A Robust Integral Quasi-Sliding Mode Control for MPPT based Photovoltaic System with a DC–DC Boost Converter DOI

J. Aiboudi,

I. El Idrissi,

A. El Alami

и другие.

Опубликована: Июнь 28, 2024

Язык: Английский

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions DOI
Debabrata Mazumdar, Pabitra Kumar Biswas⃰, Chiranjit Sain

и другие.

Renewable energy focus, Год журнала: 2024, Номер 49, С. 100577 - 100577

Опубликована: Май 5, 2024

Язык: Английский

Процитировано

13

An investigation of Photovoltaic Power Forecasting in Buildings Considering Shadow Effects: Modeling Approach and SHAP Analysis DOI

Jianjin Fu,

Yuying Sun,

Yunhe Li

и другие.

Renewable Energy, Год журнала: 2025, Номер unknown, С. 122821 - 122821

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

2

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions DOI
Chunliang Mai, Lixin Zhang,

Xue Hu

и другие.

Applied Soft Computing, Год журнала: 2024, Номер 162, С. 111822 - 111822

Опубликована: Июнь 9, 2024

Язык: Английский

Процитировано

3

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation DOI Creative Commons
Qays Adnan Ali, Mohamed Mohamed Elsakka, Nikolay Korovkin

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102967 - 102967

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

3

Enhancing MPPT efficiency in PV systems under partial shading: A hybrid POA&PO approach for rapid and accurate energy harvesting DOI Creative Commons
Hao Wang,

Lin Li,

Haoshen Ye

и другие.

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 162, С. 110260 - 110260

Опубликована: Сен. 25, 2024

Язык: Английский

Процитировано

3

Empowering MPPT Efficiency in Partial Shading with Machine Learning Driven Metaheuristic Strategies: Recent Developments DOI

Faiza Belhachat,

Chérif Larbès, Rachid Bennia

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 594 - 612

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Is the end of AI in photovoltaic power? Evidence from China DOI
Haoran Zhang,

Xiaohong Yu,

Zixuan Gao

и другие.

Energy Economics, Год журнала: 2025, Номер unknown, С. 108423 - 108423

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Global peak operation of solar photovoltaic and wind energy systems: Current trends and innovations in enhanced optimization control techniques DOI

Saranya Pulenthirarasa,

Priya Ranjan Satpathy, Vigna K. Ramachandaramurthy

и другие.

IFAC Journal of Systems and Control, Год журнала: 2025, Номер unknown, С. 100304 - 100304

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Sensorless estimation of irradiance and temperature for renewable energy applications: An experimental examination DOI
Fahad Alsokhiry

Computers & Electrical Engineering, Год журнала: 2025, Номер 124, С. 110329 - 110329

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0

Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria DOI
Oubah Isman Okieh, Serhat Şeker, Tahir Çetin Akıncı

и другие.

Electric Power Components and Systems, Год журнала: 2024, Номер unknown, С. 1 - 12

Опубликована: Март 25, 2024

Due to the extremely poor efficiency of solar energy, researchers created various maximum power point tracking (MPPT) techniques with aim enhancing effectiveness photovoltaic (PV) systems. Because their propensity address complex problems and non-linear characteristics, Artificial Neural Network (ANN) algorithms are most frequently used among these MPPT techniques. Nevertheless, performance ANN-based MPP is contingent upon factors, including choice activation function, quantity hidden neurons, training algorithm employed. Shannon's Information Criteria (SIC) determine optimal number neurons within a single layer for neuro-controller application. In this regard, two-layer Feed-Forward (FFNN) was trained using MATLAB/Simulink software, incorporating varying numbers neurons. The results indicate that FFNN five has highest performance, as demonstrated by lowest Mean Squared Error (MSE) 4.03 × 10-9, which statistically significant. successful incorporation Gaussian noise in simulation an 85 kW PV system demonstrates both theoretically robust practically viable reliable systems real-world scenarios.

Язык: Английский

Процитировано

2