Line impedance compensation control strategy for multiple interlinking converters in hybrid AC/DC microgrid

Wang, Can; Deng, Can; Pan, Xuewei

doi:10.1049/gtd2.12733

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Batteries 2023, 9, x FOR PEER REVIEW 7 of 36 management, and high-rate renewable/intermittent power control units [27,[30][31][32]. The MARL-based control has earned remarkable success in solving complicated power-storage control defects, specifically in balancing the flow of power storage in advanced applications of power distribution, such as micro/smart grids and V2G [17].…”

Section: Methodsmentioning

confidence: 99%

“…In particular, the accomplished actions are compared with other possible successful actions to track toward the optimized solution. Accordingly, the application of the ANN-based MARL control on complicated multivariable nonlinear control applications requiring high accuracy has shown significant success, such as in autonomous vehicles, aircraft applications, nuclear management, and high-rate renewable/intermittent power control units [27,[30][31][32]. The MARL-based control has earned remarkable success in solving complicated power-storage control defects, specifically in balancing the flow of power storage in advanced applications of power distribution, such as micro/smart grids and V2G [17].…”

Section: Article Structurementioning

confidence: 99%

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Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

Al-Saadi,

Short

2023

Batteries

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The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question.

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Section: Methodsmentioning

confidence: 99%

Section: Article Structurementioning

confidence: 99%

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

Al-Saadi,

Short

2023

Batteries

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A Common-Grounded Single-Phase AC/Bipolar DC Hybrid Microgrid With a Four-Leg Interlinking Converter

Naghizadeh,

Hojabri,

Muljadi

2023

IEEE J. Emerg. Sel. Topics Power Electron.

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Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

Awais

Khan

et al. 2023

Energies

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The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

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Line impedance compensation control strategy for multiple interlinking converters in hybrid AC/DC microgrid

Cited by 3 publications

References 31 publications

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

A Common-Grounded Single-Phase AC/Bipolar DC Hybrid Microgrid With a Four-Leg Interlinking Converter

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

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