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1. Quickest Detection of Series Arc Faults on DC Microgrids

   https://doi.org/10.1109/ECCE47101.2021.9595315  

   Gajula, Kaushik; Le, Vu; Yao, Xiu; Zou, Shaofeng; Herrera, Luis (October 2021, IEEE Energy Conversion Congress and Expo (ECCE))

   In this paper we explore the problem of series arc fault detection and localization on dc microgrids. Through a statistical model of the microgrid obtained by nodal equation, the injection currents are modeled as a random vector whose distribution depends on the nodal voltages and the admittance matrix. A series arc fault causes a change in the admittance matrix, which further leads to a change in the data generating distribution of injection currents. The goal is to detect and localize faults on different lines in a timely fashion subject to false alarm constraints. The model is formulated as a quickest change detection problem, and the classical Cumulative Sum algorithm (CUSUM) is employed. The proposed framework is tested on a dc microgrid with active (constant power) loads. Furthermore, a case considering fault detection in the presence of an internal node is presented. Finally, we present an experimental result on a four node dc microgrid to verify the practical application of our approach. 

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2. Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

   https://doi.org/10.1109/ECCE53617.2023.10362739  

   Bohara, Bharat; Krishnamoorthy, Harish S. (October 2023, IEEE)

   DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system. 

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3. Accurate ZVS Analysis of a Full-Bridge T-Type Resonant Converter for a 20-kW Unfolding-Based AC-DC Topology

   https://doi.org/10.1109/OJPEL.2024.3400256  

   GURUDIWAN, SHUBHANGI; Zade, Aditya; Wang, Hongjie; Zane, Regan (January 2024, IEEE Open Journal of Power Electronics)

   Unfolding-based single-stage ac-dc converters offer benefits in terms of efficiency and power density due to the low-frequency operation of the Unfolder, resulting in negligible switching losses. However, the operation of the Unfolder results in time-varying dc voltages at the input of the subsequent dc-dc converter, complicating its soft-switching analysis. The complication is further enhanced due to the nonlinear nature of the output capacitance ( Coss ) of MOSFETs employed in the dc-dc converter. Furthermore, unlike two-stage topologies with a constant dc-link voltage, as seen in high-frequency grid-tied converters, grid voltage fluctuations also impact the dc input voltages of the dc-dc converter in unfolding-based systems. This work comprehensively analyzes the soft-switching phenomenon in the T-type primary bridge-based dc-dc converter used in unfolding-based topologies, considering all the aforementioned challenges. An energy-based methodology is proposed to determine the minimum zero-voltage switching (ZVS) current and ZVS time during various switching transitions of the T-type bridge. It is shown that the existing literature on the ZVS analysis of the T-type bridge-based resonant dc-dc converter, relying solely on capacitive energy considerations, substantially underestimates the required ZVS current values, with errors reaching up to 50%. The proposed analysis is verified through both simulation and hardware testing. The hardware testing is conducted on a 20-kW 3- ϕ unfolding-based ac-dc converter designed for high-power electric vehicle battery charging applications. The ZVS analysis is verified at various grid angles with the proposed analysis ensuring a complete ZVS operation of the ac-dc system throughout the grid cycle. 

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4. A Method for Open-Circuit Faults Detecting, Identifying, and Isolating in Cascaded H-Bridge Multilevel Inverters

   https://doi.org/10.1109/PEDG.2018.8447855  

   Mhiesan, Haider; Umuhozs, Janviere; Mordi, Kenneth; McCann, Roy; Balda, Juan C.; Farnell, Chris; Mantooth, Alan (June 2018, 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG))

   The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded H-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch’s state. By monitoring each cell’s output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method. 

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5. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles

   https://doi.org/10.1109/ASPDAC.2018.8297305  

   Zhao, Pu; Wang, Yanzhi; Chang, Naehyuck; Zhu, Qi; Lin, Xue (January 2018, 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC))

   Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy. 

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