Rosdiazli Ibrahim

An intelligent speed controller for indirect field-oriented controlled induction motor drives

For the development of speed and/or torque control, this study represents the technique for the control of induction motor (IM) drive which is indirect field-oriented control. The Fuzzy-PI and Conventional-PI controller have been studied in a closed-loop speed control. By separating the current produced by the stator into flux producing (d-q) and the torque currents profile of an IM have been convoluted in the indirect field-oriented control. Via an intelligence-based Fuzzy-PI controller, the components of the current (Iqs and Ids,) have been established for an IM. By considering the behavior of the Fuzzy Logic Controller (FLC), the speedy responses, torque and stator currents have been observed and used later for comparison with the PI-Controller. By employing MATLAB/Simulink, the simulation has been executed for the purpose of determining the performance of the controller. It was found out from the results that the results shown by the Fuzzy-PI controller were more reliable and accurate than the PI-controller.

Development of an intelligent condition monitoring system for AC induction motors using PLC

This paper provides an overview of several condition monitoring (CM) techniques for the alternating current (AC) motor in a precise manner and this will be useful when selecting proper condition monitoring technique for specific application. Condition monitoring is a process of monitoring operating parameters of machine to reveal the trend of monitored characteristics to predict machine health. Protection of motors has become challenging task in industries. Different condition monitoring techniques are summarized with the specific advantages and disadvantages. Mathematical analysis of stator current is the latest and non-invasive and economical method for the condition monitoring of AC motors. A novel intelligent diagnostic CM system has been proposed. The proposed system will provide continuous real time tracking of different faults and estimates severity of faults for automatic decision making.

An intelligent diagnostic system for the condition monitoring of AC motors

The implication of failure to diagnose the faults of AC motors at the inception stage would lead to unscheduled machine down-time that can upset datelines and causes financial losses. Classical methods which involve thermal switches, overload relays, timers, magnetic contactors and programmable integrated circuits were adopted in early days. But these methods did not guarantee full protection of motor. This paper introduces new approach to condition monitoring of motors through intelligent diagnostic system based on stator current analysis and programmable logic controllers. The proposed method is validated by experiments performed on five different defect levels. Experiment results show that this technique is more accurate, cost effective, safe and provides continuous real time tracking of faults and estimates the severity through visual indication.

Analysis of bearing outer race defects in induction motors

The literature review presents the various kind of existing condition monitoring methods and highlights the need for an economical intelligent fault diagnosis system. This paper demonstrates the procedures taken in developing a condition monitoring system for the motor bearing fault identification, utilizing the commonly available motor stator current and voltage. A novel intelligent diagnostic condition monitoring system has been proposed which provide continuous real time tracking of the bearing outer race defects and determines severity of the defects which can be adopted for automatic decision making. The proposed method is validated through experiments at four different defect levels under no load and full load conditions of the motor.

Analysis and modeling of indirect field-oriented control for PWM-driven induction motor drives

For the controlling of the speed and/or torque of the induction motor (IM) this paper represents the development of a PI-controller for the control of induction motor. The PWM drive system of an induction motor and its controller have been modelled and analyzed. The objective of this control is to ensure the stability of the controller when subjected to the variations in a load torque and reference speed. To gain the required results for the whole system, in system parameters, open loop and closed loop have been considered. Through simulation, the performance of the controller has been investigated using MATLAB/Simulink. The basic framework of the induction motor which is drive controlled via a PI-controller performed as intended when a series of tests were conducted.

Design of Multi Model Predictive Control for nonlinear process plant

This paper presents a new approach to deal with the nonlinearity of control system by using Multi Model Predictive Control (MPC) strategies. The idea of this research is using Fuzzy model to divide the nonlinear system into several sub linear systems which can be applied linear MPC controller. Firstly, the structure of Takagi-Sugeno (T-S) Fuzzy model is developed and optimized using Subtractive Clustering method. Then the obtained T-S Fuzzy model is trained using Adaptive-Network Based Fuzzy System (ANFIS) to derive optimal the parameters of models. Since the obtained T-S Fuzzy model is described in number of rules (local model) which present linear relationship between outputs and inputs so that a number of linear MPC controller is designed for each local model. The global control signal is combined from control signal of each local MPC controller by parallel distributed compensation technique. The proposed multi MPC scheme applying for CSTR nonlinear process shows that Multi Model Predictive Control based on T-S Fuzzy model can improve the performance of conventional MPC in nonlinear control system.

Convertible unified power quality conditioner to mitigate voltage and current imperfections

This paper proposes a novel convertible unified power quality conditioner (CUPQC) by employing three voltage source converters (VSCs) which are connected to a multi-bus/multi-feeder distribution system to mitigate current and voltage imperfections. The control performance of the VSCs is characterized by a minimum of six circuit open/close switches configurable in a minimum of seventeen combinations to enable the CUPQC to operate as shunt and series active power filters (APFs), unified power quality conditioner (UPQC), interline-UPQC (IUPQC), multi-converter UPQC (MC-UPQC) and generalized UPQC (GUPQC). The simulation and compensation performance analysis of CUPQC are based on PSCAD/EMTDC.

AC motor fault diagnosis at incipient stage using programmable logic controller

Alternating Current (AC) motors are extensively used in the industries and it is essential to protect these motors from unexpected breakdowns and to detect faults at incipient level so that planned maintenance could be done. In the last three decades various fault diagnosis techniques has been developed but these techniques are considered intrusive as they require accessability to motor for data collection. Also the electromechanical devices, sensors and integrated circuits used in these techniques are expensive and not reliable. Special attention is given to non-invasive techniques to detect defects without accessing the machines. This paper attempts to provide a new approach for condition monitoring of AC motors through intelligent fault diagnosis based on programmable logic controllers. The stator current analysis which is non-invasive and economical method is used with LabVIEW signal processing tools. The current signal is analyzed via Fast Fourier Power spectrum to detect the bearing faults at incipient stages. The technique is validated at three different speeds.

Intelligent fault diagnosis for instrument in gas transportation system

The reliability and availability of the metering system plays a crucial part in the gas transportation system because it affects the billing integrity between the gas supplier and their customers. A slight error in measurement will lead to significant monetary impact. Therefore, the challenge lies in building an online verification system that is able to check the accuracy of instruments in the metering system as well as to enable the verification system to reconstruct data in the case of faults on the instruments during operation time. This paper proposes hypotheses as well as a research plan to deal with these problems. The proposed idea invests in the behavior and relation among instruments based on empirical models, particularly Neural Network. Based on this model, the faults of instrument will be detected and unreliable data will be corrected.

Cascade multi-level converter based generalized unified power quality conditioner

This paper proposes a novel custom power controller named as generalized unified power quality conditioner (GUPQC) based on cascade multilevel converter (CMC) for power quality improvement of a distribution system by using both five-level and three-level CMC. In a multi-bus, three independent feeders distribution system, the load reactive power and current harmonics of one feeder are compensated by a five-level CMC as shunt compensator, while in the other two feeders the voltage harmonics, voltage sag/swell and interruptions are compensated by two three-level CMCs as series compensators. The simulation and compensation performance analysis are based on PSCAD/EMTDC.