Artificial neural network-based maximum power point tracking controller for real-time hybrid renewable energy system

被引：0
作者：
Mohammad Junaid Khan
Lini Mathew
机构：
[1] Department of Electrical and Electronics Engineering,Department of Electrical Engineering
[2] Mewat Engineering College,undefined
[3] National Institute of Technical Teachers Training and Research,undefined
来源：
Soft Computing | 2021年 / 25卷
关键词：
ANFIS; ANN; FC; FL controller; Hardware in the loop (HIL); HRES; MPPT algorithms; OPAL-RT simulator; P&O; Power converters; PV and WT;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Development of various maximum power point tracking (MPPT) control techniques for proposed systems such as solar photo-voltaic (PV), wind turbine (WT), fuel cell (FC) and hybrid renewable energy system (HRES). HRES is the combination of PV, WT and FC which is connected parallelly by DC link. It is implemented in real-time using OPAL-RT system. In this research article, the MPPT algorithms viz. Perturb and Observe (P&O), Fuzzy Logic (FL), Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been analyzed and compared. Results have been carried out to record tracking performance of MPPT controllers by introducing changes in the radiation, wind speed hydrogen fuel rate. It has been observed that the proposed HRES using ANFIS-based MPPT controller provides better response as compared to other specified MPPT controllers.
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页码：6557 / 6575
页数：18
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