Node Voltage Estimation of Distribution System Using Artificial Neural Network Considering Weather Data

被引：0
作者：
Pun, Kesh [1 ]
Basnet, Saurav M. S. [2 ]
Jewell, Ward [1 ]
机构：
[1] Wichita State Univ, Wichita, KS 67260 USA
[2] Wentworth Inst Technol, Boston, MA USA
来源：
2021 IEEE KANSAS POWER AND ENERGY CONFERENCE (KPEC) | 2021年
关键词：
Load flow analysis; artificial intelligence; artificial neural network (ANN); typical meteorological year (TMY); photovoltaic (PV) power; GridLAB-D; PSEUDO-MEASUREMENTS;
D O I：
10.1109/KPEC51835.2021.9446209
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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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页数：5
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