Mod Tanh-Activated Physical Neural Network MPPT Control Algorithm for Varying Irradiance Conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260632" target="_blank" >RIV/61989100:27240/25:10260632 - isvavai.cz</a>

Výsledek na webu

<a href="https://scijournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70062?getft_integrator=scopus&src=getftr&utm_source=scopus" target="_blank" >https://scijournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70062?getft_integrator=scopus&src=getftr&utm_source=scopus</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ese3.70062" target="_blank" >10.1002/ese3.70062</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mod Tanh-Activated Physical Neural Network MPPT Control Algorithm for Varying Irradiance Conditions

Popis výsledku v původním jazyce

The increasing adoption of solar photovoltaic systems necessitates efficient maximum power point tracking (MPPT) algorithms to ensure optimal performance. This study proposes a Mod tanh-activated physical neural network (MAPNN)-based MPPT control algorithm, which addresses inefficiencies in existing models caused by spectral mismatch and improper converter control. The proposed method incorporates beta-distributed point estimation technique for mismatch factor correction and a Buck-Boost converter with a feedback control using the Chinese Remainder Theorem – Puzzle Optimization Algorithm-tuned PID controller. Simulations demonstrate an efficiency improvement of 98.42%, with a 4.54 dB reduction in total harmonic distortion and faster convergence compared to traditional methods such as ANN and LSTM. This system significantly enhances MPPT performance under dynamic irradiance conditions.

Název v anglickém jazyce

Mod Tanh-Activated Physical Neural Network MPPT Control Algorithm for Varying Irradiance Conditions

Popis výsledku anglicky

The increasing adoption of solar photovoltaic systems necessitates efficient maximum power point tracking (MPPT) algorithms to ensure optimal performance. This study proposes a Mod tanh-activated physical neural network (MAPNN)-based MPPT control algorithm, which addresses inefficiencies in existing models caused by spectral mismatch and improper converter control. The proposed method incorporates beta-distributed point estimation technique for mismatch factor correction and a Buck-Boost converter with a feedback control using the Chinese Remainder Theorem – Puzzle Optimization Algorithm-tuned PID controller. Simulations demonstrate an efficiency improvement of 98.42%, with a 4.54 dB reduction in total harmonic distortion and faster convergence compared to traditional methods such as ANN and LSTM. This system significantly enhances MPPT performance under dynamic irradiance conditions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Science &amp; Engineering

ISSN

2050-0505

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

"2606 "- 2619

Kód UT WoS článku

001457606800001

EID výsledku v databázi Scopus

2-s2.0-105001834095