Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization-Reference-Cited by-同舟云学术

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Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

Published:2017-08 Issue: Volume:145 Page:233-246
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Yu Kunjie,Chen Xu^ORCID,Wang Xin,Wang Zhenlei

Funder

the National Natural Science Foundation of China

Shanghai Natural Science Foundation

State Key Laboratory of Synthetical Automation for Process Industries

Natural Science Foundation of Jiangsu Province

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference59 articles.

1. Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data;Awadallah;Energy Convers Manage,2016

2. Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm;Muhsen;Energy Convers Manage,2015

3. A review on integrated renewable energy system based power generation for stand-alone applications: configurations, storage options, sizing methodologies and control;Mcwilliams;Renew Sustain Energy Rev,2014

4. A robust flexible-probabilistic programming method for planning municipal energy system with considering peak-electricity price and electric vehicle;Yu;Energy Convers Manage,2017

5. Actual performance and characteristic of a grid connected photovoltaic power system in the tropics: a short term evaluation;Khatib;Energy Convers Manage,2013

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