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A novel mppt method based on cuckoo search algorithm and golden section search algorithm for partially shaded pv system
Nugraha D.A.a,b,c, Lian K.L.a, Suwarno S.b
a Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40116, Indonesia
c PT PLN (Persero), Jakarta, 12160, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2003-2010 IEEE Canada.Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some metaheuristic (MH) algorithm to track the multiple-peak P – V curve of partially shaded PV system. Cuckoo search (CS) is a new optimization algorithm based on the MH approach. It has been used to solve an optimization problem in many applications, including the maximum power point tracking (MPPT) problem. The CS algorithm performs well in tracking the global maximum power point (GMPP). However, just like any other MH algorithm, there is still a dilemmatic trading between their accuracy and the tracking time needed to find GMPP. This paper proposes a new MPPT algorithm by combining the CS algorithm with golden section search (GSS) to take beneficial features from both the algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental results show a noticeable performance improvement compared with the original CS algorithm and other MH algorithms.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cuckoo searches,Golden section search,Maximum Power Point Tracking,Partial shading,Photovoltaic systems[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cuckoo search (cs),Golden section search (gss),Maximum power point tracking (mppt),Optimization,Partial shading,Photovoltaic (pv) system[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][{‘$’: ‘Manuscript received October 30, 2018; accepted April 29, 2019. Date of current version July 23, 2019. This work was supported in part by MOST under Grant NSC 106-2221-E-011-100 and in part by the Taiwan Building Technology Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education, Taiwan. This paper was presented in part at the 18th IEEE Canada Electrical Power and Energy Conference (EPEC 2018), Toronto, ON, Canada, 2018. (Corresponding author: K. L. Lian.) D. A. Nugraha is with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan, also with the School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40116, Indonesia, and also with PT PLN (Persero), Jakarta 12160, Indonesia (e-mail: d.ajinugraha@gmail.com).’}, {‘$’: ‘This work was supported in part by MOST under Grant NSC 106-2221-E-011-100’}][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/CJECE.2019.2914723[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]