Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

Mpho Sam Nkambule; Ali N. Hasan; Ahmed Ali

doi:10.1109/EI256261.2022.10117177

Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

Mpho Sam Nkambule
, Ali N. Hasan
, Ahmed Ali

Electrical and Electronic Engineering Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Photovoltaic (PV) farms can supply vital merits to the electrical systems economically and environmentally. Nevertheless, the variation of the PV power leads to technical challenges under partially shaded conditions (PSC). For this reason, Global maximum power point (GMPP) tracking represents a critical mechanism for power optimization uncertainty to guarantee system stability. In this study, a novel hybrid algorithm for GMPP tracking for a PV system under PSC is presented. The proposed hybrid system integrates machine learning (ML) Ensemble RUSBoosted tree (ERBT) and Metaheuristic Linear Programming (LP) to solve optimization problems under PSC with a fast response time and good convergence speed. The integration of ERBT-LP combines the benefits of the two algorithms into a single model. The presented algorithms have been tested and validated using MATLAB SIMULINK environment with very promising results.

Original language	English
Title of host publication	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1538-1544
Number of pages	7
ISBN (Electronic)	9798350347159
DOIs	https://doi.org/10.1109/EI256261.2022.10117177
Publication status	Published - 2022
Event	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 - Chengdu, China Duration: 11 Nov 2022 → 13 Nov 2022

Publication series

Name	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

Conference

Conference	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022
Country/Territory	China
City	Chengdu
Period	11/11/22 → 13/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Ensemble RUSBoosted Tree (ERBT)
Linear Programming (LP)
Maximum Power Point Tracking (MPPT)
Partial Shading Conditions (PSC)
Photovoltaic (PV)

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization
Computer Networks and Communications

Access to Document

10.1109/EI256261.2022.10117177

Cite this

Nkambule, M. S., Hasan, A. N., & Ali, A. (2022). Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions. In EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration (pp. 1538-1544). (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI256261.2022.10117177

Nkambule, Mpho Sam ; Hasan, Ali N. ; Ali, Ahmed. / Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions. EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1538-1544 (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration).

@inproceedings{92e8b62a896447fca641e1197f2e9cca,

title = "Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions",

abstract = "Photovoltaic (PV) farms can supply vital merits to the electrical systems economically and environmentally. Nevertheless, the variation of the PV power leads to technical challenges under partially shaded conditions (PSC). For this reason, Global maximum power point (GMPP) tracking represents a critical mechanism for power optimization uncertainty to guarantee system stability. In this study, a novel hybrid algorithm for GMPP tracking for a PV system under PSC is presented. The proposed hybrid system integrates machine learning (ML) Ensemble RUSBoosted tree (ERBT) and Metaheuristic Linear Programming (LP) to solve optimization problems under PSC with a fast response time and good convergence speed. The integration of ERBT-LP combines the benefits of the two algorithms into a single model. The presented algorithms have been tested and validated using MATLAB SIMULINK environment with very promising results.",

keywords = "Ensemble RUSBoosted Tree (ERBT), Linear Programming (LP), Maximum Power Point Tracking (MPPT), Partial Shading Conditions (PSC), Photovoltaic (PV)",

author = "Nkambule, \{Mpho Sam\} and Hasan, \{Ali N.\} and Ahmed Ali",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 ; Conference date: 11-11-2022 Through 13-11-2022",

year = "2022",

doi = "10.1109/EI256261.2022.10117177",

language = "English",

series = "EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration",

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}

Nkambule, MS, Hasan, AN & Ali, A 2022, Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions. in EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration, Institute of Electrical and Electronics Engineers Inc., pp. 1538-1544, 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022, Chengdu, China, 11/11/22. https://doi.org/10.1109/EI256261.2022.10117177

Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions. / Nkambule, Mpho Sam; Hasan, Ali N.; Ali, Ahmed.
EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1538-1544 (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

AU - Nkambule, Mpho Sam

AU - Hasan, Ali N.

AU - Ali, Ahmed

PY - 2022

Y1 - 2022

N2 - Photovoltaic (PV) farms can supply vital merits to the electrical systems economically and environmentally. Nevertheless, the variation of the PV power leads to technical challenges under partially shaded conditions (PSC). For this reason, Global maximum power point (GMPP) tracking represents a critical mechanism for power optimization uncertainty to guarantee system stability. In this study, a novel hybrid algorithm for GMPP tracking for a PV system under PSC is presented. The proposed hybrid system integrates machine learning (ML) Ensemble RUSBoosted tree (ERBT) and Metaheuristic Linear Programming (LP) to solve optimization problems under PSC with a fast response time and good convergence speed. The integration of ERBT-LP combines the benefits of the two algorithms into a single model. The presented algorithms have been tested and validated using MATLAB SIMULINK environment with very promising results.

AB - Photovoltaic (PV) farms can supply vital merits to the electrical systems economically and environmentally. Nevertheless, the variation of the PV power leads to technical challenges under partially shaded conditions (PSC). For this reason, Global maximum power point (GMPP) tracking represents a critical mechanism for power optimization uncertainty to guarantee system stability. In this study, a novel hybrid algorithm for GMPP tracking for a PV system under PSC is presented. The proposed hybrid system integrates machine learning (ML) Ensemble RUSBoosted tree (ERBT) and Metaheuristic Linear Programming (LP) to solve optimization problems under PSC with a fast response time and good convergence speed. The integration of ERBT-LP combines the benefits of the two algorithms into a single model. The presented algorithms have been tested and validated using MATLAB SIMULINK environment with very promising results.

KW - Ensemble RUSBoosted Tree (ERBT)

KW - Linear Programming (LP)

KW - Maximum Power Point Tracking (MPPT)

KW - Partial Shading Conditions (PSC)

KW - Photovoltaic (PV)

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DO - 10.1109/EI256261.2022.10117177

M3 - Conference contribution

AN - SCOPUS:85160204976

T3 - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

SP - 1538

EP - 1544

BT - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

Y2 - 11 November 2022 through 13 November 2022

ER -

Nkambule MS, Hasan AN, Ali A. Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions. In EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1538-1544. (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration). doi: 10.1109/EI256261.2022.10117177

Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this