Optimal Controller Design for Inverter-Based Microgrids

Rokrok, Esmaeel; Zavareh, Fariba Shavakhi; Soltani, Jafar; Shakarami, Mahmoud Reza

doi:10.1007/s40998-019-00197-4

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Even though the SSE of this control scheme is smaller than the conventional finite-set MPC method, the disadvantage of this control scheme is that only the SSE issue is considered while other responses, such as voltage and current THDs, are not well manipulated. Beside MPC, other optimal algorithms, such as particle swarm optimization (PSO) (Chang et al, 2022; Esmaeel et al, 2019) or salp swarm optimization (SSO) (Ahmed et al, 2020; Omnia et al, 2020), are also employed to enhance the voltage quality of the VSI. These algorithms are combined with other controllers to optimize the system with a simple structure; however, only parameters are optimized, whereas the optimal performance of the overall system is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

Thi-Thuy Vu,

Xuan Nguyen,

Quang Bui

2023

Transactions of the Institute of Measurement and Control

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The operation of the three-phase standalone inverter is affected by many factors, such as heavy changes in load, unbalanced loads, nonlinear loads, system uncertainties and external disturbances. These are critical weaknesses for the control system of the three-phase inverter to reach robust optimal performance. This paper proposes an adaptive optimal sliding mode control (AOSMC) scheme for a three-phase nonlinear uncertain inverter. This AOSMC strategy solves the problems of nonlinear optimization by adaptive dynamic programming, one of the techniques of reinforcement learning, and overcomes the uncertainties and disturbance effects by a disturbance observer–based sliding mode controller. This algorithm uses only one neural network to approximate the critic; therefore, the burden of computation is significantly reduced. Both the weight matrix of the critic network and the disturbance observer are asymptotically stable. The overall system is guaranteed to be ultimately uniformly bounded stable via Lyapunov stable theory. The simulation is conducted to validate the insensitivity of the proposed AOSMC algorithm to working conditions. Also, the competitive results are presented to demonstrate the improvement of the proposed AOSMC scheme in comparison to some other existing controllers.

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Section: Introductionmentioning

confidence: 99%

Adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

Thi-Thuy Vu,

Xuan Nguyen,

Quang Bui

2023

Transactions of the Institute of Measurement and Control

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“…In [18,63] is presented a classification of voltage and current controllers applied to the inner loop and it is illustrated in Table 2. [103] Proportional Resonant (PR) Controller [103] Dead-beat Controller (DB) [104] DB Controller [105] Predictive Controller [106] Sliding-mode Controller (SMC) [107] Hysteresis Controller [108] dq Proportional Integral (PI) Controller [109] PI Controller [110] Linear Quadratic Regulator (LQR) Controller [111] αβ PR Controller [112] PR Controller [113]…”

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confidence: 99%

Grid Forming Inverters: A Review of the State of the Art of Key Elements for Microgrid Operation

et al. 2022

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In the past decade, inverter-integrated energy sources have experienced rapid growth, which leads to operating challenges associated with reduced system inertia and intermittent power generation, which can cause instability and performance issues of the power system. Improved control schemes for inverters are necessary to ensure the stability and resilience of the power system. Grid-forming inverters dampen frequency fluctuations in the power system, while grid-following inverters can aggravate frequency problems with increased penetration. This paper aims at reviewing the role of grid-forming inverters in the power system, including their topology, control strategies, challenges, sizing, and location. In order to facilitate continued research in this field, a comprehensive literature review and classification of the studies are conducted, followed by research gaps and suggestions for future studies.

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Power management and bus voltage control of a battery backup-based stand-alone PV system

2021

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Optimal Controller Design for Inverter-Based Microgrids

Cited by 5 publications

References 28 publications

Adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

Adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

Grid Forming Inverters: A Review of the State of the Art of Key Elements for Microgrid Operation

Power management and bus voltage control of a battery backup-based stand-alone PV system

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