Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions
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 condi...
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doaj-e7a2d2c8eab649b69b42c3fc68d106cf2021-02-25T00:03:35ZengMDPI AGEnergies1996-10732021-02-01141234123410.3390/en14051234Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic ConditionsSanaz Jalali Zand0Kuo-Hsien Hsia1Naser Eskandarian2Saleh Mobayen3Faculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, IranBachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanFaculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, IranFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanThis 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.https://www.mdpi.com/1996-1073/14/5/1234incremental conductance (InC)self-predictive incremental conductance (SPInC)maximum power point (MPP)maximum power point tracking (MPPT)perturb and observe (P&O) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanaz Jalali Zand Kuo-Hsien Hsia Naser Eskandarian Saleh Mobayen |
spellingShingle |
Sanaz Jalali Zand Kuo-Hsien Hsia Naser Eskandarian Saleh Mobayen Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions Energies incremental conductance (InC) self-predictive incremental conductance (SPInC) maximum power point (MPP) maximum power point tracking (MPPT) perturb and observe (P&O) |
author_facet |
Sanaz Jalali Zand Kuo-Hsien Hsia Naser Eskandarian Saleh Mobayen |
author_sort |
Sanaz Jalali Zand |
title |
Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions |
title_short |
Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions |
title_full |
Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions |
title_fullStr |
Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions |
title_full_unstemmed |
Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions |
title_sort |
improvement of self-predictive incremental conductance algorithm with the ability to detect dynamic conditions |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-02-01 |
description |
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. |
topic |
incremental conductance (InC) self-predictive incremental conductance (SPInC) maximum power point (MPP) maximum power point tracking (MPPT) perturb and observe (P&O) |
url |
https://www.mdpi.com/1996-1073/14/5/1234 |
work_keys_str_mv |
AT sanazjalalizand improvementofselfpredictiveincrementalconductancealgorithmwiththeabilitytodetectdynamicconditions AT kuohsienhsia improvementofselfpredictiveincrementalconductancealgorithmwiththeabilitytodetectdynamicconditions AT nasereskandarian improvementofselfpredictiveincrementalconductancealgorithmwiththeabilitytodetectdynamicconditions AT salehmobayen improvementofselfpredictiveincrementalconductancealgorithmwiththeabilitytodetectdynamicconditions |
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