Artificial neural network model of photovoltaic generator for power flow analysis in PSS®SINCAL

Travis Xu; Bala Venkatesh; Chandrabhanu Opathella; Birendra N. Singh

Artificial neural network model of photovoltaic generator for power flow analysis in PSS^®SINCAL

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Author(s): Travis Xu ¹ ; Bala Venkatesh ¹ ; Chandrabhanu Opathella ¹ ; Birendra N. Singh ²
- Affiliations: 1: Centre for Urban Energy, Ryerson University, Toronto, Canada;
  2: Hydro One Networks Inc., Toronto, Canada
Source: Volume 8, Issue 7, July 2014, p. 1346 – 1353
DOI: 10.1049/iet-gtd.2013.0562 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 11/08/2013, Accepted 17/12/2013, Revised 06/12/2013, Published 25/02/2014

Output of a three-phase photovoltaic generator (PVG) is a function of sunlight irradiance, temperature, and three-phase terminal voltage phasors. Three-phase PVGs are largely connected to rural distribution systems feeders that are predominantly unbalanced. Models of PVGs that are only a function of sunlight irradiance and temperature disregarding unbalanced three-phase terminal voltages phasors are simple to use with three-phase power flow analysis but yield inaccurate solutions. Detailed three-phase PVG models are complex and non-linear, hence unsuitable for power flow analysis applications. This study proposes an artificial neural network (ANN) model to represent a PVG comprising photovoltaic panels, a boost chopper and a three-phase inverter. Main advantages of the ANN model are that it can be readily used to model a PVG of any size and type, mathematical simplicity, high accuracy with unbalanced systems and computational speed. The model was tested with the unbalanced distribution system feeder from a Canadian utility. The results show that the ANN model of a PVG is computationally fast and more accurate than simple model that ignores unbalanced three-phase terminal voltage phasors. In addition, simplicity of the proposed ANN model of PVG allows easy integration into commercial software packages such as PSS^®SINCAL as reported in this study.

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Artificial neural network model of photovoltaic generator for power flow analysis in PSS^®SINCAL

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