A Novel Fractional order controller based on Fuzzy Logic for Regulating the Frequency of an Islanded Microgrid

Oshnoei, Soroush; Aghamohammadi, Mohammad Reza; Oshnoei, Siavash

doi:10.1109/psc49016.2019.9081567

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…Microgrids (MGs) are a group of interconnected loads and distributed generations (DGs), and they are usually interfaced to the grid through power converters to reduce pollution and power transmission losses with the flexibility of different installation location. This is an important concept for future distribution systems and will be more utilized in renewable energy integration that is the fastest-growing energy source globally [1,2]. The MGs can be utilized in both islanded and grid-connected operation modes [3].…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2022

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This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics.

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Section: Introductionmentioning

confidence: 99%

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2022

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“…In recent years, the grid-following (GFL) control is widely adopted in controlling inverters of ship microgrid. Due to the mismatched voltage amplitude, phase, and frequency of the ship microgrid with respect to the grid, there may exist significant impulse current once the inverters connect to the gird (Oshnoei et al, 2019(Oshnoei et al, , 2021. To avoid the impulse current, the phase information of the point of common coupling (PCC) is detected by utilizing the phase-locked loop (PLL) to synchronize with the main grid (Arafat et al, 2015;Ochs et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Presynchronization control for ship microgrid of merchant marine inverters based on VSG algorithm with MFAC

Yao,

Pei,

Chi

et al. 2024

Transactions of the Institute of Measurement and Control

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The paper studies a presynchronization control of grid connection for large merchant marine microgrid inverters. We present a virtual synchronous generator (VSG) algorithm with model-free adaptive control (MFAC) to optimize the stable grid connection of ship microgrid and shore-to-ship power. To solve poor precision of presynchronization control under nonideal ship microgrid condition, an MFAC controller and its presynchronization method are developed for grid connection of ship-distributed generation inverters. The proposed presynchronization control method effectively avoids a high transient overcurrent and achieves a seamless grid connection to different types of shore power. The simulation results verify the effectiveness of the proposed control method.

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“…Traditional generators' frequency modulation capabilities are becoming inadequate as RESs become more integrated into power systems [8,9]. In addition, RESs [10] and energy storage systems (ESSs) [11] are widely used in MGs and power systems to decrease energy consumption and enhance energy utilization efficiency. Although the presence of RESs in electricity grids significantly reduces the concerns related to environmental problems and the lack of fossil fuels, the intermittent nature of these sources affects the stability of grids [12,13].…”

Section: Introductionmentioning

confidence: 99%

Microgrid Frequency Regulation Based on a Fractional Order Cascade Controller

et al. 2023

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Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

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A Novel Fractional order controller based on Fuzzy Logic for Regulating the Frequency of an Islanded Microgrid

Cited by 11 publications

References 16 publications

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

Presynchronization control for ship microgrid of merchant marine inverters based on VSG algorithm with MFAC

Microgrid Frequency Regulation Based on a Fractional Order Cascade Controller

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