Open AccessThree‐phase feeder parameter estimation using radial basis function neural networks and multi‐run optimisation method with bad data preparation
Author(s) -
Yang NienChe,
Huang Rui,
Guo MouFa
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12310
Subject(s) - phasor , phasor measurement unit , phase angle (astronomy) , artificial neural network , phase (matter) , mean absolute percentage error , three phase , voltage , computer science , approximation error , power (physics) , control theory (sociology) , radial basis function , nonlinear system , function (biology) , algorithm , electric power system , engineering , artificial intelligence , chemistry , physics , control (management) , organic chemistry , quantum mechanics , astronomy , evolutionary biology , electrical engineering , biology
The difference between the actual feeder parameters and feeder parameter data stored in a database or offered by manufacturers is significant owing to the ambient environment, temperature, and skin effect. Here, a parameter estimation method is proposed for unbalanced three‐phase distribution feeders based on the bus voltages and branch power flows measured from two terminals of the feeder. In the proposed method, a high‐precision phasor measurement unit is not required to estimate the magnitude and phase angle of the phasor quantity using a common time source for synchronisation. A radial basis function neural network with multi‐run optimisation (RBFNN‐MRO) is proposed to map the complex nonlinear relations between the distribution feeder parameters and electrical quantities. The feasibility and performance of the proposed RBFNN‐MRO method were verified using the four IEEE test systems. The comparison between the proposed RBFNN‐MRO method and the multi‐run method based on the quasi‐Newton method is implemented via the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN‐MRO method has excellent potential for improving the accuracy of feeder parameter estimation, even for bad data preparation.