TY - JOUR
T1 - State solutions for distribution systems and switching event using a neural network
T2 - State Solution Using Neural Network
AU - Yaniv, Arbel
AU - Lin, Avi
AU - Raz, David
AU - Beck, Yuval
N1 - Publisher Copyright:
© 2021 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
PY - 2022/1
Y1 - 2022/1
N2 - Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton-Raphson and the fixed-point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.
AB - Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton-Raphson and the fixed-point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.
KW - artificial intelligence
KW - distribution networks
KW - smart power grids
KW - switching systems (control)
UR - http://www.scopus.com/inward/record.url?scp=85117055808&partnerID=8YFLogxK
U2 - 10.1049/gtd2.12278
DO - 10.1049/gtd2.12278
M3 - מאמר
AN - SCOPUS:85117055808
VL - 16
SP - 71
EP - 83
JO - IET Generation, Transmission and Distribution
JF - IET Generation, Transmission and Distribution
SN - 1751-8687
IS - 1
ER -