Uncertainty Quantification And Economic Dispatch Models For The Power Grid

The modern power grid is constrained by several challenges, such as increased penetration of Distributed Energy Resources (DER), rising demand for Electric Vehicle (EV) integration, and the need to schedule resources in real-time accurately. To address the above challenges, this dissertation offers solutions through data-driven forecasting models, topology-aware economic dispatch models, and efficient optional power flow calculations for large scale grids. Particularly, in chapter 2, a novel microgrid decomposition scheme is proposed to divide the large scale power grids into smaller microgrids. Here, a two-stage Nearest-Generator Girvan-Newman (NGGN) algorithm, a graphicalclustering-based approach, followed by a distributed economic dispatch model, is deployed to yield a 12.64% cost savings. In chapter 3, a deep-learning-based scheduling scheme is intended for the EVs in a household community that uses forecasted demand, consumer preferences and Time-of-use (TOU) pricing scheme to reduce electricity costs for the consumers and peak shaving for the utilities. In chapter 4, a hybrid machine learning model using GLM with other methods was designed to forecast wind generation data. Finally, in chapter 5, multiple formulations for Alternating Current Optimal Power Flow (ACOPF) were designed for large scale grids in a high-performance computing environment. The ACOPF formulations, namely, power balance polar, power balance Cartesian, and current balance Cartesian, are tested on bus systems ranging from a 9-bus to 25,000. The current balance Cartesian formulation had an average of 23% faster computational time than two other formulations on a 25,000 bus system

Analysis and design of algorithm-based fault-tolerant systems

An important consideration in the design of high performance multiprocessor systems is to ensure the correctness of the results computed in the presence of transient and intermittent failures. Concurrent error detection and correction have been applied to such systems in order to achieve reliability. Algorithm Based Fault Tolerance (ABFT) was suggested as a cost-effective concurrent error detection scheme. The research was motivated by the complexity involved in the analysis and design of ABFT systems. To that end, a matrix-based model was developed and, based on that, algorithms for both the design and analysis of ABFT systems are formulated. These algorithms are less complex than the existing ones. In order to reduce the complexity further, a hierarchical approach is developed for the analysis of large systems

Network Traffic Behavioral Analytics for Detection of DDoS Attacks

As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication flows using continuous learning algorithm that learns the normal pattern of network traffic, behavior of the network protocols and identify a compromised network flow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to$2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network traffic. We use flow based traffic characteristics to analyze the difference in pattern between normal vs anomaly packet.We evaluate several supervised classification algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks

Integrated storage and pretreatment of wheat straw for biofuel production

The study aims for the development of a Pretreatment method for wheat straw, when it is stored at low temperature. The hypothesis is that an initial disruption of the crystalline nature of lignocellulose can improve the efficiency of current pretreatment method during low temperature storage. A silo model storage of wheat straw, with high moisture, involving 3 different fungal species (Holtermanniella takashimae, Pichia anomala and Anthracophyllum discolor) and their combination at two different temperatures (4°C and 15°C) was tested. The microbiology of samples was studied for analyzing the effectiveness of conservation. Dilute acid treatment was done prior to the saccharification with Accellerase™ 1000 enzyme. The soluble fraction of hydrolysate was fermented in 15 ml serum flask with Saccharomyces cerevisiae. The biomass stored with combination of Holtermanniella takashimae and Pichia anomala at 4°C showed significant improvement in initial ethanol yield (2.8% increase in ethanol at P‐value<0.05) compared to the non inoculated wet control sample. A detailed study of the simple sugars released, showed that the total sugar yield for P. anomala inoculated sample was double as that of control sample (29.283g/L Vs 17.43g/L). The ethanol yield for A. discolor inoculated samples (59%) was higher than the theoretical maximum (<50%), which suggests that the saccharification may not have completed at the time of fermentation. The samples stored with P. anomala showed significant inhibition of mold and other contaminants. But the results were compared with wet non inoculated samples which were non sterile and had high microbial load (106 CFU/g of fungus, 108 CFU/g aerobic bacteria, 107 CFU/g of enterobacteria) during incubation. So it is suggested to carry out a future study with simultaneous saccharification and fermentation (SSF) to completely utilize the free sugars and with a dry stored material as control, to avoid the effect of natural microflora

Integrated storage and pretreatment of wheat straw with different fungi : impact on ethanol production and storage microflora

Production of ethanol using cellulosic material as feedstock is crucial for sustainable fuel ethanol production.However a production process based on cellulosic biomass involves several energy and cost intensive steps like storage of biomass, pretreatment, hydrolysis and fermentation, where pretreatment is the energy intensive and troublesome step. This project aimed for an integration of storage and pretreatment step, to get more energy efficiency and more ethanol yield. In the present investigation wheat strawwas used as a model and was stored in moist conditions with different fungal species (Pichia anomala, Pichia stipitis, and Anthracophyllum discolor) inoculated separately in mini-silos for 1 month at 15°C and 4°C. Simultaneous saccharification and fermentation was carried out afterthe storage period and ethanol yields were compared with dry wheat straw as a control.A7.52 % higher ethanol yield (compared to the dry wheat straw) was obtained from wheat straw incubated by P. anomala at 15°C, and 6.87 % higher ethanol yield from P.stipitis inoculated wheat straw incubated at 4°C showed ISP can result in increasing the ethanol yield. Also it was obvious from the study that, the release of sugar from integrated storage and pretreatment (ISP) sample was faster than from the traditional sample. The higher concentration of non-fermentable sugars (eg: xylose, arabinose, mannose etc.) left during fermentation of ISP samples indicate that the ISP process causes more structural damage to the cellulosic substances and produces more sugar release than the control. Moreover P. anomala and P.stipitis showed a biocontrol activity during moist storageby preventing growth of other fungi and enterobacteria in the wheat straw during the one month incubation. In conclusion, ISP acted as an efficient method of storage and resulted in higher ethanol yield

Discrete Micromechanics of Random Fibrous Architectures

Ph.DDOCTOR OF PHILOSOPH

Automatic Gaze Classification for Aviators: Using Multi-task Convolutional Networks as a Proxy for Flight Instructor Observation

In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator\u27s gaze. We first establish the reliability of instructors to assign similar quality to an aviator\u27s scan patterns, and then investigate methods to automate this quality using machine learning. In particular, we focus on the classification of gaze for aviators in a mixed-reality flight simulation. We create and evaluate two machine learning models for classifying gaze quality of aviators: a task-agnostic model and a multi-task model. Both models use deep convolutional neural networks to classify the quality of pilot gaze patterns for 40 pilots, operators, and novices, as compared to visual inspection by three experienced flight instructors. Our multi-task model can automate the process of gaze inspection with an average accuracy of over 93.0% for three separate flight tasks. Our approach could assist existing flight instructors to provide feedback to learners, or it could open the door to more automated feedback for pilots learning to carry out different maneuvers

Metagenomic studies of the gut microbiota: the snake gut microbiota as a model organism

Microbes play a very important role in each individual. The microbial communities and its genetic blueprint greatly influence in many human diseases. Most of the microbe populations are grow in an individual’s gut. Therefore, metagenomics studies on gut microbes are essential to understand the microbial diversity in gut and the knowledge on microbial composition associates with terrestrial animals will be very important for further understand nutrition, diseases and physiological state. Besides, the availability of next generation sequencing technologies gives a better understanding on gut microbiotas communities compare to the first generation sequencing. This paper, we suggested snakes as a model to study microbial metagenomics due to its various compounds can help to cure various illnesses, even kill off unwanted germs from body. Therefore, this paper mainly review on snake gut microbes, secondary metabolites produce by microbes and the benefits of molecular technologies used in metagenomics which can be useful in medical industries and treatment of infectious diseases

A bio-inspired microstructure induced by slow injection moulding of cylindrical block copolymers.

It is well known that block copolymers with cylindrical morphology show alignment with shear, resulting in anisotropic mechanical properties. Here we show that well-ordered bi-directional orientation can be achieved in such materials by slow injection moulding. This results in a microstructure, and anisotropic mechanical properties, similar to many natural tissues, making this method attractive for engineering prosthetic fibrous tissues. An application of particular interest to us is prosthetic polymeric heart valve leaflets, mimicking the shape, microstructure and hence performance of the native valve. Anisotropic layers have been observed for cylinder-forming block copolymers centrally injected into thin circular discs. The skin layers exhibit orientation parallel to the flow direction, whilst the core layer shows perpendicularly oriented domains; the balance of skin to core layers can be controlled by processing parameters such as temperature and injection rate. Heart valve leaflets with a similar layered structure have been prepared by injection moulding. Numerical modelling demonstrates that such complex orientation can be explained and predicted by the balance of shear and extensional flow.This is the author-accepted manuscript. It will be under embargo for 12 months after publication. The final version of this article is published by RSC in Soft Matter and can be found here: http://pubs.rsc.org/en/Content/ArticleLanding/2014/SM/C4SM00884G#!divAbstract