STATIC STATE ESTIMATION AND LOAD FORECASTING VIA ADVANCED COMPUTATIONAL TECHNIQUES
Date
2019-07-17T10:41:28Z
Authors
Mosbah, Hossam
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Abstract
The system operator faced high level of uncertainties due to the evolving complexity
of the electric power systems. Therefore, efficient solutions for power system prediction,
monitoring and state estimation can be found in new methods. These methods
improve the secure operations of the network.
This thesis covers three different state estimations incorporating static, tracking, and
dynamic state estimation in order to estimate all possible operating conditions of
power systems.
This research also introduces new meta heuristic methods such as stochastic and fractal
search technique. The SFS technique is implemented in real time nonlinear power
system applications under various scenarios. New methodology of multilayer neural
networks exhibited in composite typologies are proposed in this thesis to improve
the estimation performance. Optimized Neural Network by Stochastic Fractal Search
technique is used and applied to both tracking and dynamic state estimation. The
proposed methods are validated utilizing diverse benchmark optimization methods.
The combination of conventional and synchronized measurement is also studied in
this thesis. This is used to increase the reliability of electric power systems in realtime.
Additionally, the research is extended to evaluate the benefits of multiarea state estimators
and how it uses to reduce the computational time.
Finally, all formulations proposed in this work were validated in different IEEE test
systems.
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Keywords
State Estimation, Load forecasting, neural network, meta heuristic techniques, dynamic and tracking state estimation