Khadanga, Rajendra Kumar (2016) Performance Analysis of Flexible A.C. Transmission System Devices for Stability Improvement of Power System. PhD thesis.
When large power systems are interconnected by relatively weak tie line, low-frequency oscillations are observed. Recent developments in power electronics have led to the development of the Flexible AC Transmission Systems (FACTS) devices in power systems. FACTS devices are capable of controlling the network condition in a very fast manner and this feature of FACTS can be exploited to improve the stability of a power system. To damp electromechanical oscillations in the power system, the supplementary controller can be applied with FACTS devices to increase the system damping. The supplementary controller is called damping controller. The damping controllers are designed to produce an electrical torque in phase with the speed deviation. The objective of this thesis is to develop some novel control techniques for the FACTS based damping controller design to enhance power system stability.
Proper selection of optimization techniques plays an important role in for the stability enhancement of power system. In the present thesis Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational search algorithm (GSA) along with their hybrid form have been applied and compared for a FACTS based damping controller design. Important conclusions have been drawn on the suitability of optimization technique.
The areas of research achieved in this thesis have been divided into two parts:
The aim of the first part is to develop the linearized model (Philip-Hefron model) of a single machine infinite bus power system installed with FACTS devices, such as Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC).
Different Damping controller structures have been used and compared to mitigate the system damping by adding a component of additional damping torque proportional to speed change through the excitation system. The various soft-computing techniques have been applied in order to find the controller parameters.
The recently developed Gravitational Search Algorithm (GSA) based SSSC damping controller, and a new hybrid Genetic Algorithm and Gravitational Search Algorithm (hGA-GSA) based UPFC damping controller seems to the most effective damping controller to mitigate the system oscillation.
The aim of second part is to develop the Simulink based model (to over-come the problem associated with the linearized model) for an SMIB as well as the multi-machine power system.
Coordinated design of PSS with various FACTS devices based damping controllers are carried out considering appropriate time delays due to sensor time constant and signal transmission delays in the design process. A hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is employed to optimally and coordinately tune the PSS and SSSC based controller parameters and has emerged as the most superior method of coordinated controller design considered for both single machine infinite bus power system as well as a multi-machine power system.
Finally, the damping capabilities of SSSC based damping controllers are thoroughly investigated by considering a new derived modified signal known as Modified Local Input Signal which comprises both the local signal (speed deviation) and remote signal (line active power). Appropriate time delays due to sensor time constant and signal transmission delays are considered in the design process. The hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is used to tune the damping controller parameters. It is observed that the new modified local input signal based SSSC controller provides the best system performance compared to other alternatives considered for a single machine infinite bus power system and multi-machine power system.
|Item Type:||Thesis (PhD)|
|Uncontrolled Keywords:||Flexible AC Transmission System (FACTS); Static Synchronous Series compensator (SSSC); Unified Power Flow Controller (UPFC); Genetic Algorithm (GA); Particle Swarm Optimization (PSO); Gravitational Search Algorithm (GSA); Hybrid Algorithms.|
|Subjects:||Engineering and Technology > Electrical Engineering > Power Transformers|
|Divisions:||Engineering and Technology > Department of Electrical Engineering|
|Deposited By:||Mr. Sanat Kumar Behera|
|Deposited On:||03 Nov 2016 21:32|
|Last Modified:||03 Nov 2016 21:32|
|Supervisor(s):||Satapathy, J K|
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