arXiv subjects

Thai-Thanh Nguyen

Publications and source records attributed to Thai-Thanh Nguyen.

8 recordsLinked to original sources

arXiv2024

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

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.

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arXiv2022

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined recurrent graph convolutional neural networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%, 98.75%, and 95.6% for fault detection, fault type classification, fault phase identification, and fault location respectively.

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arXiv2022

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

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build a new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

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arXiv2022

Integrated Multiport Back-to-Back Power Converter for Type-4 Wind Turbine Generator with Hybrid Energy Storage System

This paper proposes a novel integrated multiport bidirectional back-to-back power converter for a type-4 wind turbine that accommodates a battery and supercapacitor for energy storage. The circuit topology reduces 4 switches compared to the traditional configuration. Moreover, owing to the dual-buck structure embedded in the phase leg, the circuitry has no short-circuit path, therefore it withstands short-circuited events for a much longer time than the normal phase-leg and prevents the reverse current in turn-off recovery. The use of a hybrid energy storage system with battery and supercapacitor helps smooth out the power output under wind gusts and stabilizes the DC-link voltage under grid fault conditions. The case studies are carried out with a 1.5 MW wind turbine system. Simulation results are provided for the theoretical validation.

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arXiv2022

Integrated Multiport Bidirectional DC-DC Converter for HEV/FCV Applications

This paper proposes a novel integrated multiport bidirectional dc-dc converter to interface the battery, the ultra-capacitor, the fuel cell, or other energy sources with the dc-link capacitor of the hybrid energy systems such as the hybrid electric vehicle (HEV) and fuel cell vehicle (FCV) applications. The proposed converter can be applied to the distributed generation systems which include local energy sources, storage, and loads. It can perform both buck and boost functions with fewer switches. In addition, it is extendable when more inputs and/or outputs are required. The operating principle and control strategy of the proposed converter will be analyzed in detail. For verification, simulation, and experimental results of the four utilized operating modes of an HEV/FCV are provided.

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arXiv2022

Grid-Forming Inverter-based Wind Turbine Generators: Comprehensive Review, Comparative Analysis, and Recommendations

High penetration of wind power with conventional grid following controls for inverter-based wind turbine generators (WTGs) weakens the power grid, challenging the power system stability. Grid-forming (GFM) controls are emerging technologies that can address such stability issues. Numerous methodologies of GFM inverters have been developed in the literature; however, their applications for WTGs have not been thoroughly explored. As WTGs need to incorporate multiple control functions to operate reliably in different operational regions, the GFM control should be appropriately developed for the WTGs. This paper presents a review of GFM controls for WTGs, which covers the latest developments in GFM controls and includes multi-loop and single-loop GFM, virtual synchronous machine-based GFM, and virtual inertia control-based GFM. A comparison study for these GFM-based WTGs regarding normal and abnormal operating conditions together with black-start capability is then performed. The control parameters of these GFM types are properly designed and optimized to enable a fair comparison. In addition, the challenges of applying these GFM controls to wind turbines are discussed, which include the impact of DC-link voltage control strategy and the current saturation algorithm on the GFM control performance, black-start capability, and autonomous operation capability. Finally, recommendations and future developments of GFM-based wind turbines to increase the power system reliability are presented.

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arXiv2022

Real-time Transient Simulation and Studies of Offshore Wind Turbines

This paper presents developed real-time simulation models for offshore wind turbine generators in compliance with industry standards. The critical control functions such as negative sequence injection, sequence current limit, voltage ride through, and power curtailments are designed to meet the industry requirements for future electromagnetic transient (EMT) testing and controls of offshore wind farms. Average-value and switching detailed models are developed in the Opal-RT real-time simulator. Real-time capabilities of these models are compared to show the effectiveness of the average-value model in terms of accuracy and computation efficiency. Studies of balanced and unbalanced faults illustrate the ability of the proposed turbine models to inject active and reactive currents during fault events. The models are validated against the second-generation generic wind turbine model proposed by Western Electricity Coordinating Council (WECC). Validation results reveal that the proposed models are aligned with the WECC generic model. In addition, the models provide an extended capability in mitigating the active power oscillation during unbalanced fault conditions.

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arXiv2021

Energy Management System for Resilience-Oriented Operation of Ship Power Systems

This paper proposes an original energy management methodology for enhancing the resilience of ship power systems considering multiple types of energy storage systems, including battery energy storage systems (BESS) and supercapacitor energy storage systems (SCESS). The primary function of the proposed EMS is to maximize the load operability while taking ramp-rate characteristics of energy storage systems (ESS) and generators into account innovatively. Balancing state-of-charge (SoC) of BESS and prioritizing the SoC level of SCESS are two additional objectives of the proposed EMS to manage energy storage systems. The receding horizon optimization (RHO) technique is proposed to reduce the computational burden, making the proposed method feasible for real-time applications. An all-electric MVDC ship power system is used to evaluate the performance of the proposed methodology. Simulation studies and results demonstrate the effectiveness of the proposed method in managing the ESS to ensure the system resilience under generation power shortage. In addition, the proposed RHO technique significantly reduces the computation burden seen in the FHO technique while maintaining an acceptable resilience performance.

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