Data mining of remote monitored stand-alone Solar PV Systems for State of Health Estimation

被引：0

作者：

Davison, Peter ^{[1
]}

Wade, Neal ^{[1
]}

Greenwood, David ^{[1
]}

机构：

[1] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne, Tyne & Wear, England

来源：

2016 IEEE PES POWERAFRICA CONFERENCE | 2016年

基金：

英国工程与自然科学研究理事会;

关键词：

photovoltaic systems; battery management systems; rural areas; power system measurements; power system management; power system analysis computing; LEAD-ACID-BATTERIES; OF-CHARGE;

D O I：

暂无

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：

In this paper, we use data mining techniques and formulate suitable assessment metrics to derive estimates of the State of Health (SOH) of stand-alone solar home systems. Data is provided from a company with significant numbers of such systems in Africa. The systems in question contain a PV panel, lead-acid battery and a series of DC loads. Data mining allows us to not only estimate the SOH of the battery, but also infer the health of other system components.

引用

页码：194 / 198

页数：5

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