| Title |
A Neural Network to Estimate the Primary Voltage from Secondary Measurements in the Distribution Transformer |
| Authors |
안재국(Jae-Guk An) ; 송진욱(Jin-Wook Song) ; 임성일(Seongil Lim) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.11.1617 |
| Keywords |
Distribution Automation; Machine Learning; Neural Network; Voltage Compensation; Renewable Energy |
| Abstract |
In order to maintain voltage of distribution line within a regulated range, grid tie inverter of DG controls reactive power. Reactive power control amount is determined by the medium voltage at the primary-side of the distribution transformer, while DG measurements are done from secondary-side low voltage where it is installed. Due to the characteristics of iron core, voltage drops and unknown tap position, middle voltage cannot be calculated accurately from low voltage measurement with applying winding ratio. This paper proposes a new medium voltage estimation method using ANN(Artificial Neural Network) by learning the relationship between primary and secondary side voltages. Both side voltages of the transformer are used for label and input of the ANN respectively. Training inputs are measured by DGs, and labels are calculated by state estimator of DMS(Distribution Management System). Real-time power system simulator and DMS are used to verify the feasibility of the proposed method. |