Adaptive terminal synergetic-backstepping technique based machine learning regression algorithm for MPPT control of PV systems under real climatic conditions

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and obs...

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Vydané v:ISA transactions Ročník 145; s. 423 - 442
Hlavní autori: Nguimfack-Ndongmo, Jean de Dieu, Harrison, Ambe, Alombah, Njimboh Henry, Kuate-Fochie, René, Ajesam Asoh, Derek, Kenné, Godpromesse
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.02.2024
Predmet:
Integral backstepping
Linear regression
Maximum Power Point Tracking Controller
Photovoltaic systems
Real climatic conditions
Terminal synergetic
Maximum Power Point Tracking Controller
Integral backstepping
Terminal synergetic
Photovoltaic systems
Linear regression
Real climatic conditions
ISSN:0019-0578, 1879-2022, 1879-2022
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Shrnutí:This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers’ attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions. •New controller designed by combining adaptive terminal synergetic and integral backstepping techniques.•Reference voltage generation using machine learning regression algorithm.•Estimation of unmeasured variables through Neural Network.•Improved particle swarm optimization method to determine the design parameters.•Comparison of proposed controller with others under real climatic conditions.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2023.11.040