Smart charging solution for electric vehicles: Leveraging grid connected solar PV with UPQC using HBA- MORARNN approach

被引:0
|
作者
Sivaramkrishnan, M. [1 ]
Kathirvel, N. [2 ]
Kumar, C. [1 ]
Senjyu, Tomonobu [3 ]
机构
[1] Karpagam Coll Engn, Elect & Elect Engn, Coimbatore 641032, India
[2] Vel Tech Rangarajan Dr Sagunthala R&D Inst Science, Sch Comp, Informat Technol, Chennai 600062, India
[3] Univ Ryukyus, Fac Engn, Senbaru, Nishihara, Okinawa 9030213, Japan
关键词
Photovoltaic; Renewable energy source; Unified power quality conditioner; Total harmonic distortion; INVERTER;
D O I
10.1016/j.egyr.2025.02.004
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The maintenance of power quality (PQ) has become more difficult with the integration of renewable energy sources to the grid and the development of power electronics to handle non-linear loads. These changes lead to issues like harmonic distortions, voltage imbalances, and fluctuations, which can reduce the overall efficiency and reliability of power systems, necessitating improved PQ management solutions. This paper proposes a hybrid approach for solar-powered EV charging station with grid and Unified Power Quality Conditioner (UPQC). The proposed technique is the jointed operation of honey badger algorithm (HBA) and Mixed Order Relation Aware Recurrent Neural Networks (MORARNN). The main objective of the proposed technique is to reduce the source current THD, steady DC capacitance voltage (SVDC) during the load and also solar radiation changes, return of sag, swell and trouble situation. The Proposed HBA-MORARNN method is employed to optimize both shunt, series active power filter in UPQC device. They can significantly improve PQ in distribution side of the grid. By that time, the proposed hybrid technique's performance is tested using the MATLAB platform and contrasted with a number of existing techniques. The proposed system registered low Total Harmonic Distortion (THD) value of 1.5% compared with existing approaches Artificial Neural Network (ANN) of 2.6%, Puzzle Optimization Algorithm (POA) of 3.7% and Biogeography-Based Optimization (BBO) of 4.7% respectively. The proposed method improves the system's dynamic reactivity and enables improved power quality control during transient events like voltage swells and sags. The hybrid technique is adaptable for contemporary power systems since it is made to manage fluctuating load situations, such as those resulting from renewable energy sources and electric vehicle charging. It improves dynamic response, integrates energy storage, reduces THD, ensures steady DC link voltage under changeable conditions, and efficiently handles mixed load scenarios.
引用
收藏
页码:2454 / 2467
页数:14
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