Surinkaew, Tossaporn
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Surinkaew, Tossaporn
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tossaporn.su@kmitl.ac.th
26 results
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Item type:Publication, A Wildfire Risk Impact Index (WRII) for Power Distribution Systems: Integrating GIS and AHP(2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Informatics-Centric Neural Network for Distributed Energy Resources Against Diverse Cyber Threats(2024-01-01); Kerdphol, ThongchartThis article addresses challenges in modernizing microgrids (MGs) with distributed energy resources (DERs), which emphasizes cybersecurity vulnerabilities causing from integrating high DERs with cyber-physical data. To ensure seamless integration of DERs and to achieve optimal control performance, this article introduces an informatics-centric neural network (named I-ANN), which is specifically designed for DERs in weak MGs to encounter cyber threats, such as communication latency, false data injection, denial of service, and controller hijacking. The proposed framework utilizes multiagent systems to model the risks posed by cyber threats, with a particular emphasis on their impacts on frequency and voltage regulations. Here, the proposed I-ANN features a novel loss function for automatic signal restoration, and the I-ANN is iteratively trained using various cyber threat scenarios. During critical MG operating scenarios, the new loss function is proposed to enhance robustness and damping while simultaneously mitigating rapid fluctuations in voltage and frequency. Moreover, a significant departure from typical voltage and frequency control loops is the complete replacement of conventional PI controllers with the proposed I-ANN. This strategy fortifies resilience without requiring any additional controllers or control loops. Comparative analyses demonstrate I-ANN's effectiveness in low-inertia MGs with DERs through probabilistic small-signal stability analysis and time-domain simulations under diverse operating conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forced Oscillation Detection Amid Communication Uncertainties(2021-09-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small-Signal Stability Analysis in an Uncertain Microgrid with Distributed Energy Resources: A Data-Driven Monitoring(2024-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced robust frequency stabilization of a microgrid against simultaneous cyber-attacks(2024-03-01); A microgrid (MG) is a smart grid cyber-physical system, with component coordination relying on cyber resilience. Weak communications, protocols, and tools make the MG's secondary frequency control vulnerable to various cyber-attacks, posing new challenges and stability risks. In response to this challenge, this paper introduces the enhanced robust H<inf>∞</inf> technique considering the dynamic impacts of cyber-attacks on secondary frequency control to develop a secondary frequency control loop, improving the regulation performance and cyber resiliency of the MG frequency. The secondary control cyber-attack strategies mainly rely on false data injection (FDI), denial of service (DoS), and controller hijacking. These attack techniques are simultaneously considered in formulating the H∞ problem and control synthesis as unstructured parametric uncertainty, attenuating the concurrent cyber impacts. The study extends a load frequency control model to illustrate how cyber-attacks can be represented mathematically and physically in the MG. The results reveal that cyber-attacks affect secondary frequency control elements differently depending on the type of cyber threats used. By implementing an enhanced H∞ controller, the MG can effectively maintain stable frequency levels even when faced with malicious attacks and disruptions caused by renewable energy sources and loads. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small-Signal Stability Enhancement Through Integration of Distributed Grid-Forming Loads Considering Multi-Agent Collaboration(2025-01-01); ; Mitani, YasunoriThe integration of distributed controllable loads in future islanding microgrids (MGs) is growing. This creates new opportunities to actively shape grid frequency and voltage. As a result, it leads to the development of distributed grid-forming loads (DGFM-Ls). Simultaneously, it is equally crucial to ensure robustness, particularly in preserving small-signal stability amid the multi-agent collaboration. This paper presents a strategy for the small-signal stability enhancement in islanding MGs with DGFM-Ls. The small-signal models of the MG with DGFM-Ls are mathematically developed and analyzed. The multi-agent cooperation is modelled to improve the small-signal stability of the MG with DGFM-Ls. Additionally, uncertainties from multi-agent cooperation, such as partial or complete lack of measured signal observability, are considered. Data quality issues are also taken into account under various conditions. Simulation results are conducted in a MG with a significant penetration of inverter-based resources under various MG operating points and conditions such as topology changes and unavailability of certain agents. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Synthetic Inertia-Power Sharing in High Renewable Power Grids Through Vehicle-to-Grid Topology(2024-01-01); With the increasing integration of renewable energy sources (RESs), the overall inertia of the power system is expected to decline. The remaining inertia is crucial for regulating system frequency and mitigating excessive rates of change. The deployment of dispatchable loads, such as electric vehicles (EVs), offers a promising solution. This paper presents a synchronized inertia support framework utilizing a vehicle-to-grid (V2G) system through its bidirectional chargers. This concept is realized by integrating a large-scale energy storage system (ESS) composed of controllable EVs into an enhanced inertia emulation structure. The synthetic inertia control strategy has been refined to account for EV user convenience and synchronized state of charge (SOC) management, facilitating synchronized inertia power sharing. This approach enhances the grid's dynamic performance and resilience. Simulation results demonstrate that the proposed method effectively delivers rapid inertia support from the onboard ESS of EVs, improving frequency stability. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Novel Control Design for Simultaneous Damping of Inter-Area and Forced Oscillation(2021-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages(2024-09-01); Hredzak, BranislavThe paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy management systems which employ reinforcement learning-based methods to integrate energy storages. In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects of integrating energy storages into smart homes. Second, an overview of a smart home system and a theoretical background of reinforcement learning-based algorithms are given and discussed. Consequently, this study delves into the challenges and benefits of integrating energy storage, specifically looking at ways to lessen the impact of renewable sources’ intermittency, improve grid stability, and streamline efficient energy storage management. Thirdly, the paper highlights the beneficial features of smart home energy storage integration, including reduced costs, increased system resilience, and improved energy efficiency. Therefore, cutting-edge reinforcement learning-based methods utilized in smart home energy management systems that incorporate energy storage are thoroughly examined by evaluating their effectiveness and adaptability, taking into account both multi-agent and single-agent reinforcement learning-based methods. Finally, the study identifies potential research directions, including the development of hybrid reinforcement learning algorithms, integration of demand-side management strategies, and addressing privacy and security concerns in reinforcement learning-based smart home energy management systems. While some research has made use of single-agent reinforcement learning, smart home energy storage systems that use energy storages seldom use multi-agent reinforcement learning techniques. Researchers, practitioners, and policymakers will be able to use this work as a foundation to build smart, sustainable home energy systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Adaptive Data-Driven-Based Control for Voltage Control Loop of Grid-Forming Converters in Variable Inertia MGs(2024-01-01); Marungsri, BoonruangIn the transition towards sustainable energy systems, microgrid (MG) plays a pivotal role, especially in the context of variable-inertia MGs that integrate renewable energy sources (RESs) and distributed energy resources (DERs). Maintaining stable voltage control within such grids is imperative for reliable operation. This paper presents an adaptive data-driven control technique for the voltage control loop of grid-forming converters in variable-inertia MGs. The primary objective is to enhance control performance while accommodating the unpredictable nature of renewable energy sources. The approach utilizes advanced data-driven algorithms to continuously monitor and adjust control parameters based on real-time grid conditions. This adaptability allows for effective management of varying inertia and load demand, ensuring optimal grid performance. The data-driven nature of the approach enables self-adaptability, making it suitable for the dynamic MG environment. This new approach is a noteworthy advancement in controlling RESs and DERs to maintain stable voltage in MGs, without needing precise knowledge of the MG's parameters. Simulation tests and real-world examples confirm that the adaptive data-driven control method effectively optimizes voltage control in MGs with variable inertia, especially those with a high presence of RESs and DERs.