Performance Study of Neural Network and ANFIS Based MPPT Methods For Grid Connected PV System

被引：2
|
作者：
Abu Sarhan, Mohammad
Ding, Min
Chen, Xin
Wu, Min [1 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China
来源：
PROCEEDINGS OF 2017 VI INTERNATIONAL CONFERENCE ON NETWORK, COMMUNICATION AND COMPUTING (ICNCC 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
Maximum power point tracking (MPPT); PV systems; buck converters; neural network; adaptive neuro-fuzzy system (ANFIS); POINT TRACKING TECHNIQUES;
D O I：
10.1145/3171592.3171623
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The maximum power point tracking (MPPT) methods are applied in PV solar systems to accomplish the desired maximum power from the PV system. Hence, it is important to design the best technique which can reach the maximum power point (MPP) effectively. In this paper, a grid connected PV system is controlled by artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS) based MPPT methods. Both of proposed MPPT methods are analyzed related to their performance efficiency and response under the variation of solar irradiation and cell temperature. The obtained results of both methods are compared to experimental results which show that ANFIS has more response and efficiency than ANN in maximum power point tracking. The investigation has been done by using MATLAB/Simulink Environment.
引用
收藏
页码：227 / 234
页数：8
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