Development of dynamic equivalents for interconnected power systems using identification approaches

• Main Author: Kok, Boon Ching
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/16939/1/KokBoonChingPFKE2009.pdf; http://eprints.utm.my/id/eprint/16939/

Development of dynamic equivalents for interconnected power systems using identification approachesThis research presents new methods to develop power system dynamic equivalent for real time digital type power system simulator. Digital type power system simulators such as Power System Computer Aided Design/Electromagnetic Transient for Direct Current (PSCAD/EMTDC) plays an important role in cases where real time dynamic studies are required. In dynamic studies of large power system, it is vital to model the external system by their dynamic equivalents in order to retain the dynamic characteristics of the original power system as well as to reduce the problem to a solvable size. The power system structures will include studied system (internal system) and dynamic equivalents system (external system). Two methods have been proposed to identify the dynamic equivalents, i.e. using the parametric and non-parametric identification methods. Parametric identification method is based on the line flow function of the original system. The active power (P) is utilised to estimate the dynamic parameters of the equivalent generators such as inertia constant (H), damping factor (D) and the transient reactance ( ' dx ), etc. In the non-parametric identification method, Artificial Neural Networks (ANNs) is employed to solve the hard task of constructing the dynamic equivalents. Both approaches are optimised by Levenberg-Marquardt (LM) and Particle Swarm Optimisation (PSO) algorithms, respectively. The performances of the dynamic equivalents resulting from the proposed methods are compared to its original networks. The analysis and discussions on both optimisations algorithms are also presented. The proposed methods have been verified through simple test systems and realistic TNB network model. Simulations have been performed using the in-house Matlab-based Power System Dynamic Equivalents Toolbox (PSDYNET) which contains power flow analysis, time domain simulation, and identification based dynamic equivalents program.