Asad Davari

Modeling and simulation of the dynamic behavior of a polymer electrolyte membrane fuel cell

The focus of this paper is to develop a mathematical model for investigating the dynamic performance of a polymer electrolyte membrane fuel cell. The model in this work is based on physical laws having clear significance in replicating the fuel cell system and can easily be used to set up different operational strategies. Simulation results display the transient behavior of the voltage within each single cell, and also within a number of such single cells combined into a fuel cell stack system. A linear as well as a nonlinear analysis of the polymer electrolyte membrane fuel cell system has been discussed in order to present a complete and comprehensive view of this kind of modeling. Also, a comparison of the two kinds of analysis has been performed. Finally, the various characteristics of the fuel cell system are plotted in order to help us understand its dynamic behavior. Results indicate that there is a considerable amount of error in the modeling process if we use a linear model of the fuel cell. Thus, the nonlinearities present in the fuel cell system should be taken into account in order to obtain a better understanding of the dynamic behavior of the fuel cell system.

Optimal sensor placement strategy for environmental monitoring using Wireless Sensor Networks

This paper presents a novel strategy in determining an optimal sensor placement scheme for environmental monitoring using Wireless Sensor Networks (WSN). This is accomplished by minimizing the variance of spatial analysis based on randomly chosen points representing the sensor locations. These points are assigned randomly generated measurements based on a specified distribution. Spatial analysis is employed using Geostatistical Analysis (classical variography and ordinary point kriging) and optimization occurs with Monte Carlo Analysis. A simple example of measuring mercury in soil is illustrated in finding the optimal sensor placement using WSNs. Studied variables include the number of sensor locations, variances, and Monte Carlo repetitions.

Control of solid oxide fuel cell for stand-alone and grid connection using fuzzy logic technique

Fuel cells are very promising sources of electricity; environmentally safe and very efficient. The fuel cells have numerous applications: houses, industries, hospitals, vehicles etc. Another facet of these fuel cell applications is distributed generation, the implementation of various power generating resources, near the site of need, for reducing reliance on grid power. Fuel cells generate electricity from hydrogen by a chemical process. In this paper a solid oxide fuel cell mathematical model is adopted. The solid oxide fuel cells are very efficient and the technology is most suited to applications in the distributed generation. The main aim of the paper is to achieve the control of the fuel cell for stand-alone and grid connection. To achieve a grid interface and stand-alone working by designing suitable power conditioning units. The power conditioning unit is needed for the processing of the raw power output of the fuel cell in order to make it usable. The power conditioning unit might consist of only a DC/DC converter or the two stages of a DC/DC converter and a DC/AC inverter. For the stand-alone part the concentration is on the controlled DC power thus, only a boost converter (DC/DC) stage is used. For the grid interface of the solid oxide fuel cell controlled AC power is needed at the interface point, thus, both stages; boost converter as well as the inverter (DC/AC) are needed. A power conditioning unit is designed for this solid oxide fuel cell and for fuel cells in general. The fuzzy logic control strategy is used for designing the controllers.

Hybrid modeling of power system using hybrid Petri nets

Current techniques of power system modeling provide individual modeling methodologies for continuous and discrete-event dynamics, but do not allow scripting of complex behavioral sequence as is required. This paper describes a new methodology for modeling of power system using hybrid Petri nets (HPNs). The HPNs describe hybrid systems, which allow the modeling of coupled discrete-event and continuous dynamics within one integrated description language. The HPN power system model includes protective relaying model and on load tap changer transformer model. This approach has been used to model and analyze the complex interactions occurring in power system.

Real-time sign language recognition based on neural network architecture

In real-time, it is highly essential to have an autonomous translator that can process the images and recognize the signs very fast at the speed of streaming images. In this paper, architecture is being proposed using the neural networks identification and tracking to translate the sign language to a voice/text format. Introduction of Point of Interest (POI) and track point provides novelty and reduces the storage memory requirement.

Temperature Control Framework Using Wireless Sensor Networks and Geostatistical Analysis for Total Spatial Awareness

This paper presents a novel framework for intelligent temperature control in smart homes using Wireless Sensor Networks (WSN) and geostatistical analysis for total spatial awareness. Sampled temperature readings from sensor nodes have the ability to inform the system on temperatures at specific locations. However, these locations where readings are taken do not completely describe the area under consideration. To solve this issue, geostatistical techniques are utilized, which include variography and kriging to predict temperature where measurements are not available. This added information would allow the system to control cooling and heating mechanisms in buildings at every location for improved user comfort.

Differential flatness-based trajectory planning for multiple unmanned aerial vehicles using mixed-integer linear programming

This paper provides a method for planning fuel-optimal trajectories for multiple unmanned aerial vehicles to reconfigure and traverse between goal points in a dynamic environment in real-time. Recent developments in robot motion planning have shown that trajectory optimization of linear vehicle systems including collision avoidance can be written as a linear program subject to mixed integer constraints, known as a mixed integer linear program (MILP). This paper extends the trajectory optimization to a class of nonlinear systems: differentially flat systems using MILP. A polynomial basis for a Ritz approximation of the optimal solution reduces the optimization variables and computation time without discretizing the systems. Based on the differential flatness property of unmanned vehicle systems, the trajectory planner satisfies the kinematic constraints of the individual vehicles while accounting for inter-vehicle collision and path constraints. The analytical fuel-optimal trajectories are smooth and continuous. Illustrative trajectory planning examples of multiple unmanned aerial vehicles are presented.

Modeling and analysis of discrete event behaviors in power system using Petri nets

Apart from the continuous time phenomena various discrete events occur in an electric power system. Previously, the size and operation of the power systems were considerably small and centralized compared to the large, decentralized power systems of the present day. Each discrete event occurring in the power system was manually acknowledged and necessary changes were made in the system operation. But with the recent deregulation, the sizes of the power system have increased rapidly and it is becoming very difficult if not impossible to use the traditional methods. Specialized tools are necessary for modeling and analysis of the discrete events occurring in the power system. A number of formal languages, including Petri nets and finite state machines can be used for this purpose. This paper describes how Petri nets can be used for modeling and analysis of the discrete events occurring in a power system.

Decentralized load frequency control in a deregulated environment using disturbance accommodation control theory

In this paper, a decentralized controller is proposed for the load frequency control problem in a deregulated environment. The deregulation scenario considered here assumes that generating units in each area supply regulated power according to their energy contracts. Disturbance accommodating controllers (DAC) are designed which are decentralized controllers using frequency and tie-line power measurements only. Two examples are given to illustrate the proposed approach.

Context aware wireless sensor networks for smart home monitoring

This paper introduces a temperature control framework for smart homes using wireless sensor networks WSN. A key issue with temperature monitoring and control is standard sampling techniques which take few temperature samples into consideration to make heating and cooling decisions in large areas of space. This results in poor controllability of temperature in unmonitored locations with potentially significant temperature variations in comparison with monitored locations. To solve this problem, spatial analysis techniques, namely geostatistical analysis, can be utilised to predict temperature in unmonitored locations to aid in making more informed decisions on how to heat and cool certain parts of a dwelling. Results show independent temperature control in defined areas using the proposed temperature control framework.