Comparative Analysis of MPPT Controllers for Boost Converter in Solar PV Systems: PO, Incremental Conductance, and ANN

In order to maximise the performance of solar PV systems combined with a boost converter, this Paper provides a comparative examination of several Maximum Power Point Tracking (MPPT) approaches, namely Incremental Conductance, Perturb and Observe, and Artificial Neural Network. Extensive MATLAB simulations were conducted to evaluate the output voltage, settling time, and duty cycle of each method under varying input conditions. The results demonstrate that while all methods successfully achieve the desired output voltage of 390V, the ANN method outperforms the others by providing precise tracking with minimal oscillation and a faster settling time of 0.03 seconds. In contrast, the IC and P&O methods, despite achieving similar output voltages, exhibit oscillations and longer settling times. These results demonstrate how well using a boost converter in combination with ANN-based MPPT may improve the stability and efficiency of solar energy systems. The report offers insightful information about the development and application of renewable energy technology.

Authors

Institutions

Publication Details

Published
2024-12-10
DOI
https://doi.org/10.1109/piicon63519.2024.10995194
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative Analysis of MPPT Controllers for Boost Converter in Solar PV Systems: PO, Incremental Conductance, and ANN

Ram Krishan, Maneesh Kumar, Rajababu Durgam
Photovoltaic System Optimization Techniques
article

Comparative Analysis of MPPT Controllers for Boost Converter in Solar PV Systems: PO, Incremental Conductance, and ANN

Ram Krishan, Maneesh Kumar, Rajababu Durgam
article en

Abstract

In order to maximise the performance of solar PV systems combined with a boost converter, this Paper provides a comparative examination of several Maximum Power Point Tracking (MPPT) approaches, namely Incremental Conductance, Perturb and Observe, and Artificial Neural Network. Extensive MATLAB simulations were conducted to evaluate the output voltage, settling time, and duty cycle of each method under varying input conditions. The results demonstrate that while all methods successfully achieve the desired output voltage of 390V, the ANN method outperforms the others by providing precise tracking with minimal oscillation and a faster settling time of 0.03 seconds. In contrast, the IC and P&O methods, despite achieving similar output voltages, exhibit oscillations and longer settling times. These results demonstrate how well using a boost converter in combination with ANN-based MPPT may improve the stability and efficiency of solar energy systems. The report offers insightful information about the development and application of renewable energy technology.

National Institute of Technology Warangal (IN)
Affordable and clean energy
Openalex Percentile: Top 83%
Photovoltaic System Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.