Rami J. Haddad

Relief and emergency communication network based on an autonomous decentralized UAV clustering network

The recent years have witnessed an increase in the natural disasters in which the destruction of the essential communication infrastructure has significantly affected the number of casualties. In 2005, Hurricane Katrina in the United States resulted in over 1,900 deaths, 3 million land-line phones disconnections, and more than 2,000 cell sites going out of service. This incident highlighted an urgent need for a quick-deployment efficient communication network for relief purposes. In this paper, we propose a fully autonomous system to deploy Unmanned Aerial Vehicles (UAV) as the first phase disaster recovery communication network for wide-area relief. An automation algorithm has been developed to control the deployment and positioning of UAVs based on a traditional cell network structure utilizing 7-cell clusters in a hexagonal pattern using MAVLink. The distributed execution of the algorithm is based on a centralized management of UAV cells through assigning higher ranked UAVs referred to as supernodes. The algorithm autonomously elects supernodes based on weighted variables and dynamically handles any changes in total number of UAVs in the system. This system represents a novel approach for handling a large-scale autonomous deployment of a UAV communications network. The proposed autonomous communication network was verified and validated using software simulation and physical demonstration using identical quadrotor UAVs.

Anti-islanding techniques for Inverter-based Distributed Generation systems - A survey

The increased penetration of Inverter-based Distributed Generation (DG) in Smart Grid systems requires an adequate level of monitoring and detection especially under islanding conditions. Islanding occurs when a DG system is disconnected from the rest of the power grid. These DG systems are usually independently owned and controlled, thus when islanding occurs, the electric utility loses control and supervision over that section of the power grid. As a consequence, islanding can present serious safety hazard since a presumed disconnected power line can still unexpectedly be fed by nearby DG sources. Furthermore, prolonged islanding can also prevent reconnection to the power grid and may cause damage due to voltage and frequency excursions. Therefore, islanding detection, which is also called “Anti-Islanding”, is one of the most critical aspects of the integration of DG sources into the power grid. There has been considerable research on developing detection techniques, however, recent breakthroughs in this field have resulted in significant modifications to the Anti-Islanding taxonomy which is the subject of this investigation. In this paper, a comprehensive survey was conducted with the objective of highlighting the latest Anti-Islanding techniques presented in the literature. Extensive comparisons of improvements and limitations of these new techniques was provided. Finally, open research areas in this field were identified.

A passive islanding detection approach for inverter-based distributed generation using rate of change of frequency analysis

Islanding occurs when a Distributed Generation (DG) source continues to energize an isolated section of a power system even after it was disconnected from the main power grid. Since islanding can cause hazardous conditions to people and equipment, current utility standards require that islanding be quickly detected by protective relays and inverters that are parts of the DG system. Passive islanding detection techniques, unlike their active counterparts, monitor system parameters without injecting any disturbance into the grid. Although widely used, passive detection techniques are not very effective in detecting islanding especially in cases where there is small power mismatch and they also may trigger false detection in some non-islanding cases. To address these drawbacks, a novel and effective passive islanding detection technique that conforms to standard regulations has been presented in this paper. The proposed detection technique is based on monitoring the oscillations in the Rate of Change of Frequency (ROCOF) measured at the Point of Common Coupling (PCC) in the system. The proposed detection technique was developed and tested on a grid connected photovoltaic DG system using simulation. Results indicated that this technique was not only capable of detecting islanding when it occurs but also able to accurately distinguish between islanding and non-islanding under a wide range of operating conditions.

Video Bandwidth Forecasting

We survey twenty years of research literature on video frame size forecasting. We organize the discussion of the literature using model type and model parameters as a taxonomy. We discuss how to use video frame size forecasts to forecast video bandwidth requirements. We provide extensive comparisons of forecast accuracy among the various mechanisms using data extracted from the literature and a set of common experiments we conducted. Lastly, we summarize our findings with respect to forecast accuracy and we identify open areas for research.

An artificial Neural Network model for wind energy estimation

Wind energy resources are ideally suited for distributed generation systems to provide electricity for residential use. This paper proposes a novel method for wind energy estimation in the state of Georgia. This method is based on Artificial Neural Network (ANN) using real data obtained from several weather station sites around the state. The proposed ANN model was trained and then tested using a local station located in Savannah. The ANN inputs are elevation, latitude, longitude, day, temperatures (min/max), and the output is the daily wind speed. The model was efficiently implemented in Simulink environment using closed-form algebraic equations which eliminated the need for repeated training. The ANN model was formulated with suitable numbers of layers/neurons which was trained and tested with excellent regression constant. Furthermore, the ANN model has the ability to interpolate between learning curves to generate wind speed estimates for different locations. It is anticipated that this model will be able to successfully select sites for wind turbine installations for residential applications in the state of Georgia.

Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast

Video bandwidth forecasts can empower video transport mechanisms with a new intelligence that can increase the efficiency of Dynamic Bandwidth Allocation. We exploit the fact that for pre-recorded video, the size of every video frame is known prior to the video being delivered. We propose Feed-Forward Bandwidth Indication (FFBI) which feeds video frame sizes forward in a sequence of video frames. We extend FFBI to live video by introducing a delay at the source equivalent to the forecast window. We compare FFBI to the most accurate forecast methods found in the literature. With network transport of video projected to supplant other transport mechanisms over the next few years, we conduct a performance analysis of FFBI within Ethernet Passive Optical Networks (EPONs). We find that the use of FFBI can provide a 50% reduction in queueing delay compared to the use of no forecasting and a 35% reduction in queueing delay compared to other forecasting methods. In addition, we find that FFBI can provide a very significant reduction in queueing delay variation compared to the use of no forecasting or other forecasting methods.

A technical and economical study of implementing a micro-grid system at an educational institution

Micro-grids are among the basic components in the future distributed generation smart grid systems. The transmission efficiency improvements and the utilization of renewable energy sources are some of the key advantages of using micro-grids in power systems. In this paper, the technical and economical study of implementing a micro-grid system at an educational institution is discussed and presented. Multiple types of distributed generators were considered including gas-fired generator units, solar photovoltaic, fuel cell, and bio-power systems. Several simulations were conducted using HOMER and a detailed economic analysis of implementing a micro-grid system is presented including recommended actions.

A multiuser EEG based imaginary motion classification using neural networks

Using Electroencephalography (EEG) to detect imaginary motions from brain waves to interface human and computer is a very nascent and challenging field that started developing rapidly in the past few decades. This technique is termed as Brain Computer Interface (BCI). BCI is extremely important in case of people who are incapable of communicating due to spinal cord injury. This technique uses the brain signals to make decisions, control and communicate with the world using brain integration with peripheral devices and systems. In this paper, in order to classify imaginary motions, raw data are used to train a system of neural networks with a majority vote output. EEG data for 3 subjects are used from the BCI Competition III dataset V. Each subject has data collected in three sessions representing three different types of imaginary motions. Using an optimized set of electrodes, classification accuracy was optimized for the three users as a group. A cross validation method is applied to improve the reliability of the generated results. The optimization resulted in an electrode structure consisting of 15 electrodes with a relatively high classification accuracy of almost 80%.

Voltage Ripple-Based Passive Islanding Detection Technique for Grid-Connected Photovoltaic Inverters

One of the main challenges of integrating distributed generation into the power grid is islanding, which occurs when a disconnected power line is adversely energized by a local distributed generation source. If islanding is not quickly detected, it can present serious safety and hazardous conditions. Conventional passive detection techniques used today are entirely dependent on the parameters of the power system, which under certain operating conditions may fail to detect islanding. In this paper, a novel and efficient passive islanding detection technique for grid-connected photovoltaic-based inverters is presented. In this technique, the ripple content of the inverter output voltage at the point of common coupling is monitored for deviations using time-domain spectral analysis. Islanding is then detected whenever the ripple spectral content exceeds a preset threshold level for a certain period of time. The performance of this technique was extensively tested and quantified under a wide range of operating conditions. It was determined that the proposed technique did not exhibit any non-detection zone and was able to detect all types of islanding cases within 300 ms of the allowed delay time. Furthermore, the proposed technique was found to be robust and inherently immune to other degrading factors, since it is relatively independent of system parameters, power system scaling, or the number of distributed generation sources present within the islanding zone.

Modeling Network Protocol Overhead for Video

With the gaining popularity of video communication over the Internet, it is important for networking protocol researchers to have tools to analyze and simulate video. We present the results of an extensive study to understand which applications and protocols are used for video communication over the Internet. We obtained an extensive list of sources of video content around the Internet (e.g., Netflix, Hulu, and You Tube) and used Wire shark to capture packets containing video from these various video sources. We found Adobe Flash to be, by far, the most common application used to deliver video over the Internet. Adobe Flash uses its own proprietary application layer protocol called RTMP. We used the results of this study to develop analytical models for the network protocol overhead added to video that is communicated over the Internet. In addition, we have developed simulation tools that we are making available to the research community.