Stability Analysis of Permanent Magnet Generator System With Load Current Compensating Method

Huang, Jian; Zhang, Zhuoran; Han, Jianda

doi:10.1109/tsg.2021.3112707

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Since the exciter is a first-order model, the order of the whole system is increased by one order from the original one, and the newly added state variables are [23]. Since p ω r and p δ are not affected by the exciter, the entire state-space model of the power system is written in the form of the following vector matrix:…”

Section: Modeling Of Single-machine Infinity Systems Considering Actu...mentioning

confidence: 99%

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Bao,

Ma,

et al. 2024

ENERGY

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The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning, and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.

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Section: Modeling Of Single-machine Infinity Systems Considering Actu...mentioning

confidence: 99%

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Bao,

Ma,

et al. 2024

ENERGY

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show abstract

“…In the field of electrical research, in [3], the MMC impedance remodeling method with advanced correction method is added to the DC voltage control to compensate for the phase angle of the negative damping frequency 2 band of MMC, which can effectively improve the stability domain margin of the grid-connected system and enhance the stability of the MMC grid-connected system. In [4], the Load Current Compensation Method (LCCM) is used to improve the stability of the aircraft power system caused by the constant power load. In [5], the inertia of the power electronic-dominant system (PEDS) is improved by using the method of virtual inertial control and the design and inertial layout optimization of the converter are guided by stability analysis during the control time by considering multiple time scales of the power electronic converter.…”

Section: Introductionmentioning

confidence: 99%

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Cheng,

Li,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

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“…One of these devices is the double-fed induction generator, also known as DFIG. This alternative has a good cost-benefit ratio mainly due to its power electronic converter only being precise near to 30% of the rated power [1], which leads to minor power losses, smaller and cheaper components when it is compared with full power converter applications used in other generator-converter topologies, as in squirrel cages [2] and permanent magnet generators [3].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

Lunardi

Chaves

et al. 2022

Energies

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This paper shows an increase in the robustness of a DFIG-based wind energy system when a predictive-repetitive controller in the dq reference frame is used for the control of the rotor current. This designing approach proposes a model predictive control with a receding horizon technique that uses the frequency decomposition of the reference signal as a filter. Furthermore, an augmented space-state model from the original plant model is obtained, and its implementation is addressed from the perspective of normal operating conditions of DFIG in wind energy systems. Additionally, this repetitive controller increases the robustness of the predictive control when a mismatch in machine parameters is considered. Experimental results presented in this paper endorse the advantages of this controller.

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Stability Analysis of Permanent Magnet Generator System With Load Current Compensating Method

Cited by 7 publications

References 29 publications

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

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