Finite-time function projective synchronization control method for chaotic wind power systems

Zhang, Hongli; Fan, Wenhui; Ma, Ping

doi:10.1016/j.chaos.2020.109756

Cited by 20 publications

(3 citation statements)

References 24 publications

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“…After the centralized grid connection of large-scale wind power, the stability of power grid voltage can be easily affected by natural wind, especially by high wind speed. Consequently, the system will suffer from bifurcation or chaotic oscillations, which will cause grid disassembly in serious cases [1][2][3], drastically affecting the safety, quality, and stable operation of the power grid [4][5][6]. It is difficult to control or suppress these chaotic oscillations using traditional linear controllers.…”

Section: Introductionmentioning

confidence: 99%

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Wang

et al. 2023

IET Generation Trans & Dist

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As a complex non‐linear system, the chaotic oscillation of power system seriously threatens the safe and stable operation of power grids. In this paper, a finite time adaptive synchronization control method is proposed to mitigate the problem of chaotic oscillation in the power system. The proposed method can realize chaos control and parameter identification by completely synchronizing the fractional‐order chaotic power system with the stable fractional‐order power system to identify parameters within a finite time. The fractional Lyapunov stability theory is used for numerical simulation. Theoretical and simulation results show that this method can effectively stabilize the system in a finite time. It is proved that compared with the adaptive synchronous control method, the control method is simpler in design, shorter in action time, and more meaningful in engineering practice.

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Section: Introductionmentioning

confidence: 99%

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Wang

et al. 2023

IET Generation Trans & Dist

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“…Synchronization of chaotic systems has received substantial attention theoretically and experimentally in recent years. Such attention is justified by the potential applications in secure communication [ 1 , 2 , 3 ], the control of chemical reactions with the aim of determining the favorable conditions for practical implementation [ 4 ], the stability of the chaotic wind power system in finite time [ 5 ], the synchronization of chaotic finance systems with known and unknown systems parameters [ 6 , 7 ], authentication from brain signals [ 8 ], chaotic attitude synchronization and anti-synchronization for master–slave satellites under unknown moments of inertia and disturbance torques [ 9 ], regulation of glucose–insulin concentrations from a chaotic regime (an illness) to a disorder-free equilibrium [ 10 ], and the relation between a meteorological phenomenon and influenza pandemics [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Synchronization of Chaotic Systems with Hidden Attractors

Zaqueros-Martinez

Gómez

Tlelo‐Cuautle

et al. 2023

Entropy

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Chaotic systems are hard to synchronize, and no general solution exists. The presence of hidden attractors makes finding a solution particularly elusive. Successful synchronization critically depends on the control strategy, which must be carefully chosen considering system features such as the presence of hidden attractors. We studied the feasibility of fuzzy control for synchronizing chaotic systems with hidden attractors and employed a special numerical integration method that takes advantage of the oscillatory characteristic of chaotic systems. We hypothesized that fuzzy synchronization and the chosen numerical integration method can successfully deal with this case of synchronization. We tested two synchronization schemes: complete synchronization, which leverages linearization, and projective synchronization, capitalizing on parallel distributed compensation (PDC). We applied the proposal to a set of known chaotic systems of integer order with hidden attractors. Our results indicated that fuzzy control strategies combined with the special numerical integration method are effective tools to synchronize chaotic systems with hidden attractors. In addition, for projective synchronization, we propose a new strategy to optimize error convergence. Furthermore, we tested and compared different Takagi–Sugeno (T–S) fuzzy models obtained by tensor product (TP) model transformation. We found an effect of the fuzzy model of the chaotic system on the synchronization performance.

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“…Besides, ABA Al-Hussein et al [17] proposed an adaptive cooperative control strategy to realize the chaos suppression of power system with multiple control inputs based on multiequipment. Furthermore, C. Wang et al [18] combined finite time theory and function projection to design a chaos controller to synchronize the power system chaos model with the ideal model in a limited time, which indirectly realized the power system chaos control. Reference [19] proposed a sliding mode control method with the reaching law of the relay function, which can quickly suppress the chaos oscillation of the power system while significantly reducing the chatter.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Cooperative Internal Mode Control of RBF Neural Network for Power System Chaos Suppression

Liu,

Zhang,

Yang

et al. 2023

IEEE Access

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In this paper, a multivariate cooperative internal mode control method based on RBF neural network (RBF-NN) inverse system is proposed to suppress the chaotic behavior in the power system. Firstly, a seven-dimensional model of the controlled power system including energy storage (ES) and static var compensator (SVC) is constructed, and the chaotic dynamics of the system is analyzed by local bifurcation diagram, attractor phase diagram and timing diagram, and the merging crisis and coexistence of chaotic attractors are found in the power system under the action of the low-frequency power disturbance. Secondly, considering the parameter uncertainties of the power system, an RBF-NN inverse system model of the controlled power system is established based on inverse system theory and neural network theory to realize its pseudo-linearization, and a multivariable cooperative internal mode controller is designed to suppress the chaotic behavior in the power system by combining the ES and the SVC. Finally, the effectiveness and robustness of the proposed chaos suppression control strategy are verified by simulation.INDEX TERMS chaos, chaos suppression, internal mode control, power system, RBF neural network.

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Finite-time function projective synchronization control method for chaotic wind power systems

Cited by 20 publications

References 24 publications

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Fuzzy Synchronization of Chaotic Systems with Hidden Attractors

Multivariate Cooperative Internal Mode Control of RBF Neural Network for Power System Chaos Suppression

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