Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system

被引:0
作者
Boutaghane, Karima [1 ]
Benbouhenni, Habib [2 ]
Bennecib, Nedjoua [1 ]
Colak, Ilhami [3 ]
Elbarbary, Z. M. S. [4 ,5 ]
Al-Gahtani, Saad F. [4 ,5 ]
机构
[1] Bros Mentouri Univ, Dept Elect Engn, Lab Elect Engn Constantine LGEC, Constantine 25000, Algeria
[2] Ecole Natl Polytech Oran ENP Oran, Lab Automatique& Anal Syst LAAS, BP 1523 Mnaouer, Oran, Algeria
[3] Istinye Univ, Dept Elect & Elect Engn, Istanbul, Turkiye
[4] King Khalid Univ, Coll Engn, Dept Elect Engn, KSA, POB 394, Abha 61421, Saudi Arabia
[5] King Khalid Univ, Ctr Engn & Technol Innovat, Abha 61421, Saudi Arabia
来源
SCIENTIFIC REPORTS | 2025年 / 15卷 / 01期
关键词
Hybrid energy systems; Fractional-order proportional-integral regulator; Adaptive neuro-fuzzy inference system method; Photovoltaic system; Integral sliding mode control; PV SYSTEM; DESIGN; PERFORMANCE; INTELLIGENT; MANAGEMENT; QUALITY; MODE;
D O I
10.1038/s41598-025-88804-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES. In this study, the battery-powered HES is presented, where this designed system consists of a wind system and a photovoltaic (PV) system. The strategy of maximum power point (MPP) tracking (MPPT) based on the adaptive neuro-fuzzy inference system (ANFIS) method is used to command the PV system and the wind system, and the MPPT based on the neural method is used. These proposed strategies do not need the mathematical model of the studied system and augment the robustness and stability, where the system performance is great. Also, the fractional-order proportional-integral regulator and the integral sliding mode control approach are combined to control the battery-based storage system, and the particle swarm optimization approach was used to estimate the gain values of the resulting controller. The HES was realized using MATLAB, where the competence is tested under different work scenarios. The results showed excellent efficacy of the designed control and were compared with conventional control. The simulation results showed that using the neural MPPT strategy in the case of the wind speed being 12 m/s, the values of rise time, response time, MPP, and steady-state error (SSE) are improved by rates estimated at 99.32%, 60%, 1.5%, and 60%, respectively compared to the perturbations and observations-based MPPT approach. Compared to the traditional strategy, the ANFIS-MPPT strategy improves the values of MPP, response time, SSE, and rise time in the case of irradiation, which takes the value of 1000 W/m2, by percentages estimated at 18%, 60%, 94.70%, and 69.23%, respectively. Also, the PSO-FOPI-ISMC strategy improves the harmonic distortion of the current value in the second test by 55.20% and 72.90% for mode 1 and mode 2, respectively, compared to the traditional approach. These results make the designed approach of great importance in the future in other industrial applications.
引用
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页数:32
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