Noisy Reading Correction in Low Power MPPT using Kalman Filter

被引：0

作者：

Haidar, Mohammad ^{[1
,2
]}

Chible, Hussein ^{[2
]}

Caviglia, Daniele D. ^{[1
]}

机构：

[1] Univ Genoa, DITEN, Cosm Lab, Genoa, Italy

[2] Lebanese Univ, EDST, MECRL Lab, Beirut, Lebanon

来源：

2019 26TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS) | 2019年

关键词：

Kalman filter; MPPT; measurements; power; noise; stability;

D O I：

10.1109/icecs46596.2019.8964635

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Kalman filter is designed to predict the correct values for noisy measurements, using a probabilistic model. However, the chosen system model has a huge impact on the outcome of this algorithm, which makes it very important for the model to be as representative of the system as possible. A new approach to handle the noisy measurements in low power Maximum Power Point Tracking (MPPT) algorithms is presented in this paper. The proposed filtering proved beneficial in terms of algorithm stability.

引用

页码：133 / 134

页数：2

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