Node voltage estimation of distribution system using artificial neural network considering weather data
Pun, Kesh Bahadur Basnet, Saurav Man Singh Jewell, Ward T.
Pun, Kesh Bahadur
Basnet, Saurav Man Singh
Jewell, Ward T.
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Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2021-04-19
Type
Conference paper
Genre
Keywords
Wind,Voltage measurement,Estimation,Artificial neural network,Wind power generation,Data models,Loss measurement
Subjects (LCSH)
Citation
Pun, K., Basnet, S. M. S., & Jewell, W. (2021). Node voltage estimation of distribution system using artificial neural network considering weather data. Paper presented at the 2021 IEEE Kansas Power and Energy Conference, KPEC 2021, doi:10.1109/KPEC51835.2021.9446209
Abstract
Load flow analysis using traditional methods for power flow is becoming complex (reverse power flow and voltage volatility) due to the configuration complexity brought about by renewable energy resource (RER) integration. The variable and intermittent nature of RER integration also contributes to the power flow complexity. Power system operators should be aware of the state of the operation. An alternative to traditional power flow methods could be an artificial intelligence technique. Therefore, in this study, the node voltage estimation of a distribution system using an artificial neural network (ANN) has been proposed. Since a significant portion of residential load and RER generation are dependent on weather conditions, load flow analysis including weather data in GridLAB-D has been carried out. Typical meteorological year (TMY) information has been used as the weather data. Results show that node voltage estimation using the ANN technique is robust on different photovoltaic (PV) and wind power penetration levels as well as the significant loss of load measurement data and/or PV and wind generation data.
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Publisher
IEEE
Journal
Book Title
Series
2021 IEEE Kansas Power and Energy Conference (KPEC);