Work Experience

Graduate Research Assistant

Networking and Analytics for CPS | Lab From: January 2018

Department of Electrical Engineering To: Present

University of South Florida


Graduate Teaching Assistant

EEL 3705 Fundamental of Digital Circuit From: January 2018

Department of Electrical Engineering To: May 2022

University of South Florida

Education

Ph.D. in Electrical Engineering

Networking and Analytics for CPS | Lab From: January 2018

Department of Electrical Engineering To: Present

University of South Florida PI: Dr. Mahshid Rahnamay Naeini


M.Sc. in Electrical Engineering

Networking and Analytics for CPS | Lab From: January 2018

Department of Electrical Engineering To: August 2020

University of South Florida PI: Dr. Mahshid Rahnamay Naeini


B.Sc. in Electrical & Electronic Engineering

Faculty of Engineering From: February 2011

Department of Electrical Engineering To: July 2015

Khulna University of Engineering and Technology PI: Prof. Dr. M. A. Samad

List of Accomplised Courses

Graduate Level Courses

Advanced Machine Learning and Data Analytics

  • EEL 6935: AI in Cyber-Physical Systems

  • EEL 6935: Advanced Data Analytics (II)

  • EEL 6935: Data Analytics for Electrical (I)

  • EEL 6935: Deep Learning

  • MAT 6932: Stochastic Modeling of Dynamic Systems

  • STA 6876: Time Series Analysis

Advanced Mathematics

  • EEL 6545: Random Process in Electrical Engineering

  • EEL 6935: Advanced Linear and Matrix Algebra

  • MAT 5932: Optimization

Power and Energy System Engineering

  • EEL 5250: Power System Analysis (III)

  • EEL 6936: Electrical Distribution System

  • EEL 6752: Digital Signal Processing (II)

  • EEL 6935: Sustainable Energy

Undergraduate Courses

Electrical and Electronics Engineering

  • EE 4217: Power Plant Engineering

  • EE 4203: Power System Switch-gear and Protection

  • EE 4233: High Voltage Engineering

  • EE 4109: Power Electronics and Industrial Drives

  • EE 4103, EE 3203: Power System Analysis (I,II)

  • EE 4235: Digital Signal Processing (I)

  • EE 4205, EE 4105, EE 3205: Communication Engineering (I,II,III)

  • EE 4121: VLSI Design

  • EE 4101: Control System Engineering

  • EE 3207, EE 3107, EE 2107: Electrical Machines (I,II,III)

  • EE 3109, EE 2209, EE 2109: Electronics (I,II,III)

  • EE 3213: Microprocessor, Microcontroller & Peripherals

  • EE 3113: Digital Electronics and Logic Design

Mathematics & Other Engineering Disciplinary Courses

  • EE 3121: Numerical Methods & Statistics

  • EE 3101: Engineering Materials

  • EE 2122, EE 1222: Programming Techniques (I,II)

  • CE 1104: Civil Engineering & Drawing

  • IEM 2103: Industrial Management


Training and Certificates





List of Projects

[1] Wide-Area Situational Awareness Using Data Analytic for Smart-grids Under Cyber and Physical Stresses.

Goal: Wide-area situational awareness (WASA) comprises automatic prevention, perception, and restorative actions of any kind of anomalies directed towards a smart grid. Thus, to ensure proper WASA it is customary to make available the status information of the grid to control center, such that detected anomalies immediately addressed. Since the smart-grid relies on deep integrated of cyber components which provide invaluable opportunities for a more secure and reliable operation of the smart grid. But also, the critical interdependency of power grids on the cyber components, modern power grids exhibit new vulnerabilities to cyber and physical attacks. For instance, the immense volume of energy data collected by various sensors, such as Phasor Measurement Units (PMUs), provide new opportunities for detecting, estimating and predicting various events in the system using data analytics techniques and machine learning. Understanding the nature of attacks and exploiting the vulnerabilities towards it is very important to design proper defense against ever-increasing cyber and physical stresses on the smart grid. The paper presented in this report intended to utilize this immense volume of PMU measurement data and processing and quizzing out potential information using various advanced data analytics techniques to design an efficient defense mechanism against cyber and physical stresses. Thus ensuring sufficient situational awareness for the smart grid.

[2] Overview of US Solar Energy Prospective and Challenges in Grid Integration

A sustainable energy future can be realized also preserving the environmental balance by generating electricity through renewable energy technologies. To meet the world's energy demand, renewable sources can be the fuel of choice because of the affordability, availability, and prevalent nature. Sustainable energy technologies have come a long way and ongoing research efforts to find new ways to make energy harvesting even more efficient and cost-effective. Among the various types of renewable energy sources, solar energy is one of the most viable sustainable sources. In this paper, the concentration is limited to solar energy resources, a summary of solar energy prospects in the US, and also the key challenges to integrate harvesting technologies to the grid along with potential solutions.

[3] State estimation in Cyber-physical Smart Grids Using Graph Convolution Network

Smart grids as dynamic cyber-physical systems need active monitoring and control for seamless operations. Wide area situational awareness in smart grids can enable protection, prevention, and restorative capabilities against unusual situations. Artificial intelligence with the blessing of big data analytics has the potential to make smart grids situation-aware. Graph neural networks are such an artificial intelligence technique that can handle complex network data better than traditional neural networks. The goal of this works is to evaluate the potential of such techniques addressing critical challenges such as state estimation. This work evaluates the potential of graph convolution-LSTM network addressing the centralized state estimation over IEEE 118 test case system.

[4] Robust Adaptive Filters applied to distorted power system signals for frequency estimation

In general, abnormal data obtained from measurements may cause noises and disturbances of power systems as well as affect the accuracy of frequency estimation. This kind of disturbance may lead to power system instability and blackouts. This is still an active area of research and many researchers have proposed several techniques to address this issue. The goal of this project is to explore those methods. Also to design an adaptive filter (e.x. Extended Kalman Filter) to estimate the frequency from the distorted power system signals. Synthetically generated PMU measurements of phase voltage and phase angle signal will be used for computer simulation.

[5] Stochastic Modeling of Cascading Failure Dynamics with Branching Process in Bulk Power Grids

Cascading failures in interdependent systems like smart-grid is a sequence of dependent failures of individual components that successively weakens the system and eventually causes wide spread blackouts or system failure. This interdependent failure of components is stochastic in nature and might well be explained mathematically using stochastic process models such as Branching Process. The goal of this project is to analyze simulated cascading failure data generated from random transmission line failure in IEEE 118 bus test case system and quantify the failure propagation of transmission lines in a cascade using Galton-Watson branching process. And eventually, predict the expected cascade size distribution from the branching process model.

[6] Data-driven Methods for Sensor-less Condition Monitoring of Electric Drive-train from Phase Current Measurements.

Multi-class classification from phase current measurements and detecting type of fault in the machine, accuracy 98.5% by SVM(Gaussian), Tools used: MATLAB Deep Learning Toolbox, Python.

[7] Implementation of RNN-LSTM for Electrical Load Forecasting.

Using NYISO load data short-term, medium-term, and long-term load forecasting by multivariate LSTM network, Accuracy 78%, Tools used: Python, MATLAB Deep Learning Toolbox.

List of Publications

International Journals

  1. Hossain MJ, Naeini M. Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters. Energies. 2022; 15(19):7105.

  2. Nakarmi, Upama; Rahnamay Naeini, Mahshid; Hossain, Md J.; Hasnat, Md A. "Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey" Energies 13, 2020.

  3. Hossain, Eklas, Feng Shi, Ramazan Bayindir, and Jakir Hossain. "Feasibility Analysis: Evaluating Sites for Possible Renewable Energy Options and Their Implications to Minimize the Cost." The International Journal of Electrical Engineering & Education, (June 2020).

  4. E. Hossain, J. Hossain and F. Un-Noor, "Utility Grid: Present Challenges and Their Potential Solutions," in IEEE Access, vol. 6, pp. 60294-60317, 2018.

  5. Jakir Hossain, N. Sakib, E. Hossain, R. Bayindir, "Modelling and Simulation of Solar Plant and Storage System: A Step to Microgrid Technology", International Journal of Renewable Energy Research (IJRER), Volume 7, Issue 2, Pages 723-737.

  6. E. Hossain, Jakir Hossain, N. Sakib, R. Bayindir, "Modelling and Simulation of Permanent Magnet Synchronous Generator Wind Turbine: A Step to Microgrid Technology", International Journal of Renewable Energy Research (IJRER), Volume 7, Issue 1, Pages 443-450.

  7. N. Sakib, Jakir Hossain, E. Hossain, R. Bayindir, "Modelling and Simulation of Natural Gas Generator and EV Charging Station: A Step to Microgrid Technology", International Journal of Renewable Energy Research (IJRER), Volume 7, Issue 1, Pages 399-410.

  8. J. Hossain , "A Novel keyless and key based Encryption Algorithm to handle Cyber Security in Microgrid Application", Gazi University Journal of Science, vol. 30, no. 4, pp. 330-343, Dec. 2017


International Conference Papers

  1. M. J. Hossain and M. Rahnamy-Naeini, "State Estimation in Smart Grids Using Temporal Graph Convolution Networks," North American Power Symposium (NAPS 2021), 2021, pp. 01-05, Texas, USA.

  2. M. J. Hossain and M. Rahnamy-Naeini, "Data-Driven, Multi-Region Distributed State Estimation for Smart Grids," IEEE PES Innovative Smart Grid Technologies, Europe 2021 (ISGT Europe), 2021, pp. 1-6 Espoo, Finland (Accepted).

  3. N. Hudson, M. J. Hossain, M. Hosseinzadeh, H. Khamfroush, M. Rahnamay-Naeini, and N. Ghani, "A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning," 2021 International Conference on Computer Communications and Networks (ICCCN), 2021, pp. 1-9.

  4. M. J. Hossain and M. Rahnamy-Naeini, "Line Failure Detection from PMU Data after a Joint Cyber-Physical Attack," 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019, pp. 1-5.

  5. M. A. Hasnat, M. J. Hossain, A. Adeniran, M. Rahnamay-Naeini, and H. Khamfroush, "Situational Awareness Using Edge-Computing Enabled Internet of Things for Smart Grids," 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA, 2019, pp. 1-6.

  6. J. Hossain, S. S. Sikander, and E. Hossain, "A wave-to-wire model of ocean wave energy conversion system using MATLAB/Simulink platform," 2016 4th International Conference on the Development in the in Renewable Energy Technology (ICDRET), Dhaka, 2016, pp. 1-6.

  7. N. Sakib, J. Hossain, H. I. Bulbul, E. Hossain, and R. Bayindir, "Implementation of unit commitment algorithm: A comprehensive droop control technique to retain microgrid stability," 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, 2016, pp. 1074-1079.

  8. I. Colak, E. Hossain, R. Bayindir and J. Hossain, "Design a grid-tie inverter for PMSG wind turbine using FPGA & DSP builder," 2016 IEEE International Power Electronics and Motion Control Conference (PEMC), Varna, 2016, pp. 372-377.