Open AccessNew unified NR power flow algorithm for initialising fixed speed wind generation system with turbine characteristics
Author(s) -
Vardhan Varma Pothuri Vishnu,
Kalyan Kumar Boddeti
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2019.0049
Subject(s) - turbine , power flow , wind power , flow (mathematics) , computer science , control theory (sociology) , algorithm , electric power system , power (physics) , engineering , mechanical engineering , electrical engineering , physics , mechanics , artificial intelligence , control (management) , quantum mechanics
Fixed speed induction machine‐based wind generators are widely used. Proper initialisation of the models representing fixed speed induction machine‐based wind generators connected to grid is of primary concern for stability analysis. The initialisation of fixed speed induction generators (FSIG) has been performed using conventional power flow techniques, but there exists a reactive power mismatch which makes initialisation imprecise. Moreover, turbine characteristics along with control mechanisms are not properly included for initialising the dynamics for stability analysis. A unified Newton–Raphson (NR) method was proposed in literature to include turbine characteristics and control methods for accurate initialisation of FSIG. However, this method creates an extra node for every induction machine causing an increase in the size of the Jacobian and time of execution of power flow algorithm. A new unified‐NR method, which does not need an extra node, for initialisation of grid‐connected FSIG is proposed in the present work. The proposed unified NR method has been validated on 14‐bus, 41‐bus, 203‐bus, and 1336‐bus test systems. The results of the proposed new unified‐NR method are compared with existing unified‐NR method. The results clearly demonstrate that the proposed method initialisation is accurate with considerably less computational time as compared to existing unified‐NR.