Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

Zhu, Guoqiang; Nie, Linlin; Zhou, Miaolei; Zhang, Xiuyu; Sun, Lingfang; Zhong, Cheng

doi:10.1109/access.2020.3022293

Cited by 7 publications

(3 citation statements)

References 52 publications

(60 reference statements)

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“…The authors in [77] proposed a CA selection model for TS EPS based on the FL algorithm, which is described by the following expression:…”

Section: Providing Transient Stabilitymentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

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“…The authors in [77] proposed a CA selection model for TS EPS based on the FL algorithm, which is described by the following expression:…”

Section: Providing Transient Stabilitymentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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“…However, the obvious limitation of conventional backstepping design is the problem of "complexity of explosion" caused by the repeated differentiation of some nonlinear functions and the lack of robustness against uncertainties. To overcome this limitation of traditional backstepping control, dynamic surface control (DSC) is proposed as an effective alternative method [26][27][28][29][30]. In [27], a dynamic surface control method based on RBF neural network approximation is proposed for a class of nonlinear time-delay systems with state variables all measurable, which greatly simplifies the design process of the controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Dynamic Surface Integral Sliding Mode Control for Quadrotor UAVs under Parametric Uncertainties and External Disturbances

Zhang

Zhu

et al. 2020

Complexity

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A robust adaptive fuzzy nonlinear controller based on dynamic surface and integral sliding mode control strategy (ADSISMC) is proposed to realize trajectory tracking for a class of quadrotor UAVs. In this study, the composite factors including parametric uncertainties and external disturbances are added to controller design, which make it more realistic. The quadrotor model is divided into two subsystems of attitude and position that make the control design become feasible. The main contributions of the proposed ADSISMC strategy are as follows: (1) The combination of dynamic surface and integral sliding mode makes the system always in sliding stage by finding the appropriate initial position compared with the common sliding mode, and the complexity of explosion in backstepping method is eliminated. (2) By introducing the fuzzy system, the unknown functions and uncertainties can be approximated which significantly improves the robustness and the tracking performance. (3) The switching control strategy is utilized to compensate for the errors between estimated and ideal inputs; the tracking performance of the whole system has been significantly improved. The simulation results show the effectiveness of the proposed control method.

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“…However, the feedback linearized excitation controller has a parameter sensitivity problem. Sliding mode control (SMC) overcomes the parameter sensitivity problem due to being less sensitive to changes in parameters and external disturbances [29][30][31][32][33][34]. In [35], for the multimachine power system with external disturbance, the adaptive method and the sliding mode variable structure method are combined based on the feedback linearization to design a decentralized coordinated adaptive sliding mode stabilizer.…”

mentioning

confidence: 99%

Adaptive Dynamic Surface Integral Sliding Mode Fault-Tolerant Control for Multimachine Excitation Systems with SVC

Zhu

et al. 2020

Complexity

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In this paper, an adaptive dynamic surface integral sliding mode fault-tolerant controller is designed for the multimachine power system with static var compensator (SVC) to overcome the problem of actuator failure. The main features of the proposed method are as follows: (1) By combining the dynamic surface control (DSC) method with integral sliding mode (ISM), the tracking errors of the system converge to the neighborhood of zero within a finite time, and the convergence speed, tracking accuracy, and anti-interference ability of the system are also significantly improved. (2) By introducing the failure factors, an adaptive fault-tolerant controller is designed to ensure the stability of the entire system after partial failure of the actuator. (3) By estimating the norm of the ideal weight vector of the radial basis function neural networks (RBFNNs), the computational burden of the controller is reduced. Finally, the simulation results show the effectiveness of the proposed control scheme.

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Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

Cited by 7 publications

References 52 publications

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Adaptive Robust Dynamic Surface Integral Sliding Mode Control for Quadrotor UAVs under Parametric Uncertainties and External Disturbances

Adaptive Dynamic Surface Integral Sliding Mode Fault-Tolerant Control for Multimachine Excitation Systems with SVC

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