Engineering Commons^™

Hardware Based Testing Of Communication Based Control For Dc Microgrid, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed

Publications and Research

This paper further describes our work presented in Industry Application Society 2016 Conference, with more details related to the control and operation of the microgrid. The DC microgrid facility was custom designed and implemented at CCNY with minimal off-the-shelf components to enable flexibility and reconfiguration capability. The design steps, requirements, and experimental results of the developed testbed were discussed. As a case study, a central controller for energy management algorithm was developed and tested under several operational scenarios. The experimental results verify the applicability of the developed testbed for validating DC microgrid controllers.

Energy Management Algorithm For Resilient Controlled Delivery Grids, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed, Haim Grebel, Roberto Rojas-Cessa

Publications and Research

Resilience of the power grid is most challenged at power blackouts since the issues that led to it may not be fully resolved by the time the power is back. In this paper, a Real-Time Energy Management Algorithm (RTEMA) has been developed to increase the resilience of power systems based on the controlled delivery grid (CDG) concept. In a CDG, loads communicate with a central controller, periodically sending requests for power. The central controller runs an algorithm, based on which it may decide whether to grant the requested energy fully or partially. Therefore, the CDG limits loads discretionary access to …

Quantitative Analysis Of Regenerative Energy In Electric Rail Traction Systems, Mahmoud Saleh, Oindrilla Dutta, Yusef Esa, Ahmed Mohamed

Publications and Research

This paper aims at determining the influential factors affecting regenerative braking energy in DC rail transit systems. This has been achieved by quantitatively evaluating the dependence of regenerative energy on various parameters, such as vehicle dynamics, train scheduling, ground inclination and efficiency of the electrical devices. The recuperated power and energy have been presented by a mathematical model, comprising of a set of empirical forms, which allows for thorough analysis. A detailed simulation model of a typical DC-traction system has been developed to validate the developed empirical forms. The results verified the validity of the proposed mathematical model, and demonstrated …

Optimal Microgrids Placement In Electric Distribution Systems Using Complex Network Framework, Mahmoud Saleh, Yusef Esa, Nwabueze Onuorah, Ahmed Mohamed

Publications and Research

This paper provides a new approach to find the optimal location for Microgrids (MGs) in electric distribution systems using complex network analysis. An optimal location in this paper refers to a location that would result in increased grid resilience, reduced power losses, less line loading, higher voltage stability and secured supply to critical loads during power outage. The criteria used to find the optimal placement of MGs were based on the centrality analysis adopted from complex network theory, the center of mass concept used in physics, and the controlled delivery grid (CDG) concept. An IEEE 30-bus system was used as …

The Influence Of The Electrode Dimension On The Detection Sensitivity Of Electric Cell–Substrate Impedance Sensing (Ecis) And Its Mathematical Modeling, Xudong Zhang, William Wang, Anis Nurashikin Nordin, Fang Li, Sunghoon Jang, Ioana Voiculescu

Publications and Research

Detection sensitivity is a crucial criterion in the design and application of ECIS sensors. The influence of sensing electrode dimension on detection sensitivity is investigated in this paper. Eight types of ECIS sensors were fabricated, and their experimental results reveal that smaller-radius working electrodes generate more sensitive impedance shift to cell density change. Also, the smaller radius of working electrodes yield higher impedance values, which improves signal-to-noise ratio. In a range from 1.0 mm to 3.5 mm, the distance between the working and counter electrodes does not affect impedance measurements. However, the distance should be large enough to prevent the …

Centralized Control For Dc Microgrid Using Finite State Machine, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed

Publications and Research

In this paper, an autonomous communication-based centralized control for DC microgrids (MG) has been developed and implemented. The proposed controller enables smooth transition between various operating modes. Finite state machine (FSM) has been used to mathematically describe the various operating modes (states), and events that may lead to mode changes (transitions). Therefore, the developed centralized controller aims at optimizing the performance of MG during all possible operational scenarios, while maintaining its reliability and stability. Results of selected cases have been presented. These results show stable transition between modes, verifying the validity and applicability of the proposed controller.

A Hybrid State/Event Driven Communication-Based Control For Dc Microgrids, Yusef Esa

Dissertations and Theses

The U.S. electric power industry is undergoing unprecedented changes triggered by the growing electricity demand, and the national efforts to reduce greenhouse gas emissions. Moreover, there is a call for increased power grid resiliency, survivability and self-healing capabilities. As a result of these challenges, the smart grid concept emerged. One of the main pillars of the smart grid is microgrids. In this thesis, the technical merits of clustering multiple microgrids during blackouts on the overall stability and supply availability have been investigated.

We propose to use the existing underground distribution grid infrastructure, if applicable, during blackouts to form microgrid clusters. …

Brief Study Of Classification Algorithms In Machine Learning, Ramesh Sankara Subbu

Dissertations and Theses

The purpose of this study is to briefly learn the theory and implementation of three most commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an implementation of a Machine Learning problem solved by each algorithm. KNN algorithm is designed using Euclidean distance measurement and Decision Trees make use of ID3 algorithm as a basis. We conclude the study by providing an overall picture of its strengths and weaknesses …

Understanding Adversarial Training: Improve Image Recognition Accuracy Of Convolution Neural Network, Naoki Ishibashi

Dissertations and Theses

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits. Recently many researchers work on Convolution Neural Network for image recognition, and get results as good as human being. Additionally, Image recognition task is getting more popular and high demand to apply to other fields, but also there are still many problems to utilize in everyday life. One of these problems is that several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that …

Property Analysis Of Silylamine Type Reversible Ionic Liquids For Use As A Thermal Safety Switch In Batteries, Showmik Podder

Dissertations and Theses

The increased capacity of the modern battery system has also brought about safety apprehensions. Uncontrollable runaway reactions are a big concern in these systems; these reactions are the result of in situ heat generation and very much increase the risk of explosions and device failures. The concept of this work is to provide a preliminary understanding into the use of a type of switchable solvent known as reversible ionic liquids (RevILs) and their feasibility in being used in electrolytes as a thermally-controlled reversible safety switch. In their pure forms these switchable solvents experience a dramatic change in their properties upon …

Data Driven Approach For Increasing Power Grid Situational Awareness And Mitigating Cascaded Failures, Yassine Mhandi

Dissertations and Theses

The main purpose of this thesis is to use Artificial Neural network as a tool to monitor system health and performance. In other word Using ANN can increase the system awareness and can be used as a tool to mitigate cascade failure in power grid due to loss of communication in a critical power node and as a result avoid catastrophic phenomena like electric blackout. In this thesis, a modified IEEE 30 bus system is used as a system under study. Modified IEE 30 bus system is IEEE 30 bus system in which 2 sets of its synchronous condensers changed …