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Volume 18, Issue 1
  • ISSN: 2213-1116
  • E-ISSN: 2213-1132

Abstract

Background

Machine Learning (ML) techniques have successfully replaced traditional control algorithms in recent years due to their ability to carry out complicated tasks with significant efficiency and accuracy.

Objective

The main objective of the current work is to investigate and compare the performances of different ML models in modeling Maximum Power Point Tracking (MPPT) control for a wind turbine system. The main advantage of the designed MPPT based on ML is that it does not require any detailed mathematical model or prior knowledge of the system, such as turbine parameters or aerodynamic properties, unlike traditional MPPT techniques.

Methods

The ML models included in this study were Support Vector Machines, Regression Trees, and Ensemble Trees. Their design was performed through a training process, and their performances were evaluated based on various metrics. During the training phase, the ML models were selected to understand the basic concept of the control strategy and extract essential hidden connections between the inputs and the output of the system.

Results

The effectiveness of the control method was investigated using MATLAB/Simulink. The findings of this study revealed that ML models were effective in modeling the MPPT for the studied wind power system, which provides an interesting and sophisticated alternative to classical control methods for wind systems.

Conclusion

The ML models designed allow for optimal operation of the system with a simple structure that is independent of system parameters and wind speed measurement and is adaptable for any kind of system.

© 2025 Bentham Science Publishers
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2025-01-01
2024-12-26

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/content/journals/eeng/10.2174/2352096516666230803144411
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An Intelligent Maximum Power Point Tracking Strategy for a Wind Energy Conversion System using Machine Learning Algorithms
Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) 18, 50; https://doi.org/10.2174/2352096516666230803144411
/content/journals/eeng/10.2174/2352096516666230803144411
/content/journals/eeng/10.2174/2352096516666230803144411
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  • Article Type:     Research Article
Keyword(s): Intelligent power control; machine learning; MATLAB/Simulink; maximum power point tracking; regression tree; support vector machine
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