Power system stability improvement using teaching learning based optimization technique

Abstract

The extension and development in our existing power system have been continued from the past. It is still in a continuous state of development to satisfy the day to day increased demand for electrical load. This so-called process of development has turned our same power system into a very complex network and hence its operation is complex too. This complexity is very closely related to the operation of this vary system and hence it has to be stable in all the conditions for proper functioning. Due to this the limits of its stable region requires extension too to be in proper operating status without disturbing its stability. Here the term stability is of utmost importance to our concerned system as many events can deviate it or even disturb it from its desired and stable region. One such phenomenon is of the generation of low-frequency electromechanical oscillations (LFO) in a synchronous generator s rotor which if not cleared in time may cause the respective machine to go out of synch and may also result in isolation of the affected part. This is more pronounced if the concerned electrical network is not so strong. Taking this as the main focus the following work of thesis has been developed and constructed. It primarily examines and assesses stability phenomena. It also discusses the way with which this stability feat ture can be improvised. Specifically, one of the most popular families of devices i.e. Flexible AC Transmission System (FACTS) has been made use of. From this family, Static Series Compensator (SSSC) is selected to stabilize the system after being disturbed while encountering LFO. However, power system stabilizers (PSS) are also used. The controllers PSS and SSSC have been considered as an optimization problem for constructing it. As the world has grown so is the way with which it solves various problems in it. Among this is the meta-heuristic optimization techniques (OT) which form the very past to the very present has developed, evolved, and grouped into so many different types of techniques.