Extensive studies have been undertaken on the transient stability of large interconnected
power systems with flexible ac transmission systems (FACTS) devices installed.
Varieties of control methodologies have been proposed to stabilize the postfault system
which would otherwise eventually lose stability without a proper control. Generally speaking,
regular transient stability is well understood, but the mechanism of load-driven voltage
instability or voltage collapse has not been well understood. The interaction of generator
dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging.
There is currently increasing interest in the research of neural networks as identifiers
and controllers for dealing with dynamic time-varying nonlinear systems. This study
focuses on the development of novel artificial neural network architectures for identification
and control with application to dynamic electric power systems so that the stability of the
interconnected power systems, following large disturbances, and/or with the inclusion of
uncertain loads, can be largely enhanced, and stable operations are guaranteed.
The latitudinal neural network architecture is proposed for the purpose of system
identification. It may be used for identification of nonlinear static/dynamic loads, which
can be further used for static/dynamic voltage stability analysis. The properties associated
with this architecture are investigated.
A neural network methodology is proposed for dealing with load modeling and
voltage stability analysis. Based on the neural network models of loads, voltage stability
analysis evolves, and modal analysis is performed. Simulation results are also provided.
The transient stability problem is studied with consideration of load effects. The
hierarchical neural control scheme is developed. Trajectory-following policy is used so that
the hierarchical neural controller performs as almost well for non-nominal cases as they do
for the nominal cases. The adaptive hierarchical neural control scheme is also proposed
to deal with the time-varying nature of loads. Further, adaptive neural control, which is
based on the on-line updating of the weights and biases of the neural networks, is studied.
Simulations provided on the faulted power systems with unknown loads suggest that the
proposed adaptive hierarchical neural control schemes should be useful for practical power
applications. / Graduation date: 1999
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/33519 |
| Date | 30 September 1998 |
| Creators | Chen, Dingguo |
| Contributors | Mohler, Ronald R. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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