A Study on the Pv Plant Fault State Recognition by using Weather Corrected Linear Regression and Estimated Error Matrix

Autor:	Jun-Hyun Shin, Jin-O Kim
Rok vydání:	2019
Předmět:	Variable (computer science) Index (economics) Linear regression Statistics Temperature correction Fault (power engineering) Renewable energy penetration Power (physics) Mathematics Pv power
Zdroj:	Journal of the Korean Institute of Illuminating and Electrical Installation Engineers. 33:48-56
ISSN:	2287-5034 1229-4691
Popis:	PV renewable energy penetration rate has been sharply increased based on the 3020 government policy. However, most of the pv plant output power is decreased due to the lack of the management tool and skills. IEC 61724-1 suggests PR index to evaluate the pv plant performance. But, PR index have a problem of fault recognition rate because of low value when low irradiation day. This paper present a linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index to classify the fault state of pv power plant.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2cdf6ff28ecf95d17356d559e9e78bb6 https://doi.org/10.5207/jieie.2019.33.7.048 Zobrazit plný text záznamu