MPPT Control Method for Photovoltaic System Based on Particle Swarm Optimization and Bacterial Foraging Algorithm
International Journal of Electrical Components and Energy Conversion
Volume 4, Issue 1, June 2018, Pages: 45-49
Received: Apr. 12, 2018;
Accepted: Apr. 27, 2018;
Published: May 19, 2018
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Zhiguo Zhu, Department of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
Guowei Liu, Department of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
The P-V output feature of photovoltaic (PV) array presents multi-wave peaks under non-uniform illumination, so the traditional algorithm can not overcome the shortcomings of the local optimal value. In this paper, an optimization algorithm based on particle swarm and bacteria foraging is proposed, which is applied to the maximum power point tracking (MPPT) of PV arrays. The algorithm introduces the tendency operation to find the optimal solution in the local range. The replication operation is introduced to avoid the blind randomness of population update, and the convergence speed of the algorithm is accelerated. The migration operation is introduced to avoid the algorithm falling into the local optimal solution. The output power characteristics of PV array under occlusion are analyzed, and the MPPT control method experiment is carried out using bacterial foraging algorithm (BFA). Experimental results show that the algorithm can get rid of the constraint of local optimal value, quickly find the global maximum power point, and the control precision is high. It provides a new implementation method for PV array MPPT.
MPPT Control Method for Photovoltaic System Based on Particle Swarm Optimization and Bacterial Foraging Algorithm, International Journal of Electrical Components and Energy Conversion.
Vol. 4, No. 1,
2018, pp. 45-49.
D. Teshome, C. H. Lee, Y. W. Lin, and K. L. Lian, “A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading,” IEEE J. Emerging Sel. Topics Power Electron., vol. 5, no. 2, pp. 661–671, Jun. 2017.
R. Koad, A. F. Zobaa, and A. El Shahat, “A novel MPPT algorithm basedon particle swarm optimisation for photovoltaic systems,” IEEE Trans. Sustainable Energy, vol. 8, no. 2, pp. 468–476, Apr. 2017.
S. Mohanty, B. Subudhi, and P. K. Ray, “A new mppt design using grey wolf optimization technique for photovoltaic system under partial shading conditions,” IEEE Trans. Sustain. Energy, vol. 7, no. 1, pp. 181–188, Jan.2016.
Oliveira, F. M., Silva, S. A. O., Durand, F. R., et al.: ‘Grid-tied photovoltaic system based on PSO MPPT technique with active power line conditioning’,IET Power Electron., 2016, 9, (6), pp. 1180–1191
Renaudineau, H., Donatantonio, F., Fontchastagner, J., et al.: ‘A PSO-based global MPPT technique for distributed PV power generation’, IEEE Trans. Ind. Electron., 2015, 62, (2), pp. 1047–1058
Sérgio, A. O, S., Leonardo, P, S., Fernando M, O., et al.: ‘Feed-forward DC-bus control loop applied to a single-phase grid-connected PV system operating with PSO-based MPPT technique and active power-line conditioning’, IET Renew. Power Gener., 2017, Vol. 11 Iss. 1, pp. 183-193
A. Ramyar, H. Iman-Eini, and S. Farhangi,“Global maximum powerpoint tracking method for photovoltaic arrays under partial shading conditions,”IEEE Trans. Ind. Electron., vol. 64, no. 4, pp. 2855–2864, Apr. 2017.
M. Seyedmahmoudian et al., “Simulation and hardware implementation of new maximum powerpoint tracking technique for partially shaded PV system using hybrid DEPSO method,” IEEE Trans. Sustain. Energy, vol. 6, no. 3, pp. 850–862, Jul. 2015.
Nishant, K., Ikhlaq H., Bhim S., Bijaya, K. P., “Single sensor based MPPT for partially shaded solar photovoltaic by using human psychology optimisation algorithm,” IET Gener. Transm. Distrib., 2017, Vol. 11 Iss. 10, pp. 2562-2574
Mohanty, S., Subudhi, B., Ray, P. K.: ‘A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 181–188
Neeraja Krishnakumar, Rini Venugopalan, N. Rajasekar, “Bacterial Foraging Algorithm based parameter estimation of solar PV model,” International Conference on Microelectronics, Communication and Renewable Energy, 2013,pp. 1–6