapidly varying irradiance and electrical load level. The step size (the magnitude of change in the operating voltage) affects to both the speed of convergence to the MPP and the amount of oscillation around the MPP at steady state operation. Flow chart of the P&O method is described in Fig. 1.

Δ

Figure 1. Flow chart of ordinary P&O method

B. Selection of the modulation step size (∆ma) Normally, trial and error method is used to select ∆ma

(modulation index in inverter) or ∆d (duty cycle in DC-DC converter) [8]. If the modulation step size is too large, the tracking time is fast but the power oscillation around the MPP will be large. This behavior is clearly shown in Fig 2. As seen in this figure, higher modulation step size results in higher oscillation and tracking speed. Fig 3 shows the meaning of tracking time and power oscillation defined in this paper.

Figure 2. Average power curves at ∆ma 0.08, 0.022 and 0.002 (irradiance = 800 W/m2)

Figure 3. power oscillation and tracking time

Power oscillation and tracking time at each irradiance can be evaluated by varying ∆ma from 0.01 to 0.1. In this paper, the objective function used for selecting the appropriate ∆ma is:

F(x) = mA(∆ma) + B(∆ma) (2)

F(x) is the objective function for finding the appropriate ∆ma, m is the selected weight, A(∆ma) is the function of power oscillation and B(∆ma) is the function of the tracking time. As seen in Fig.4 , the weight m is selected as 0.34 and the appropriate ∆ma is 0.021.

Fig.5 shows the appropriate ∆ma at different insolation level. For example, the appropriate ∆ma for the insolation levels of 300, 600 and 900 W/m2 are 0.021, 0.033 and 0.024, respectively. The data of irradiance and appropriate ∆ma are adopted as the training set of the proposed ANN.

Figure 4. Objective function F versus the ∆ma (at irradiance 300 W/m2)

SCIS-ISIS 2012, Kobe, Japan, November 20-24, 2012

978-1-4673-2743-5/12/$31.00 ©2012 IEEE 1505

Figure 5. Objective function F at irradiation levels of 300, 600 and 900 W/m2

Figure 6. Input, Hidden and Output layer of the proposed Neural Network

C. Artificial neural network based ∆ma adjustment The appropriate ∆ma from various irradiance conditions are

adopted as the training set of the proposed ANN. In the proposed system, ANN is adopted for approximating the appropriate value of ∆ma for a given irradiance level. Irradiance level from a light sensor is the input of the proposed ANN; ∆ma is the output of the proposed ANN. In the proposed design, the number of nodes of hidden layer is selected as 20 and Back Propagation (BP) method is used to train the network. Fig. 6 shows the proposed ANN.

III. SIMULATION RESULTS The trained ANN and Flyback inverter [11] have been

modeled and simulated by using Matlab/SIMULINK. Flyback inverter developed in this paper is operated in discontinuous mode to achieve high voltage utilization. Fig. 7 shows the developed simulink model. In the simulation study, the appropriate ∆ma based on the proposed ANN P&O method is evaluated by the developed MPPT function. The result of MPP

tracking is investigated in comparison with the conventional P&O tracking under several varying insolation levels (from 500 to 900 W/m2). The specification of PV module adopted in the simulation are shown in Table I.

TABLE I. THE SPECIFICATION OF PV MODULE THAT USED IN THE SIMULATION

Open circuit voltage 21 V

Short circuit current 7.8 A

Volt at PMAX 12.7 V

Current at PMAX 6.72 A

Figure 7. The developed model

The performance of MPPT using the proposed ANN and conventional technique (Fixed ∆ma) is verified by varying the irradiance from 500 to 900 W/m2. As seen in all figures, the power oscillation and tracking time from the proposed technique are less than those of the conventional technique. Figs. 8(a), 8(b) and 8(c) show the modulation index, instantaneous input power and average power of the proposed system. As seen in these figures, the average power is calculated at each period 10 ms which is the period of absolute sinusoidal waveform. The absolute sinusoidal waveform is the reference signal in control circuit of the proposed flyback inverter.

Figure 8. Simulation results of flyback inverter at irradiance level 825 W/m2 (a) modulation index, ma (b) instantaneous input power and (c) average power

(a)

(b)

(c)

output input

SCIS-ISIS 2012, Kobe, Japan, November 20-24, 2012

978-1-4673-2743-5/12/$31.00 ©2012 IEEE 1506

Figure 9. Power versus time of Flyback inverter at irradiance 625 W/m2

Figure 10. Power versus time of Flyback inverter at irradiance 725 W/m2

Figure 11. Power versus time of Flyback inverter at irradiance 825 W/m2

Figs. 9-11 show the time domain responses of power of Flyback inverter at irradiance levels 625, 725 and 825 W/m2, respectively. Comparing with the proposed technique, the power oscillation from the ANN based inverter is lower than that of the conventional fixed modulation index in various irradiance levels. Tracking time from all techniques is less than 0.15 second which is acceptable for this application. Consequently, in this study, tracking time is not significant factor for considering in performance comparison.

TABLE II. POWER OSCILLATION OF THE PROPOSED AND CONVENTIONAL TECHNIQUES

Irradiance Levels (W/m2)

Proposed Technique

Conventional Technique

ΔP (W) ΔP (W)

625 2.4 4.3

725 1.7 3.7

825 0.5 8.3

IV. CONCLUTION This paper presents the technique for approximating the

appropriate ∆ma for all insolation levels. The proposed method adopts the weight function and ANN to find the appropriate ∆ma that achieves both the good power oscillation and the acceptable tracking time for all irradiance levels. The simulation results verify the effectiveness of the proposed system. The comparison with the conventional fixed modulation step size shows that the proposed technique is superior to the conventional technique. As seen in Table II, the power oscillation from the proposed system is less than that of the conventional technique for all tested irradiance levels.

However, the effects of abient temperature and electrical load are not taken into account in this study. These issues will be studied in the next research work.

ACKNOWLEDGMENT This work was supported by King Mongkut’s Institute of

Technology Ladkrabang Research Fund.

REFERENCES [1] Chuanzong Fu and Shiping Su, “Simulation studying of MPPT control

by a new method for photovoltaic power system,” Electrical and Control Engineering (ICE) 2011., pp. 1274-1278, 2011.

[2] S. Liu and R. A. Dougal, “Dynamic multiphysics model for solar array,” IEEE Trans., On Energy conversion., Vol. 17, No. 2, pp.285-294, 2002.

[3] Pongsakor Takun, Somyot Kaitwanidvilai, “Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems,” IMECS 2011., Vol. 2, pp. 986-990, 2011.

[4] B. Amrouche, M. Belhamel and A. Guessoum, “Artificial intelligence based P&O MPPT method for photovoltaic systems,” Revue des Energies Renouvelables ICRESD-07 Tlemcen 2007., pp. 11-16, 2007.

[5] S. Premrudeepreechachain and N. Patanapirom, “Solar Array Modelling and Maximum Power Tracking Using Neural Networks,” IEEE Power Tech Conference, Bologna, Italy., pp. 53–68, 2003.

[6] I. Batarseh, T. Kasparis, K. Rustom, etc. “DSP-based Multiple Peak Power Tracking for Expandable Power System”, Applied Power Electronics Conference and Exposition, 2003. APEC ‘03. Eighteenth Annual IEEE., Vol. 1, pp. 525 – 530, 2003.

[7] Jae Ho Lee , HyunSu Bae and Bo Hyung Cho, “Advanced Incremental Conductance MPPT Algorithm with a Variable Step Size,” Power Electronics and Motion Control Conference 2006., pp. 603–607, 2006.

[8] Nicola Femia, Giovanni Petrone, Giovanni Spagnuolo, etc. “Optimization of Perturb and Observe Maximum PowerPoint Tracking Method,” IEEE Trans. on Power Electronics., Vol. 20, No. 4, pp. 963-973, 2005.

[9] N. Femia, G. Petrone, G. Spagnolo and M. Vitelli, “Optimizing Duty-Cycle Perturbation of P&O MPPT Technique,” 35th Annual IEEE Power Electronocs., Vol. 3, pp. 1939-1944, 2004.

[10] I. H. Altas and A.M. Sharaf, “A Photovoltaic Array Simulation Model for Matlab-Simulink GUI Environment,” Clean Electrical Power, 2007. ICCEP '07. International Conference on., pp. 341-345, 2007.

[11] Kyritsis, A.Ch.; Tatakis, E.C.; Papanikolaou and N.P, “Optimum Design of the Current-Source Flyback Inverter for Decentralized Grid-Connected Photovoltaic Systems,” Energy Conversion, IEEE Transactions on ., Vol. 23, No. 1, pp. 281–293, 2008.

SCIS-ISIS 2012, Kobe, Japan, November 20-24, 2012

978-1-4673-2743-5/12/$31.00 ©2012 IEEE 1507