TY - JOUR
T1 - A novel modified sine-cosine optimized MPPT algorithm for grid integrated PV system under real operating conditions
AU - Padmanaban, Sanjeevikumar
AU - Priyadarshi, Neeraj
AU - Holm-Nielsen, Jens Bo
AU - Sagar Bhaskar, Mahajan
AU - Azam, Farooque
AU - Sharma, Amarjeet Kumar
AU - Hossain, Eklas
PY - 2019/1
Y1 - 2019/1
N2 - This research work presents a modified sine-cosine optimized maximum power point tracking (MPPT) algorithm for grid integration. The developed algorithm provides the maximum power extraction from a photovoltaic (PV) panel and simplified implementation with a benefit of high convergence velocity. Moreover, the performance and ability of the modified sine-cosine optimized (MSCO) algorithm is equated with recent particle swarm optimization and artificial bee colony algorithms for comparative observation. Practical responses is analyzed under steady state, dynamic, and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO-based MPPT algorithm always shows fast convergence rate, easy implementation, less computational burden and the accuracy to track the optimal PV power under varying weather conditions. The experimental results provided in this paper clearly show the validation of the proposed algorithm.
AB - This research work presents a modified sine-cosine optimized maximum power point tracking (MPPT) algorithm for grid integration. The developed algorithm provides the maximum power extraction from a photovoltaic (PV) panel and simplified implementation with a benefit of high convergence velocity. Moreover, the performance and ability of the modified sine-cosine optimized (MSCO) algorithm is equated with recent particle swarm optimization and artificial bee colony algorithms for comparative observation. Practical responses is analyzed under steady state, dynamic, and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO-based MPPT algorithm always shows fast convergence rate, easy implementation, less computational burden and the accuracy to track the optimal PV power under varying weather conditions. The experimental results provided in this paper clearly show the validation of the proposed algorithm.
KW - Artificial bee colony
KW - Maximum power point tracking
KW - Particle swarm optimization
KW - Photovoltaic
KW - Sine-cosine optimized
UR - http://www.scopus.com/inward/record.url?scp=85061107512&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2890533
DO - 10.1109/ACCESS.2018.2890533
M3 - Journal article
AN - SCOPUS:85061107512
SN - 2169-3536
VL - 7
SP - 10467
EP - 10477
JO - IEEE Access
JF - IEEE Access
M1 - 8598859
ER -