R Joseph Xavier

Control of Heavy-duty Gas Turbine Plants for Parallel Operation Using Soft Computing Techniques

Abstract Gas turbine generators, normally used in isolated operation, require an effective control and design for their parallel operation. Otherwise, the load variations and set-point variations may cause severe stability problems. Soft computing techniques, such as genetic algorithms, artificial neural networks, and fuzzy logic, have been utilized for developing a controller for a gas turbine plant. The proportional-integral-derivative controller is used to control the gas turbine plant because of its versatility, high reliability, and ease of operation. For better performance, the gains of the proportional-integral-derivative controller have been tuned using the Ziegler–Nichols method and genetic algorithm. The artificial neural network and fuzzy controllers are developed, and the performance is compared with the conventional proportional-integral-derivative controller. The results show that the optimal time domain performance of the system can be achieved with the fuzzy logic controller. The fuzzy logic controller removes the steady-state error in less time with no overshoot and oscillation.

Development of Controller for Parallel Operation of Gas Turbine Plants

Abstract Gas turbines are used in biomass plants for energy conversion. An effective control scheme is required for optimal performance during parallel operation. In this article, an optimal proportional-integral-derivative controller is developed for isolated operation. The gas turbines are then connected in parallel, and an effective control scheme is developed for their parallel operation. The response of the gas turbine plants with a proportional-integral-derivative controller in parallel operation shows improved performance.

Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor

ABSTRACT This paper discusses about self-regulating the reference flux in induction motor (IM) direct torque control (DTC) drive by fuzzy logic. Self-regulation is improved by using “Artificial Neural Network (ANN)” and “Adaptive Network Based Fuzzy Inference System (ANFIS)” based reference flux estimators. Furthermore, PI speed controller is investigated to develop the performance of the drive. Two different PI speed controller tuning strategies, manual and Fuzzy Gain Scheduling (FGS), are compared for load torque disturbance. The results clearly show that the modified DTC of IM with “ANFIS-based reference flux estimator and FGS-tuned PI speed controller” is most suitable for torque ripple reduction and speed control.

A measurement technique for direct axis and quadrature axis magnetic characteristics in a projected pole (stationary field type) synchronous machine

The magnetic characteristics of a projected pole stationary field type synchronous machine are important in design and performance studies though they differ along direct axis (d-axis) and quadrature axis (q-axis). It is difficult to determine the q-axis magnetic characteristics, whereas the determination of d-axis magnetic characteristics can be done by performing an open-circuit characteristics test on the machine. In order to estimate both the d–q axes magnetic characteristics by a direct method, various flux measurement techniques such as the moving coil flux meter, ballistic galvanometer, magneto resistance flux meter, Hall effect flux meter and solid-state diode-flux meter have been analysed. A novel approach of using germanium diodes as flux sensors is proposed in this work to determine experimentally the d–q axes magnetic characteristics. The implementation of hardware, experimental investigations and the results are presented here. Furthermore, the merits and applications of the proposed method are also discussed.