Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems

Abstract This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing...

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Vydané v:Clean energy (Online) Ročník 6; číslo 5; s. 762 - 775
Hlavní autori: Mahesh, P Venkata, Meyyappan, S, Alla, RamaKoteswara Rao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: UK Oxford University Press 01.10.2022
Predmet:
Algorithms
Artificial neural networks
Computer simulation
Decision trees
Irradiance
Learning algorithms
Machine learning
Maximum power tracking
Neural networks
Nonlinear systems
Open circuit voltage
Photovoltaic cells
Photovoltaics
Search algorithms
Short circuit currents
Short-circuit current
Solar energy
Solar panels
Topology
Voltage
Weather
decision tree
photovoltaic system
regression machine learning
maximum power point tracking
boost converter
ISSN:2515-4230, 2515-396X
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Shrnutí:Abstract This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing with the non-linear data, the use of ML methods to operate the PV system at its maximum power point (MPP) is desirable. A strategy based on the decision-tree (DT) regression ML algorithm is proposed in this work to determine the MPP of a PV system. The data were gleaned from the technical specifications of the PV module and were used to train and test the DT. These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature. The boost converter duty cycle was determined using predicted values. The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m2 irradiance and a temperature of 25°C. The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such as β-MPPT, cuckoo search and artificial neural network results. From the proposed algorithm, efficiency has been improved by >93.93% in the steady state despite erratic irradiance and temperatures. Maximizing the output of PV modules as environmental conditions change requires a maximum power point tracking (MPPT) method. An MPPT method based on a decision-tree machine-learning algorithm is compared with other commonly used algorithms: beta-MPPT, cuckoo search, and artificial neural network. Graphical Abstract Graphical Abstract
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ISSN:2515-4230
2515-396X
DOI:10.1093/ce/zkac057