Abstract:
The power system can be viewed as a dynamical changing system with many
components being modified depending upon the utility requirements. Under such
operational scenario, the basic objectives of system’s control have been to ensure stable
behavior even during changing dynamics. The present work is an attempt to propose a
power control concept as outcome of research work to stabilize system even under
varied dynamical changes. The work presented in thesis reports an intelligent multi-area
power control with dynamic knowledge domain inference concept. It is known that ongoing
operational shift leads to unacceptable states variation, which may result in power
oscillations, and if the controllers are not suitably designed, the system may be
interactive and oscillations can aggravate. To address these issues, Knowledge Domain
States Mapping Concept for intelligent power flow control has been presented in this
research work. The work includes a new concept of updating control parameters, which
is linked with operational shift, initially in offline mode in building respective
knowledge domain that fits into the framework of changing situations, to ensure states
regulation. The proposed concept also provides flexibility to update the knowledge
domain over and above offline data with newer data set combining the nearest data
clusters to derive an averaged data (controller parameter) within predefined boundary to
change the controller functioning. The knowledge retrieval, as operational shift
proceeds, has been mapped utilizing dynamical inference concept. The control so
derived, effectively ensures best damping well within time for large network reliability
and security. The structure of the controller so obtained is termed as the intelligent
power flow controller. Three well-known optimization techniques (Particle Swarm
Optimization (PSO), Gravitational Search Algorithm (GSA) and Firefly Algorithm
(FA)) have been used to develop knowledge domain and find the optimal parameters of
the controllers associated with operational shift. This approach demonstrates the
capability of controller performance based on knowledge domain driven tuning of
controllers employing dynamic knowledge domain inference concept. It is to emphasize
that this approach is general enough to accommodate any system complexity on
modular basis thus extendable to large power system as such. Power System Stabilizer
(PSS) is used as local supplementary excitation control to enhance the damping of the
electromechanical mode of oscillations in power system by individual generator
regulated participation. The FACTS controller, due to fast power flow control, not only
enhances the steady-state stability but also improves transient stability along with
damping of low-frequency oscillations and thus ensures better network availability.
New controllers (Integrated Multi-Stage LQR-POD controller and Modified-Multi
Stage LQR (M-MSLQR) Controller) have been reported in this work to improve the
system stability under various operating conditions.
