Analysis on Chaotic Mechanism of Direct-Drive Permanent Magnet Synchronous Generators Based on Lyapunov Stability Theory

Li, Yang; Huang, Tianmin; Deng, Lin; Zeng, Yingfeng; Huang, Su-Dan

doi:10.18280/ejee.210607

Cited by 6 publications

(4 citation statements)

References 16 publications

(16 reference statements)

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“…The stochastic optimization problem defined in Eq. (6) can be transformed into a series of continuous deterministic optimization problems through Lyapunov optimization theory [15,16]. In each time slot, the standard convex optimization method is used to solve these problems.…”

Section: B Dynamic Resource Allocation Algorithm For Reducing Energymentioning

confidence: 99%

Optimization of Time and Power Resources Allocation in Communication Systems Under the Industrial Internet of Things

Wang

2020

IEEE Access

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In the Industrial Internet of Things (IIoT), it is an urgent task to reduce power loss and enhance energy efficient through reasonable allocation of resources. Inspired by time slot channel frequency hopping, this paper puts forward a dynamic allocation model for time and power resources. Based on the proposed model, a dynamic resource allocation algorithm was designed to reduce energy consumption. In addition, a power and time allocation algorithm was developed to maximize the energy efficiency of the system. The workflows of the two algorithms were introduced in details. Simulation results show that both dynamic resource allocation algorithm could reduce the energy loss of the communication system, while ensuring the stability of the data queue. The research findings help to promote the performance of communication systems in different scenarios of the IIoT.

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Section: B Dynamic Resource Allocation Algorithm For Reducing Energymentioning

confidence: 99%

Optimization of Time and Power Resources Allocation in Communication Systems Under the Industrial Internet of Things

Wang

2020

IEEE Access

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“…Regarding the generators for wind energy conversion system (WECS), wind turbine manufacturers use several types of electric generators, notably doubly fed induction generators [5][6][7], permanent magnet synchronous generators [8,9], and the dual star induction generator (DSIG) [10,11]. The DSIG has more advantages than the conventional cage induction generator, namely high reliability, low magnitude with high frequency of torque pulsations, reduced rotor harmonic current, and segmentation power [12].…”

Section: Introductionmentioning

confidence: 99%

Robust Neural Control of the Dual Star Induction Generator Used in a Grid-Connected Wind Energy Conversion System

Mesai-Ahmed¹,

Bentaallah²,

Cardoso³

et al. 2021

MMEP

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This paper presents a field-oriented control (FOC) of a dual star induction generator (DSIG) applied in a grid-connected wind energy conversion system. Currently, the dual star induction machine (DSIM) is increasingly used among multiphase machines. The machine has two star-connections, sharing the same stator offset, by an electrical angle of 30° and fed by two parallel converters. Maximum power point tracking (MPPT) is illustrated in a first stage, in order to extract a maximum of power under fluctuating wind speed. In a second stage, vector control of a DSIG with FOC is described. Finally, voltage oriented control (VOC) is used to ensure the power factor unity on the grid side. The main contribution of the presented paper is the application of a simple architecture of an artificial neural network (ANN) controller in order to improve the robustness and stability of the system, especially against the parameter change. In comparison with the conventional control, which is known by its sensitivity, the proposed neural MPPT with neural FOC (NMPPT-NFOC) presents better performance under normal and abnormal conditions. The robustness and effectiveness of the proposed control has been validated through illustrative simulation results with different functional zones, and for fixed and variable wind speed.

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“…Among these control methods, the design method of state feedback control is simpler, the control performance is better, and the dynamic response is faster, which can better suppress the chaos phenomenon and achieve the desired effect. The chaos motion of D-PMSG was found for the first time in 2009 [30], and the chaos modeling and dynamic characteristics analysis of D-PMSG has received much attention among researchers [31,32]. A backstepping adaptive control algorithm was proposed to suppress the influence of parameter uncertainty and wind speed disturbance on the operation performance of D-PMSG [33].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

Yang

Sheng

et al. 2021

Energies

Self Cite

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To research the chaotic motion problem of the direct-drive permanent magnet synchronous generator (D-PMSG) for a wind turbine with uncertain parameters and fractional order characteristics, a control strategy established upon fuzzy state feedback is proposed. Firstly, according to the working mechanism of D-PMSG, the Lorenz nonlinear mathematical model is established by affine transformation and time transformation. Secondly, fractional order nonlinear systems (FONSs) are transformed into linear sub-model by Takagi–Sugeno (T-S) fuzzy model. Then, the fuzzy state feedback controller is designed through Parallel Distributed Compensation (PDC) control principle to suppress the chaotic motion. By applying the fractional Lyapunov stability theory (FLST), the sufficient conditions for Mittag–Leffler stability are formulated in the format of linear matrix inequalities (LMIs). Finally, the control performance and effectiveness of the proposed controller are demonstrated through numerical simulations, and the chaotic motions in D-PMSG can be eliminated quickly.

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Analysis on Chaotic Mechanism of Direct-Drive Permanent Magnet Synchronous Generators Based on Lyapunov Stability Theory

Cited by 6 publications

References 16 publications

Optimization of Time and Power Resources Allocation in Communication Systems Under the Industrial Internet of Things

Optimization of Time and Power Resources Allocation in Communication Systems Under the Industrial Internet of Things

Robust Neural Control of the Dual Star Induction Generator Used in a Grid-Connected Wind Energy Conversion System

Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design

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