Conference proceeding
A nonlinear observability formulation for power systems incorporating generator and load dynamics
Proceedings of the 41st IEEE Conference on Decision and Control, 2002, v 3, pp 2445-2449 vol.3
2002
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Traditionally, observability in power systems is derived from the state-estimation problem and is based on a non-linear algebraic model of the system. This measure of system observability focuses on the sensitivity of the system measurements to the change In the system states. Since the model of the system used is an algebraic model, describing the flow of power throughout the network, the non-linear dynamics of the system related to generator performance and non-linear components are ignored. The proposed observability formulation accounts for these non-linearities and provides a more comprehensive observability determination with the ability to track the observability determination of the system along the trajectory of the dynamic system states. The observability formulation of the system is sensitive to the trajectories of the system states, and the regions of the statespace where the system observability is lost are presented. A qualitative analysis for the limits of the observability formulation is presented for a 3-bus power system.
Metrics
9 Record Views
Details
- Title
- A nonlinear observability formulation for power systems incorporating generator and load dynamics
- Creators
- C.J Dafis - Drexel UniversityC.O Nwankpa - Drexel UniversityIEEE
- Publication Details
- Proceedings of the 41st IEEE Conference on Decision and Control, 2002, v 3, pp 2445-2449 vol.3
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000181352300440
- Other Identifier
- 991019170485004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Operations Research & Management Science