Publications

2024

1. Real-time hybrid controls of energy storage and load shedding for integrated power and energy systems of ships

   Linh Vu , Thai-Thanh Nguyen , Bang Le-Huy Nguyen , and 2 more authors

   Electric Power Systems Research, 2024

   Abs

   This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS’s SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. A comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance.

2. Physics-informed graphical neural network for power system state estimation

   Quang-Ha Ngo , Bang L.H. Nguyen , Tuyen V. Vu , and 2 more authors

   Applied Energy, 2024

   Abs

   State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods.

2023

1. Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

   Bang L. H. Nguyen , Tuyen V. Vu , Thai-Thanh Nguyen , and 2 more authors

   IEEE Access, 2023
2. Synopsis of Pulse Modulator for Beam Chopper in LANSCE Particle Accelerator

   Bang L.H. Nguyen , Henry John Gaus , Gregory E. Dale , and 1 more author

   In 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON) , 2023
3. State Estimation for Power Distribution System Using Graph Neural Networks

   Quang-Ha Ngo , Bang L. H. Nguyen , Tuyen V. Vu , and 1 more author

   In 2023 IEEE Electric Ship Technologies Symposium (ESTS) , 2023

2022

1. A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system

   Bui Ngoc-Thang , Thi My Tien Nguyen , Trong Toai Truong , and 2 more authors

   Biosensors and Bioelectronics: X, 2022

   Abs

   Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR), saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper proposes a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is significantly improved and power consumption on wireless module is minimized. In the proposed architecture, parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. Moreover, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For validation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA and minimize the power consumption on wireless transfer module of wearable PPG devices.

2. Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems

   Ngoc-Thang Bui , Thi My Tien Nguyen , Bang Le-Huy Nguyen , and 6 more authors

   Measurement Science and Technology, Mar 2022

   Abs

   Optical time-of-flight sensors have potential in the revolution of distance measurement. These sensors can continuously monitor the distance and track the movement of objects. However, the existing sensing methods for such distance optical sensors mainly calculate the flight time, e.g. pulse transmission and receiving time, without considering the environmental effects. Therefore, the measurement accuracy is severely reduced. There are other technologies with higher accuracy in distance measurement. Nonetheless, they are too expensive due to the high accurate power supply. In this paper, we innovatively improve the accuracy of continuous distance measurement using the artificial neural network (ANN) technique. The proposed method can be applied for very cheap optical distance sensors with analog output in a real-time system. Moreover, the proposed method can self-calibrate and be miniaturized for cheap analog sensor applications. The prototype is built with the infrared sensor GP2Y0A02YK0F and an Arduino control board (ESP32_DevC), and the ANN is implemented using the deep learning algorithm. The test results show that the distance measurement accuracy is significantly improved and the measuring range is increased from 15 to 150 cm. In addition, we calculate mean squared error, mean absolute error, mean bias error, and R 2 for further performance evaluation. The experimental results have proven the superiority of the proposed ANN method in optical distance measurement. The proposed method can be applied to many types of sensors.

3. Resilience-Oriented Energy Management System for Ship Power Systems

   Thai–Thanh Nguyen , Bang Le–Huy Nguyen , and Tuyen Vu

   In 2022 IEEE Power & Energy Society General Meeting (PESGM) , Mar 2022
4. Hierarchical Control of Grid-Connected Hydrogen Electrolyzer Providing Grid Services

   Bang L.H. Nguyen , Mayank Panwar , Rob Hovsapian , and 2 more authors

   In 2022 IEEE 1st Industrial Electronics Society Annual On-Line Conference (ONCON) , Mar 2022
5. 1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

   Bang L.H. Nguyen , Tuyen Vu , Thai-Thanh Nguyen , and 2 more authors

   In 2022 IEEE 1st Industrial Electronics Society Annual On-Line Conference (ONCON) , Mar 2022
6. Integrated Multiport Back-to-Back Power Converter for Type-4 Wind Turbine Generator with Hybrid Energy Storage System

   Bang Le-Huy Nguyen , Thai-Thanh Nguyen , Van-Long Pham , and 3 more authors

   In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society , Mar 2022

2021

1. Power Converter Topologies for Electrolyzer Applications to Enable Electric Grid Services

   Bang L. H. Nguyen , Mayank Panwar , Rob Hovsapian , and 2 more authors

   In IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society , Mar 2021
2. Distributed dynamic state-input estimation for power networks of Microgrids and active distribution systems with unknown inputs

   Bang L.H. Nguyen , Tuyen V. Vu , Joseph M. Guerrero , and 3 more authors

   Electric Power Systems Research, Mar 2021

   Abs

   This paper proposes a joint input and state dynamic estimation scheme for power networks in microgrids and active distribution systems with unknown inputs. The conventional dynamic state estimation of power networks in the transmission system relies on the forecasting methods to obtain the state-transition model of state variables. However, under highly dynamic conditions in the operation of microgrids and active distribution networks, this approach may become ineffective as the forecasting accuracy is not guaranteed. To overcome such drawbacks, this paper employs the power networks model derived from the physical equations of branch currents. Specifically, the power network model is a linear state-space model, in which the state vector consists of branch currents, and the input vector consists of bus voltages. To estimate both state and input variables, we propose linear Kalman-based dynamic filtering algorithms in batch-mode regression form, considering the cross-correlation between states and inputs. For the scalability of the proposed scheme, the distributed implementation is also presented. Complementarily, the predicted state and input vectors are leveraged for bad data detection. Results carried out on a 13-bus microgrid system in real-time Opal-RT platform demonstrate the effectiveness of the proposed method in comparison with the traditional weighted least square and tracking state estimation methods.

3. Advanced Load Shedding for Integrated Power and Energy Systems

   Bang L. H. Nguyen , Tuyen Vu , Colin Ogilvie , and 7 more authors

   In 2021 IEEE Electric Ship Technologies Symposium (ESTS) , Mar 2021
4. Integrated Multiport Bidirectional DC-DC Converter for HEV/FCV Applications

   Bang Le-Huy Nguyen , Honnyong Cha , Tuyen Vu , and 1 more author

   In IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society , Mar 2021

2020

1. Cyber-Physical Microgrids: Toward Future Resilient Communities

   Tuyen V. Vu , Bang L.H. Nguyen , Zheyuan Cheng , and 2 more authors

   IEEE Industrial Electronics Magazine, Mar 2020
2. A Novel Single-Phase Three-Level Dual-Buck Inverter

   Tien-The Nguyen , Honnyong Cha , Bang Le-Huy Nguyen , and 1 more author

   IEEE Transactions on Power Electronics, Mar 2020
3. Distributed Dynamic State Estimation for Microgrids

   Bang L. H. Nguyen , Tuyen V. Vu , Thomas H. Ortmeyer , and 1 more author

   In 2020 IEEE Power & Energy Society General Meeting (PESGM) , Mar 2020
4. Decoupling Proportional-Resonant Controllers for Positive and Negative Sequences under Unbalanced Voltages and Frequency Variations

   Linh Vu , Tuan Ngo , Bang LH Nguyen , and 1 more author

   In 2020 IEEE Power & Energy Society General Meeting (PESGM) , Mar 2020

2019

1. Distributed Optimal Dynamic State Estimation for Cyber Intrusion Detection in Networked DC Microgrids

   Tuyen V. Vu , Bang H. L. Nguyen , Tuan A. Ngo , and 3 more authors

   In IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society , Mar 2019
2. Decentralized Dynamic State Estimation in Microgrids

   L. H. Bang Nguyen , Tuyen V. Vu , and Tuan A. Ngo

   In 2019 IEEE Electric Ship Technologies Symposium (ESTS) , Mar 2019

2018

1. Single-Phase Six-Switch Dual-Output Inverter Using Dual-Buck Structure

   Bang Le-Huy Nguyen , Honnyong Cha , and Heung-Geun Kim

   IEEE Transactions on Power Electronics, Mar 2018
2. Family of Integrated Multi-Input Multi-Output DC-DC Power Converters

   Bang Le-Huy Nguyen , Honnyong Cha , Tien-The Nguyen , and 1 more author

   In 2018 International Power Electronics Conference (IPEC-Niigata 2018 -ECCE Asia) , Mar 2018
3. Two-Channel Interleaved Buck LED Driver Using Current-Balancing Capacitor

   Duc-Tuan Do , Honnyong Cha , Bang Le-Huy Nguyen , and 1 more author

   IEEE Journal of Emerging and Selected Topics in Power Electronics, Mar 2018
4. Novel T-type Dual-Buck Inverter with Minimum Number of Inductors

   Tien-The Nguyen , Honnyong Cha , Bang Le-Huy Nguyen , and 1 more author

   In 2018 International Power Electronics Conference (IPEC-Niigata 2018 -ECCE Asia) , Mar 2018

2016

1. A novel battery formation equipment using two-stage differential buck converter

   Jongwoo Bae , Bang L.H Nguyen , and Honnyoung Cha

   In 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific) , Mar 2016

2014

1. A phase shifted PWM technique for common-mode voltage reduction in five level H-bridge cascaded inverter

   N. L. H. Bang , N. V. Nho , N. K. T. Tam , and 1 more author

   In 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) , Mar 2014
2. Simulation and experiment of hybrid modulation strategy with common-mode voltage reduction for seven-level hybrid cascaded inverter

   N. L. H. Bang , N. V. Nho , N. K. T. Tam , and 1 more author

   In 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) , Mar 2014