Ngamroo, Issarachai
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Preferred name
Ngamroo, Issarachai
Alternative Name
Ngamroo, I.
Main Affiliation
Email
issarachai.ng@kmitl.ac.th
14 results
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Item type:Publication, Forced Oscillation Detection Amid Communication Uncertainties(2021-09-01); - 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, Control of Distributed Converter-Based Resources in a Zero-Inertia Microgrid Using Robust Deep Learning Neural Network(2024-01-01); Considering the evolution of future microgrids (MGs) towards zero-inertia level due to the penetrations of distributed converter-based resources (DCRs), a large number of data produced by these generations will lead the control decisions to be more complicated than conventional power systems. This paper presents a control strategy for a zero-inertia MG with DCRs using a robust deep learning neural network (RDeNN). In a training phase, a sub-space state-based identification method is employed to monitor and analyze the data regarding stability indices, i.e., damping and frequency of dominant modes, and robustness against uncertainties. In addition, a mixed H2/H∞ control strategy is applied to enhance the training efficacy in the frequency and voltage control loops of DCRs. The trained RDeNN is activated to make quick and effective control decisions by using only measured signals from the MG. Simulation results are verified in the zero-inertia MG (or the grid with 100% DCRs) and compared with several existing control techniques. The study results demonstrate the advantages of the proposed RDeNN in many aspects such as low computational time, require-less physical controller models, fast and flexible stabilizing responses, and high robustness against various time delays, data quality issues, and MG uncertainties. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forced oscillation suppression using extended virtual synchronous generator in a low-Inertia microgrid(2023-09-01); Effects of forced oscillations (FOs) in well-damped power systems are relatively smaller than those of microgrids (MGs) in which the severity of the FOs may be intensified by converter interfaced generators (CIGs). According to the distinct system characteristics, the FOs in MGs will be challenging problems in future research topics. Without proper controls of the CIGs, the FOs may be exhibited extremely higher amplitude, resulting in the MG instability. Such influences will be exacerbated in a low-inertia MG, which can trigger critical frequency oscillation and system collapse. This paper introduces an extended virtual synchronous generator (VSG) with virtual forced components to attenuate the dynamic FO effects in the presence of a low-inertia MG. Contrastive scenarios, i.e., periodic, combined, full sine, and high-frequency FOs are conducted to validate the performances of VSG control in both stand-alone and interconnected MG environments. Numerical results verify that the extended VSG control provides promising benefits in a low-inertia MG not only for the sake of MG stability improvement but also the further FO suppression. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Communication-Driven Learning-Based Harmonic Mitigation Approach for Grid-Forming Converters(2025-01-01); This paper proposes a harmonic mitigation framework for multi-bus microgrids (MGs) that utilizes a communication-driven learning-based approach. A dedicated neural network is used to model additional intelligent control loops. We also develop a global harmonic distortion (GHD) matrix, formulated by the total harmonic distortion at each point of common coupling (PCC). The GHD specifically focuses on harmonic orders that exceed acceptable limits observed from multi-bus systems. The GHD is used as an input signal of the neural network. Based on the system conditions, the developed GHD allows the system to intelligently trigger the control signal whenever dominant harmonic orders are detected and need mitigation. Other input signals include the voltage and current vectors from all PCCs, which contain local harmonics. To train the network, various critical system operating points are collected as time-series data. At this stage, system parameters are not needed; however, secure communication for data transfer is considered instead. Consequently, the collected data are used to train the network using the developed loss functions that focus on harmonic mitigation. After network training, the output signal is sent to the summing points in the direct axis of the inner voltage and current control loops of different models of grid-forming IBRs units. Simulation results are verified in a modified multi-bus MG with IBRs under various operating conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Embedded Intelligent Reactive Power Control for Distributed Controllable Loads to Support Grid Voltage Considering Islanding Conditions(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Resiliency-Guided Grid-Forming Converter Control of Distributed Solar-Powered Electric Vehicles(2024-01-01); This paper proposes an intelligent control framework designed for distributed solar-powered electric vehicles (DSPEVs) operating within weak microgrids (MGs) that are experiencing a substantial influx of distributed converter-interfaced resources. Main contributions include developing and analyzing a new DSPEV model, which incorporates distributed electric vehicles and a solar-powered charging station with a battery. In islanding mode, DSPEV converters are modelled as grid-forming (GFM) converters, which enhances MG stability while also charging/discharging distributed electric vehicles. The resiliency-guided physics-informed neural network (named RPiNN) is applied for GFM converters, which considers the dynamics of low-inertia MGs and incorporates the physics laws governing DSPEV and MG behaviors. Furthermore, a novel resiliency-guided control framework that coordinates DSPEVs with GFM converters and the RPiNN is proposed. This framework addresses unexpected islanding scenarios and ensures both grid synchronization and re-synchronization, while maintaining acceptable voltage and frequency profiles for various MG operations. The proposed framework is validated through simulation results in islanding and grid-connected scenarios within a weak MG. Results shown that the proposed RPiNN effectively stabilizes islanding scenarios by reducing voltage and frequency fluctuations, demonstrating resilience and robustness in various operating conditions, and facilitating smooth grid synchronization.