Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions

Sanaz Jalali Zand, Kuo-Hsien Hsia, Naser Eskandarian and Saleh Mobayen
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Sanaz Jalali Zand: Faculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, Iran
Kuo-Hsien Hsia: Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Naser Eskandarian: Faculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, Iran
Saleh Mobayen: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

Energies, 2021, vol. 14, issue 5, 1-14

Abstract: This paper presents a new version of the incremental conductance algorithm for more accurate tracking of the maximum power point (MPP). The modified algorithm is called self-predictive incremental conductance (SPInC), and it recognizes the operational region. It is capable of detecting dynamic conditions, and it detects sudden changes in power resulting from changes in the intensity of radiation or temperature. By selecting the appropriate step size, it obtains maximum power from the panel at any moment. The improved algorithm reduces output power ripple and increases the efficiency of the system by detecting the operating area and selecting the appropriate step size for each region. The SPInC algorithm divides the system’s work areas into three operating zones. It calculates the size of the appropriate step changes for each region after identifying the regions, which allows for more accurate tracking of the MPP and increases the system efficiency at a speed equal to the speed of the conventional method. These additional operations did not result in a system slowdown in the tracking maximum power. According to the MATLAB/Simulink simulation results, the SPInC algorithm is more efficient than conventional InC, and the ripple output power is reduced. SPInC is also compared to the improved perturb and observe (P&O) algorithm. In general, SPInC can compete with the popular algorithms that have been recently proposed for MPPT in the other researches.

Keywords: incremental conductance (InC); self-predictive incremental conductance (SPInC); maximum power point (MPP); maximum power point tracking (MPPT); perturb and observe (P& O) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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