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  3. Volume 6, Number 1, 2024
  4. Comparative Analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Panels

Comparative Analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Panels

SEPES.
2024;
Volume 6, Number 1
: pp. 72 - 83
Authors:
  1. Yulian Paitash
  2. Yaroslav Paranchuk
1
Lviv Polytechnic National University, Department of Electric Mechatronics and Computerized Electromechanical Systems
2
Lviv Polytechnic National University

The growing demand for electricity and the need for environmentally friendly energy sources are driving the active development of renewable technologies, with solar energy playing a leading role. Photovoltaic (PV) systems are capable of converting solar radiation into electrical energy; however, their efficiency depends on the ability to adapt to changing external conditions, such as solar irradiance and ambient temperature. One of the key challenges in working with PV panels is the nonlinearity of the current-voltage and power characteristics, which complicates the identification of the Maximum Power Point (MPP.) under dynamic changes in solar irradiance and ambient temperature. To address this issue, Maximum Power Point Tracking (MPP.T) algorithms are used, allowing the system to operate at its maximum efficiency.

This paper investigates various MPP.T aPp.roaches, including traditional algorithms such as Perturb and Observe (P&O), Incremental Conductance (INC), and the Open Circuit Voltage (OCV) method. However, these algorithms exhibit reduced efficiency under rapidly changing environmental conditions, leading to oscillations and delays in achieving the MPP..

A novel aPp.roach based on a Multilayer Neural Network (MLNN) with a backpropagation algorithm is proposed, significantly improving MPP.T efficiency due to its learning and prediction capabilities. The model uses solar irradiance and ambient temperature as input variables to predict the optimal duty cycle of a boost converter. The output signal is the pulse width modulation (PWM) duty cycle, which controls the converter’s output voltage.

Simulation results confirmed the advantages of using MLNN for MPP.T. Comparisons with traditional algorithms in terms of response speed, operational stability, reduction of oscillations, and overshoot showed significant efficiency improvements. The results demonstrate the potential for substantial reduction in the root mean square error during MPP. tracking and enhanced stability of the PV system under real-world conditions.

stand-alone system
photovoltaic installation
maximum power point tracking algorithm
neural network
energy efficiency
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