DDPG-Based Backstepping Controller for DC Solid State Transformer in DC Microgrid With Constant Power Loads

Meng, Xiangqi; Jia, Yanbing; Ren, Chunguang; Zhao, Pei; Han, Xiaoqing; Huang, Tao; Wang, Peng

doi:10.1109/tsg.2022.3184404

Cited by 18 publications

(4 citation statements)

References 38 publications

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“…In order to cope with large disturbances, a direct power control strategy was proposed in literature [21] to improve the dynamic response capability of the input voltage sudden change process. Literature [22] uses large signal analysis modeling and inverse system decoupling control to solve the problem of large voltage disturbance in Buck-Boost converters. Literature [23] proposes high order sliding mode control to maintain the output voltage stability of Buck-Boost converter under large disturbance.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control strategy based on Kalman filter for dual active bridge converter

Yan,

Hua,

Xun

et al. 2024

IEICE Electron. Express

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With the widespread use of electric vehicles, the large disturbance caused by the uncertainty of the AC side of the power grid will adversely affect the voltage stability of the DC side of the charging pile, which will threaten the charging safety of electric vehicles. In order to achieve fast and adaptive DC voltage regulation under large disturbance, this paper proposes a dual active bridge (DAB) converter control strategy based on Kalman Filter (KF) and Model Predictive Control (MPC). The improved Euler method is used to obtain a more accurate DAB discrete model, and the MPC control system is designed to improve the control accuracy. The best observation results of KF are used as feedforward compensation to improve the accuracy of output voltage regulation and ensure the stability of electric vehicles in the face of various disturbance problems. Finally, the effectiveness of the control strategy is verified by experiment.

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Section: Introductionmentioning

confidence: 99%

Model predictive control strategy based on Kalman filter for dual active bridge converter

Yan,

Hua,

Xun

et al. 2024

IEICE Electron. Express

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show abstract

“…But it is very sensitive to step size. Due to continuous output actions, strong robustness, and accelerating convergence speed, the deep deterministic policy gradient (DDPG) algorithm has been utilized in modern power electronic systems for different applications [120], [121] to improve dynamic performance and optimize efficiency. However, DDPG can produce suboptimal policies due to the overestimation of value functions.…”

Section: Ai-aided Control In Power Convertersmentioning

confidence: 99%

“…However, the current stress minimization problem has seldom been studied in the ISOP-DAB converter. Recently, data-driven methods are having more and more attention in modern power electronic systems due to the less dependence on model accuracy [119], [121]. With the advantages of model-free, strong exploration and exploitation ability, DRL methods show a brilliant future in controlling and stabilizing stochastic and time-varying power electronic systems [117], [123].…”

Section: Introductionmentioning

confidence: 99%

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Data-driven control methods of dual-active-bridge-based grid-connected battery energy storage system

Zeng

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Review and outlook on reinforcement learning: Its application in agricultural energy internet

Fu,

Zhang,

Bai

et al. 2024

IET Renewable Power Gen

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Agricultural Energy Internet (AEI), representing a key evolutionary direction in the integrated energy landscape of rural regions, holds a vital position in advancing the electrification of agricultural sectors. However, the disjointed control between agricultural loads and grid operations hinders the collaborative development of agriculture and energy. Addressing these issues, this paper investigates the current applications of artificial intelligence in the fields of agriculture and energy. The authors examine the evolutionary path of AEI, particularly emphasizing the critical technologies emerging from the intersection of agriculture, energy, and digital networks. Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. In response to the challenges posed by low energy efficiency in rural areas, a reinforcement learning framework is proposed for coordinating fisheries, agriculture, livestock farming, and rural distribution networks. This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning.

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DDPG-Based Backstepping Controller for DC Solid State Transformer in DC Microgrid With Constant Power Loads

Cited by 18 publications

References 38 publications

Model predictive control strategy based on Kalman filter for dual active bridge converter

Model predictive control strategy based on Kalman filter for dual active bridge converter

Data-driven control methods of dual-active-bridge-based grid-connected battery energy storage system

Review and outlook on reinforcement learning: Its application in agricultural energy internet

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