volume 01 issue 02

This paper deals with the application of non-linear predictive control with neural networks to Proton Exchange Membrane Fuel Cells (PEM-FC). The control objective is to regulate the cell voltage, acting on the hydrogen pressure, trying to reduce the variation of the input control variable. An analysis of the non-linearities of the fuel cell stack has been carried out, making use of a suitable fuel cell model.
The non-linear predictive control has been implemented by several neural networks (multi value perceptrons), after dividing the operating domain into three areas according to the cell current value (low loads, quasi-linear zone and high loads).
Simulation results have been provided and discussed, showing the goodness of the proposed non-linear control technique in reducing the variations of hydrogen pressure.

This paper presents solution of optimal power flow ( OPF) problem of electrical power system via a genetic algorithm of real type. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines.
CPU times can be reduced by decomposing the optimization constraints to active constraints that affect directly the cost function manipulated directly the GA, and passive constraints such as generator bus voltages and transformer tap setting maintained in their soft limits using a conventional constraint load flow. The algorithm was developed in an Object Oriented fashion, in the C++ programming language. This option satisfies the requirements of flexibility, extensibility,maintainability and data integrity. The economic power dispatch is applied to IEEE 30-bus model system ( 6-generator, 41-line and 20-load) . The numerical results have demonstrate the effectiveness of the stochastic search algorithms
because its can provide accurate dispatch solutions with reasonable time. Further analyses indicate that this method is effective for large-scale power systems.

It is known that power electronics and its related subjects are not easy to understand for students taking them for first time. This is due to nature of the subjects which involve many areas and disciplines. The introduction of generalpurpose simulation package has helped the student a step further in understanding this subject. However, because of the generality of these tools and their drag-anddrop and ad-hoc features, the students still face problems in designing a converter circuit. In this paper, the problem above is addressed by introducing a learning aid
tool that guides the student over prescribed steps to design a power electronics circuit. The tool is knowledge-based system where its knowledge base encompasses
two types of knowledge ; topologies and switching devices. The first step in the design procedure is the selection of the application of the desired circuit. Then few steps are to be followed to come out with the appropriate topology with the optimum switching devices and parameters. System structure, its different modules and the detailed design procedure are explained in this paper

The objective of this paper is to present an analysis of how to get high performance of an induction motor drive for both dynamic and steady state operation. In order to improve a good dynamic response in transient ( minimize the speed drop) , an algorithm for a maximum torque capability of the available maximum inverter current and voltage is presented. When the rotor speed reaches its reference, a loss minimization algorithm is developed to improve high efficiency. Simulation studies show the performance of the proposed work.