Multiple Peaks Tracking Algorithm using Particle Swarm Optimization Incorporated with Artificial Neural Network

Due to the non-linear characteristics of photovoltaic (PV) array, PV systems typically are equipped with the capability of maximum power point tracking (MPPT) feature. Moreover, in the case of PV array under partially shaded conditions, hotspot problem will occur which could damage the PV cells. Partial shading causes multiple peaks in the P-V characteristic curves. This paper presents a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) MPPT algorithm for the detection of global peak among the multiple peaks in order to extract the true maximum energy from PV panel. The PV system consists of PV array, dc-dc boost converter controlled by the proposed MPPT algorithm and a resistive load. The system was simulated using MATLAB/Simulink package. The simulation results show that the proposed algorithm performs well to detect the true global peak power. The results of the simulations are analyzed and discussed.

Authors:

• Mei Shan Ngan
• Chee Wei Tan

Keywords:

• Photovoltaic (PV)
• Partial Shading
• Maximum Power Point Tracking (MPPT)
• Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)

References:

[1] O. Santos, J.L., et al., A maximum power point tracker for PV systems
using a high performance boost converter. Solar Energy, 2006. 80(7): p.
772-778.
[2] Acciari, G., D. Graci, and A. La Scala, Higher PV Module Efficiency by
a Novel CBS Bypass. Power Electronics, IEEE Transactions on, 2011.
26(5): p. 1333-1336.
[3] Young-Hyok, J., et al., A Real Maximum Power Point Tracking Method
for Mismatching Compensation in PV Array Under Partially Shaded
Conditions. Power Electronics, IEEE Transactions on, 2011. 26(4): p.
1001-1009.
[4] Ben Salah, C. and M. Ouali, Comparison of fuzzy logic and neural
network in maximum power point tracker for PV systems. Electric
Power Systems Research, 2011. 81(1): p. 43-50.
[5] Esram, T. and P.L. Chapman, Comparison of Photovoltaic Array
Maximum Power Point Tracking Techniques. Energy Conversion, IEEE
Transactions on, 2007. 22(2): p. 439-449.
[6] Salas, V., et al., Review of the maximum power point tracking
algorithms for stand-alone photovoltaic systems. Solar Energy Materials
and Solar Cells, 2006. 90(11): p. 1555-1578.
[7] Femia, N., et al., Optimization of perturb and observe maximum power
point tracking method. Power Electronics, IEEE Transactions on, 2005.
20(4): p. 963-973.
[8] Fangrui, L., et al., A Variable Step Size INC MPPT Method for PV
Systems. Industrial Electronics, IEEE Transactions on, 2008. 55(7): p.
2622-2628.
[9] Carannante, G., et al., Experimental Performance of MPPT Algorithm
for Photovoltaic Sources Subject to Inhomogeneous Insolation.
Industrial Electronics, IEEE Transactions on, 2009. 56(11): p. 4374-
4380.
[10] S. Silvestre, a.A.C., Effects of Shadowing on Photovoltaic Module
Performance. Prog. Photovolt: Res. Appl., 2008. 16: p. 141-149.
[11] Ghoddami, H. and A. Yazdani, A Single-Stage Three-Phase
Photovoltaic System With Enhanced Maximum Power Point Tracking
Capability and Increased Power Rating. Power Delivery, IEEE
Transactions on, 2011. 26(2): p. 1017-1029.
[12] R.Ramaprabha , D.B.L.M., Impact of Partial Shading on Solar PV
Module Containing Series Connected Cells. International Journal of
Recent Trends in Engineering, 2009. Vol. 2(No. 7): p. 56-60.
[13] Giraud, F. and Z.M. Salameh, Analysis of the effects of a passing cloud
on a grid-interactive photovoltaic system with battery storage using
neural networks. Energy Conversion, IEEE Transactions on, 1999.
14(4): p. 1572-1577.
[14] Hsu, Y.-J.W.P.-C., Analytical modelling of partial shading and different
orientation of photovoltaic modules. IET Renew. Power Gener., 2010.
Vol. 4(Iss. 3): p. 272-282.
[15] Patel, H. and V. Agarwal, Investigations into the performance of
photovoltaics-based active filter configurations and their control
schemes under uniform and non-uniform radiation conditions.
Renewable Power Generation, IET, 2010. 4(1): p. 12-22.
[16] Agarwal, H.P.a.V., MATLAB-Based Modeling to Study the Effects of
Partial Shading on PV Array Characteristics. IEEE TRANSACTIONS
ON ENERGY CONVERSION, VOL. 23, NO. 1, MARCH 2008, 2008.
Vol. 23(No. 1): p. 302-310.
[17] MASAFUMI MIYATAKE, M.V., FUHITO TORIUMI, NOBUHIKO
FUJII, HIDEYOSHI KO, Maximum Power Point Tracking of Multiple
Photovoltaic Arrays: A PSO Approach. IEEE TRANSACTIONS ON
AEROSPACE AND ELECTRONIC SYSTEMS, 2011. Vol 47(No. 1).
[18] Phimmasone, V.K., Y.; Kamejima, T.; Miyatake, M.; , Evaluation of
extracted energy from PV with PSO-based MPPT against various types
of solar irradiation changes 2010 International Conference on Electrical
Machines and Systems (ICEMS), 2010: p. 487-492.
[19] Low, T.L.N.a.K.-S., A Global Maximum Power Point Tracking Scheme
Employing DIRECT Search Algorithm for Photovoltaic Systems IEEE
TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2010. Vol.
57(No. 10): p. 3456-3467.
[20] Engin Karatepe, T.H., Mutlu Boztepe, Metin C┬© olak Voltage based
power compensation system for photovoltaic generation system under
partially shaded insolation conditions. Energy Conversion and
Management, 2008. 49: p. 2307-2316.
[21] Agarwal, H.P.a.V., Maximum Power Point Tracking Scheme for PV
Systems Operating Under Partially Shaded Conditions. IEEE
TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO.
4, APRIL 2008, 2008. Vol. 55(No. 4): p. 1689-1698.
[22] Bai, Q., Analysis of Particle Swarm Optimization Algorithm. Computer
and Information Science, 2010. 3(1): p. 180-184.
[23] Wijnings, P., Training neural networks with particle swarm
optimization. March 12, 2011. p. 60.
[24] Daniel Merkle, M.M., Swarm Intelligence, in Book Chapter 14. p. 37.
[25] Ze Cheng, H.Z., Hongzhi Yang, Research on MPPT control of PV
system based on PSO algorithm. Control and Decision Conference
(CCDC), 2010 Chinese, 2010: p. 887-892.
[26] Larsen, J., Introduction to Artificial Neural Network. Book chapter, 1st
ed. 1999.
[27] Phan Quoc, D., et al. The new MPPT algorithm using ANN-based PV.
in Strategic Technology (IFOST), 2010 International Forum on. 2010.
[28] C.-C. YANG, S.O.P., J.-A. LANDRY, H.S. RAMASWAMY and A.
DITOMMASO, Application of artificial neural networks in image
recognition and classification of crop and weeds. Canadian Agricultural
Engineering 2000. 42(3): p. 147-152.
[29] PremChand Kumar, E.W., Cash Forecasting: An Application of
Artificial Neural Networks in Finance. International Journal of
Computer Science & Applications, 2006. III(1): p. 61-67.
[30] Haykin, S., Feedforward Neural Network: An Introduction. Book
chapter, p. 1-16.