TY - JOUR
T1 - A solar tracking system based on local solar time integrated to photovoltaic systems
AU - Ferreira, Luiz A.S.
AU - Loschi, Hermes J.
AU - Rodriguez, Abel A.D.
AU - Iano, Yuzo
AU - Do Nascimento, Douglas A.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - The performance of photovoltaic (PV) systems is highly influenced by the tilt angle of PV modules and the incidence of global solar irradiance, which may change the solar to electrical conversion efficiency. Some authors have addressed these uncertainties arising from PV solar generation by using mechanisms and methods in which solar tracking systems are integrated to PV systems. Since the advent of the internet of things (IoT), this solar tracking strategy has yet to meet the requirements of scalable distributed power systems that can seamlessly support the PV solar generation, mainly for remote monitoring and control. In this context, this paper aims at developing a prospective study devoted to examine fundamental concepts to implement solar tracking algorithms based on local solar time by using embedded technology from the IoT platform. Preliminary results evidenced an improvement of up to 38% in power generation performance for algorithmdriven PV modules compared to fixed PV modules.
AB - The performance of photovoltaic (PV) systems is highly influenced by the tilt angle of PV modules and the incidence of global solar irradiance, which may change the solar to electrical conversion efficiency. Some authors have addressed these uncertainties arising from PV solar generation by using mechanisms and methods in which solar tracking systems are integrated to PV systems. Since the advent of the internet of things (IoT), this solar tracking strategy has yet to meet the requirements of scalable distributed power systems that can seamlessly support the PV solar generation, mainly for remote monitoring and control. In this context, this paper aims at developing a prospective study devoted to examine fundamental concepts to implement solar tracking algorithms based on local solar time by using embedded technology from the IoT platform. Preliminary results evidenced an improvement of up to 38% in power generation performance for algorithmdriven PV modules compared to fixed PV modules.
UR - http://www.scopus.com/inward/record.url?scp=85042711427&partnerID=8YFLogxK
U2 - 10.1115/1.4039094
DO - 10.1115/1.4039094
M3 - Article
AN - SCOPUS:85042711427
VL - 140
JO - Journal of Solar Energy Engineering, Transactions of the ASME
JF - Journal of Solar Energy Engineering, Transactions of the ASME
SN - 0199-6231
IS - 2
M1 - 021010
ER -