09-07-2012, 12:52 PM
POWER SYSTEM STABILITY IMPROVEMENT USING FACTS WITH EXPERT SYSTEMS
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INTRODUCTION
Since the development of interconnection of large electric power systems, there have been
spontaneous system oscillations at very low frequencies in order of 0.2–3.0 Hz. Once started, they
would continue for a long period of time. In some cases, they continue to grow causing system
separation due to the lack of damping of the mechanical modes [1; 2]. In the past three decades, power
system stabilizers (PSSs) have been extensively used to increase the system damping for low
frequency oscillations. The power utilities worldwide are currently implementing PSSs as effective
excitation controllers to enhance the system stability [1–12]. However, there have been problems
experienced with PSSs over the years of operation. Some of these were due to the limited capability
of PSS, in damping only local and not inter area modes of oscillations. In addition, PSSs can cause
great variations in the voltage profile under severe disturbances and they may even result in leading
power factor operation and losing system stability [13]. This situation has necessitated a review of the
traditional power system concepts and practices to achieve a larger stability margin, greater operating
flexibility, and better utilization of existing power systems.
FACTS DEVICES
Overview:
In the late 1980s, the Electric Power Research Institute (EPRI) formulated the vision of the Flexible
AC Transmission Systems (FACTS) in which various power-electronics based controllers regulate
power flow and transmission voltage and mitigate dynamic disturbances. Generally, the main
objectives of FACTS are to increase the useable transmission capacity of lines and control power flow
over designated transmission routes. Hingorani and Gyugyi [5] and Hingorani [6; 8] proposed the
concept of FACTS. Edris et al. [18] proposed terms and definitions for different FACTS controllers.
There are two generations for realization of power electronics-based FACTS controllers: the first
generation employs conventional thyristor-switched capacitors and reactors, and quadrature tapchanging
transformers, the second generation employs gate turn-off (GTO) thyristor-switched
converters as voltage source converters (VSCs). The first generation has resulted in the Static Var
Compensator (SVC), the Thyristor- Controlled Series Capacitor (TCSC), and the Thyristor-Controlled
Phase Shifter (TCPS) [10; 11].
CLASSIFICATION OF FACTS CONTROLLERS
Coordination Techniques
A. by Placement of FACTS Controllers in Power Systems References [3]-[5], [14], classify
three broad categories such as a sensitivity based methods, optimization based method, and artificial
intelligence based techniques for placement of FACTS controllers from different operating conditions
viewpoint in multi-machine power systems.
Optimization Based Methods:
This section reviews the optimal placement of FACTS controllers based on various
optimization techniques such as a linear and quadratic programming, non-linear optimization
programming, integer and mixed integer optimization programming, and dynamic optimization
programming. A non-linear optimization programming techniques has been proposed for optimal
network placement of SVC controller and a Benders Decomposition technique has been used for these
solutions. A mixed integer optimization programming algorithm has been proposed for allocation of
FACTS controllers in power system for security enhancement against voltage collapse and corrective
controls, where the control effects by the devices to be installed are evaluated together with the other
controls such as load shedding in contingencies to compute an optimal VAR planning, a mixed
integer non-linear optimization programming algorithm is used for determine the type, optimal
number, optimal location of the TCSC for loadability and voltage stability enhancement in
deregulated electricity markets. A mixed integer optimization programming algorithm has been used
for optimal location of TCSC in a power system .
NEURAL NETWORKS IN POWER SYSTEMS
Several papers dealing with ANN applications in power systems are briefly described in the
subsections below. They have been grouped with respect to the following application areas: Static and
dynamic security assessment, transient stability assessment, identification, modeling and prediction,
control, load forecasting and fault diagnosis. This work, referenced by the most of the authors in
ANNs and power systems, dealt with the assessment of dynamic security. An adaptive pattern
recognition approach based on a feed forward neural net with a back propagation learning scheme was
implemented to synthesize the Critical Clearing Time (CCT). This parameter is one of paramount
importance in the post-fault dynamic analysis of interconnected systems. The net successfully
performed the estimation task for the variable system topology conditions. In [4]-1992, the same
authors described the results of the investigation to "discover" relevant ANN training information.