학술논문

Power system dynamic stability enhancement via coordinated design of PSSs and SVC-based controllers using hierarchical real coded NSGA-II
Document Type
Conference
Source
2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE. :1-8 Jul, 2008
Subject
Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Computing and Processing
Power system stability
Power systems
Damping
Oscillators
Power system dynamics
Eigenvalues and eigenfunctions
Optimization
Hierarchical Non dominated Sorting Genetic Algorithms - II (HNSGA-II)
FACTS
SVC
PSS
Damping Controller and Transient Stability
Language
ISSN
1932-5517
Abstract
Optimal Coordination and tuning of PSSs and SVC-based controllers to enhance the dynamic power system stability using Hierarchical Non-dominated Sorting Genetic Algorithms-II (HNSGA-II) is presented in this paper. The coordinated design problem of robust excitation and SVC-based controllers over a wide range of system configurations is formulated as a Multi Objective Optimization problem with eigenvalue-based objective functions comprising a Damping ratio, the number of PSSs and SVC-based Controllers. The Multi Objective Optimization will be solved by a HNSGA-II which is a metaheuristic based technique. The Damping Controllers are tuned to simultaneously shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane, and to self identify the appropriate choice of PSS and SVC-based controlled locations. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping ratio of the lightly damped electromechanical modes, the number of damping controller. The efficacy of this technique in damping local and interarea modes of oscillations in multimachine power systems is confirmed through eigenvalues analysis over many scenarios.