Coordinated Control of Flexible AC Transmission System Devices Using an Evolutionary Fuzzy Lead-Lag Controller With Advanced Continuous Ant Colony Optimization

Abstract

This paper proposes an evolutionary fuzzy lead-lag control approach for coordinated control of flexible AC transmission system (FACTS) devices in a multi-machine power system. The FACTS devices used are a thyristor-controlled series capacitor (TCSC) and a static var compensator (SVC), both of which are equipped with a fuzzy lead-lag controller to improve power system dynamic stability. The fuzzy lead-lag controller uses a fuzzy controller (FC) to adaptively determine the parameters of two lead-lag controllers at each control step according to the deviations of generator rotor speeds. This paper proposes an Advanced Continuous Ant Colony Optimization (ACACO) algorithm to optimize all of the free parameters in the FC, which avoids the time-consuming task of parameter selection by human experts. The effectiveness and efficiency of the proposed evolutionary fuzzy lead-lag controller for oscillation damping control is verified through control of a multi-machine power system and comparisons with other lead-lag controllers and various population-based optimization algorithms.Index Terms-Ant colony optimization, flexible AC transmission system (FACTS), fuzzy control, static var compensator (SVC), swarm intelligence, thyristor controlled series capacitor (TCSC).

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments

“…Several studies on hybrids of discrete ACO and continuous optimization algorithms have been proposed to address continuous optimization problems [23]- [25]. Recently, several continuous ACO have also been proposed [12], [26]- [30], such as ACO in continuous space (ACO R ) [27], design of fuzzy rule-based systems using continuous ACO (RCACO) [28], advanced continuous ACO (ACACO) algorithms [30], and species-differentialevolution-activated continuous ant colony optimization (SDE-CACO) [12]. The SDE-CACO is a hybrid algorithm, in which the information provided by species differential evolution is used to guide the ants toward a potentially better solution.…”

Self-organizing fuzzy sliding-mode control for a voice coil motor

“…To resolve this problem, several learning methods have been proposed [8][9][10]. Though favorable control performance can be achieved in [8][9][10], the learning algorithms only take care of parameter learning but neglect structure learning of fuzzy rules.…”

“…Though favorable control performance can be achieved in [8][9][10], the learning algorithms only take care of parameter learning but neglect structure learning of fuzzy rules. Time-consuming trial-and-error process is needed to determine the number of fuzzy rules.…”

Design of a hybrid controller for voice coil motors with simple self-learning fuzzy control

“…The fuzzy rules should be pre-constructed to achieve the design performance by trial-and-error; however, this trial-and-error tuning procedure is time-consuming. To overcome the trailand-error tuning of the membership functions and fuzzy rules, the fuzzy control scheme has been combined with many different methods to tune the fuzzy control rules [10][11][12][13][14][15][16]. In [10][11][12], the adaptive fuzzy control approach is designed to online tune the fuzzy rules in the Lyapunov stability theory; however, the approximation error between the system uncertainty and fuzzy uncertainty observer may cause instability of the closed-loop system.…”

“…In [13,14], the fuzzy neural network approach with parameter learning are proposed by using backpropagation learning algorithm, but it is based on gradient descents that is easily trapped at local minima. In [15,16], the evolution algorithm has been successfully applied to solve many optimization problems. However, it always leads to heavy computational costs and the convergence speed may be slow.…”