A Review of Various Neural Network Algorithms for Operation of AC Microgrids

Nasir, M.; Bansal, Ramesh C.; Elnady, A.

doi:10.1109/aset53988.2022.9734899

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…Depending on the availability of primary resources for DGs, the MG may import or export power to/from the main grid in this mode. Among various architectures, the AC MG is considered the dominant type, in which most DGs connect to the distribution system through a power electronics interface to supply part or all of the user's demand [6,24]. Additionally, the DGs can also be connected through transformers.…”

Section: Ac Microgrids and Principal Componentsmentioning

confidence: 99%

Development of Control Techniques for AC Microgrids: A Critical Assessment

Cárdenas,

Martínez,

Molina

et al. 2023

Sustainability

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This article aims to provide a comprehensive review of control strategies for AC microgrids (MG) and presents a confidently designed hierarchical control approach divided into different levels. These levels are specifically designed to perform functions based on the MG’s mode of operation, such as grid-connected or islanded mode. The primary control ensures frequency (f) and voltage (V) stability, whereas the secondary control adjusts their values to their references and the tertiary control efficiently manages the power of distributed generators (DGs) in a cost-effective manner. The article extensively discusses centralized, decentralized, and distributed strategies for each control level, highlighting their differences, advantages, disadvantages, and areas of application. Finally, the usefulness of different control strategies at different levels is demonstrated through practical examples.

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Section: Ac Microgrids and Principal Componentsmentioning

confidence: 99%

Development of Control Techniques for AC Microgrids: A Critical Assessment

Cárdenas,

Martínez,

Molina

et al. 2023

Sustainability

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“…Neural network and fuzzy logic-based control schemes [48] have been applied for microgrid voltage control and energy management. In [49], the authors provide a detailed review of various neural network algorithms applied to the AC microgrid. In [50], the authors provide the current progress and future scopes for the implementation of artificial intelligence techniques in the microgrid control.…”

Section: Introductionmentioning

confidence: 99%

Robust Output Feedback Control of the Voltage Source Inverter in an AC Microgrid

Khan¹,

Memon²

2022

Energies

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This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous (dq) reference frame via a sliding mode control strategy. The control strategy required only output voltages; other states of the system were estimated by using a high-gain observer. The power-sharing among multiple inverters was achieved by solving power flow equations of the electrical network. The stability analysis showed that the error was ultimately bound in the case of the real PWM inverter and/or with a nonlinear load in the electrical network. The microgrid was simulated using the SimPowerSystems Toolbox from MATLAB/Simulink. The simulation results show the effectiveness of the proposed control scheme. The output voltage regulation of the inverter and power-sharing was achieved with the ultimately bounded error for the linear load. Later, the nonlinear load was also included in the electrical network and the error was shown to remain ultimately bounded. The output voltage regulation and power-sharing were achieved with the nonlinear load in the system.

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The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Ahmed,

Basit,

e Mustafa

et al. 2023

IET Generation Trans & Dist

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Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability.

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A Review of Various Neural Network Algorithms for Operation of AC Microgrids

Cited by 4 publications

References 45 publications

Development of Control Techniques for AC Microgrids: A Critical Assessment

Development of Control Techniques for AC Microgrids: A Critical Assessment

Robust Output Feedback Control of the Voltage Source Inverter in an AC Microgrid

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

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