Showing 4 results for Fuzzy Control
S. H. Zahiri, H. Rajabi Mashhadi, S. A. Seyedin,
Volume 1, Issue 3 (7-2005)
Abstract
The concepts of robust classification and intelligently controlling the search
process of genetic algorithm (GA) are introduced and integrated with a conventional
genetic classifier for development of a new version of it, which is called Intelligent and
Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision
hyperplanes in the feature space.
It is shown experimentally that the proposed IRGA-classifier has removed two important
weak points of the conventional GA-classifiers. These problems are the large number of
training points and the large number of iterations to achieve a comparable performance with
the Bayes classifier, which is an optimal conventional classifier.
Three examples have been chosen to compare the performance of designed IRGA-classifier
to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the
Wine data classification, and radar targets classification from backscattered signals. The
results show clearly a considerable improvement for the performance of IRGA-classifier
compared with a conventional GA-classifier.
A. Hajizadeh, M. Aliakbar-Golkar,
Volume 3, Issue 1 (1-2007)
Abstract
The operation of Fuel Cell Distributed Generation (FCDG) systems in
distribution systems is introduced by modeling, controller design, and simulation study of a
Solid Oxide Fuel Cell (SOFC) distributed generation (DG) system. The physical model of
the fuel cell stack and dynamic models of power conditioning units are described. Then,
suitable control architecture based on fuzzy logic control for the overall system is presented
in order to active power control and power quality improvement. A MATLAB/Simulink
simulation model is developed for the SOFC DG system by combining the individual
component models and the controllers designed for the power conditioning units.
Simulation results are given to show the overall system performance including active power
control and voltage regulation capability of the distribution system.
A. A. Khodadoost Arani, B. Zaker, G. B. Gharehpetian,
Volume 13, Issue 1 (3-2017)
Abstract
The Micro-Grid (MG) stability is a significant issue that must be maintained in all operational modes. Usually, two control strategies can be applied to MG; V/f control and PQ control strategies. MGs with V/f control strategy should have some Distributed Generators (DGs) which have fast responses versus load changes. The Flywheel Energy Storage System (FESS) has this characteristic. The FESS, which converts the mechanical energy to electrical form, can generate electrical power or absorb the additional power in power systems or MGs. In this paper, the FESS structure modeled in detail and two control strategies (V/f and PQ control) are applied for this application. In addition, in order to improve the MG frequency and voltage stability, two complementary controllers are proposed for the V/f control strategy; conventional PI and Fuzzy Controllers. A typical low voltage network, including FESS is simulated for four distinct scenarios in the MATLAB/ Simulink environment. It is shown that fuzzy controller has better performance than conventional PI controller in islanded microgrid.
H. Ahmadi, A. Rajaei, M. Nayeripour, M. Ghani,
Volume 14, Issue 4 (12-2018)
Abstract
Considering the increasing usage of the clean and renewable energies, wind energy has been saliently improved throughout the world as one of the most desired energies. Besides, most power houses and wind turbines work based on the doubly-fed induction generator (DFIG). Based on the structure and the how-ness of DFIG connection to the grid, two cases may decrease the performance of the DFIG. These two cases are known as a fault and a low-voltage in the grid. In the present paper, a hybrid method is proposed based on the multi-objective algorithm of krill and the fuzzy controller to improve the low-voltage ride through (LVRT) and the fault ride through (FRT). In this method, first by using the optimal quantities algorithm, the PI controllers’ coefficients and two variables which are equal to the demagnetize current have been calculated for different conditions of fault and low voltage. Then, these coefficients were given to the fuzzy controller. This controller diagnosed the grid condition based on the stator voltage and then it applied the proper coefficients to the control system regarding the diagnosed condition. To test the proposed method, a DFIG is implemented by taking the best advantages of the proposed method; additionally, the system performance has been tested in fault and low voltage conditions.
