K. Malekian, J. Milimonfared, B. Majidi,
Volume 5, Issue 1 (March 2009)
Abstract
The main theme of this paper is to present novel controller, which is a genetic
based fuzzy Logic controller, for interior permanent magnet synchronous motor drives with
direct torque control. A radial basis function network has been used for online tuning of the
genetic based fuzzy logic controller. Initially different operating conditions are obtained
based on motor dynamics incorporating uncertainties. At each operating condition, a
genetic algorithm is used to optimize fuzzy logic parameters in closed-loop direct torque
control scheme. In other words, the genetic algorithm finds optimum input and output
scaling factors and optimum number of membership functions. This optimization procedure
is utilized to obtain the minimum speed deviation, minimum settling time, zero steady-state
error. The control scheme has been verified by simulation tests with a prototype interior
permanent magnet synchronous motor.
H. Sh. Solari, B. Majidi, M. Moazzami,
Volume 15, Issue 4 (December 2019)
Abstract
In this paper, a new method for modelling and estimation of reliability parameters of power transformer components in distribution and transmission voltage levels for preventive-corrective maintenance schedule of transformers is proposed. In this method, with optimal estimation of Weibull distribution parameters using least squares method and input data uncertainty reduction, failure rate and probable distributions of power transformers’ components as the key parameters of equipment reliability is estimated. Then by using the results of this modelling, a maintenance schedule for evaluation the effect of maintenance on reliability of this equipment is presented. Simulation results using real failure data of 196 power transformers on 33 to 230kV voltage levels show that applying the proposed method in addition to uncertainty reduction of raw input data and better estimation of equipment reliability, improve decision making regarding maintenance schedule of power transformers.
