S. Ushakumari

Fault detection and diagnosis of an induction motor using fuzzy logic

Induction motors are critical components in many industrial processes. Therefore online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor having different types of faults like winding faults. To model shorted stator turns, the model assumes that the one of the phase has two windings in series, representing the unaffected portion and the shorted portion. This work presents a reliable method for the detection of stator winding faults based on monitoring the stator current amplitudes. In this method, fuzzy logic is used to make decisions about the motor condition. The fuzzy system is able to identify the motor stator condition with high accuracy.

Sensorless vector control of SVPWM inverter fed induction machine using MRAS-sliding mode

One of the most important technologies for electric vehicles is the drive control technology which does not require a position or speed sensor. In a sensorless vector controlled induction machine, the speed must be estimated from the system measurements. Model Reference Adaptive System (MRAS) based techniques are one of the best methods to estimate the rotor speed due to its performances and straight forward stability approach. The performance of the traditional rotor flux MRAS with PI controller deteriorates due to integrator drift and sensitivity to current measurement noise. Practically, system tuning is difficult and may yield under damped responses. In this paper, a new robust MRAS scheme based on sliding mode technique to estimate the rotor speed of a sensorless vector controlled induction machine is proposed. Further, a space vector pulse width modulated (SVPWM) voltage source inverter is used to feed the induction machine rather than the traditional sinusoidal PWM inverter for the simulation. The drive system with the proposed adaptive mechanism is simulated by MATLAB/Simulink to verify the principles of the technique and compared its performance with that of PI controller.

Adaptive neuro-fuzzy controller for improved performance of a permanent magnet brushless DC motor

This paper deals with the mathematical modelling of a permanent magnet brushless DC motor, considering the nonlinearities in the torque-balance equation under a closed loop operation with a set reference speed. A controller based on the adaptive neuro-fuzzy inference system (ANFIS) is developed to minimize the overshoot and settling time following sudden changes in load torque. The entire system is modeled and simulated using the SIMULINK toolbox. The advantages of fuzzy logic and neural network are fused together to form a connectionist adaptive network based fuzzy logic controller. The required data for training the ANFIS controller are generated by simulation of the closed loop system with a conventional PID controller. The overshoot present in the transient response with conventional controller is eliminated using the ANFIS controller. The transient deviation of the response from the set reference following the variation in load torque is found to be negligibly small along with a desirable reduction in settling time for the ANFIS controller.

Optimal management of islanded microgrid using binary particle swarm optimization

Microgrids are predicted to play a major role in the future, as they are capable of improving the quality and reliability of power in large power systems. The critical issues in microgrid are ensuring continuous availability of energy to the critical loads, and providing supply to the maximum possible portion under any abnormal conditions by topology management. An intelligent load shedding and fast reconfiguration of the system is therefore necessary in order to serve the critical loads and to maintain a proper power balance in the microgrid. This paper presents a novel strategy for load shedding and reconfiguration of microgrid using V-shaped transfer function for binary particle swarm optimization. The proposed load shedding strategy can also be used in the event of reduction in generation from the intermittent renewable energy sources, which is the major source of power in islanded microgrid. The strength of the proposed strategy is illustrated with MATLAB results on 8 bus Shipboard Power System(SPS) and modified Consortium for Electric Reliability Technology Solutions(CERTS) microgrid.

CFT based optimal PWM strategy for three phase inverter

Selective Harmonic Eliminated Pulse Width Modulation (SHEPWM) techniques necessitate the solution of non-linear transcendental equations for achieving optimal PWM strategy. Online solution of these equations is very difficult and the conventional method is to store the offline prepared optimal switching angles in memory, which are to be recalled for online generation of PWM waveforms. The need to achieve continuous voltage and frequency control, especially for AC machines, has imposed large memory requirements and computation time for PWM systems. This paper proposes an effective online method to arrive optimal PWM switching angles based on Curve Fitting Technique (CFT) for three phase inverters. Polynomial functions for optimal switching angles are generated using CFT in MATLAB. The correctness and harmonic performance of the proposed method is verified by comparing the results with the Newton-Raphson (N-R) iterative method.

Optimal load frequency controller for a deregulated non-reheat thermal power system

Load frequency control (LFC) is a very important issue in the operation and control of power systems for supplying sufficient and reliable electric power with good quality. With deregulation of power system, major changes have been introduced into the structure of the electric power utilities all around the world. In this new framework, consumers are free to make a choice among competing providers of electric power. This paper deals with the modelling and simulation of optimal load frequency control in an interconnected two area non-reheat deregulated power system.

Biogeography based tuning of PID controllers for Load Frequency Control in microgrid

This paper presents a novel technique for tuning PID controllers for Load Frequency Control (LFC) in an autonomous hybrid microgrid using Biogeography Based Optimization (BBO). The hybrid system consists of Wind Turbine Generators (WTG), Solar Photo Voltaic generators (SPV), Diesel Engine Generators (DEG), Fuel Cells (FC), Battery Energy Storage System (BESS), Flywheel Energy Storage System (FESS) and Aqua Electrolyser (AE). In this work, BBO algorithm is employed to search for the optimal controller parameters and to achieve the best coordinated control of the hybrid system. Further, the dynamic performance analysis of the hybrid system is done by subjecting the system to various disturbance conditions such as sudden variation of wind power, solar irradiation and load. The simulation results obtained are compared with conventional method. The result shows that the PID controller tuned using BBO is superior in both the transient as well as steady state performances in comparison with conventional method.

Robust flight control of a typical RLV during re-entry phase

Due to the inherent nonlinearities of Reusable Launch Vehicle (RLV) dynamics, its changing properties during flight and the engineering difficulties to predict its aerodynamics with high levels of fidelity, flight control requires strategies that allow to cope up with the non-linearity of the model and assure robustness in the presence of inaccuracies and changes in configuration. This paper presents a flight control strategy based on dynamic inversion controller which is designed for the re-entry phase of Reusable Launch Vehicle. In order to solve the robustness problem of regular explicit Nonlinear Dynamic Inversion (NDI) control law, the Incremental Nonlinear Dynamic Inversion (INDI) control law is proposed. Sensitivity to model mismatch is eliminated by feeding back state acceleration in INDI approach. The improved control law design is validated for re-entry phase of RLV for nominal and aerodynamic perturbation cases. Analysis of simulation results reveal that the robustness of the control law is increased.

Four-quadrant operation of sensorless FOC induction machine in field weakening region using MRAS-sliding mode observer

In high speed induction machine drive such as elevator system, 50% to 200% of the torque of the steady state torque is needed to get the required acceleration and deceleration for a short period. Also the machine should generate positive torque as well as negative torque at either direction. Thus the four-quadrant operation in torque-speed plane is necessary in such applications. In a sensorless field oriented controlled (FOC) induction machine, the speed estimation in Field Weakening (FW) region and the sensitivity to parameter error of the motor is a major problem. Model Reference Adaptive System (MRAS) approach is one of the best techniques to estimate the rotor speed due to its straight forward stability approach. Space vector pulse width modulated (SVM) voltage source inverter is preferred to feed the FOC induction machine rather than the traditional sinusoidal pulse width modulated (SPWM) inverter due to the full utilization of voltage and current rating of the inverter. In this paper, a sensorless FOC induction motor drive with MRAS scheme based on sliding mode (SM) technique to estimate the rotor speed in the FW region for four-quadrant operation using SVM fed inverter is modeled. The drive system is simulated by MATLAB/Simulink and the performance of the technique is evaluated.

Rotor fault analysis of an induction motor using FEM

Squirrel cage induction motors are commonly used in industrial and domestic applications, because of their advantages. Now a days on line parameter monitoring of induction motors with the help of fault detection algorithms are very useful to the early detection of the fault and protect the motor from catastrophic damages. Rotor fault analysis of three phase induction motors is very helpful for the design of the fault detection algorithm. The rotor of the induction motor is subjected to very low voltages and much higher temperature, compared to the stator winding. Therefore most common failure mode in the rotor is open or broken bar of the rotor. Main aim of this paper is to conduct the study of rotor fault analysis of a three phase squirrel cage induction motor using Finite Element method (FEM) and its result processing is done using MATLAB SIMULINK. This paper presents a systematic approach to analyze the rotor faults of an induction motor.