LMI-Based Robust Predictive Load Frequency Control for Power Systems With Communication Delays

Ojaghi, Pegah; Rahmani, M.

doi:10.1109/tpwrs.2017.2654453

Cited by 151 publications

(100 citation statements)

References 29 publications

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“…For example, as the most popular control technique, proportional-integral-derivative (PID) controller and its various variations have been widely applied to the LFC issue [3][4][5][6][7][8]. Moreover, some researchers have paid more attention to the advanced control theories based LFC methods recently, such as robust control theories [9], model predictive control [10][11][12][13][14], sliding mode control [15,16], neural network control [17], internal model control [18], and differential games [19]. It should be noted that there are different evolutionary algorithms based PID or proportional-integral (PI) control methods for the LFC issue of multi-area power systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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Abstract:As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

2017

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“…Model predictive control [90][91][92][93] ·Reduce data loss ·Good robustness at large delay error ·Can handle fixed and random delay ·Complex algorithm ·Large computation load ·Low dynamic response Smith predictor [90,94,95] ·Fast dynamic response ·Can handle fixed delay and random delay ·Model uncertainties and external disturbances are existed Neural network predictive control [96] ·Linearized ·High robustness ·Not handling random delay Weighted average predictive control [97,98] ·Improve consensus convergence ·Fast tracking speed ·Strong anti-interference ability ·Influence of practical factors on prediction are not considered ·Not handling random delay Gain scheduling method [99][100][101][102][103][104][105][106] ·Provide a general modeling approach ·Cost reduction ·Good power sharing performance in delay margin ·High robustness ·Gain coefficient and integral term are difficult to be selected ·Not handling random delay H∞ control method [107][108][109] ·Handle fixed and random delay ·Large computation ·Complex algorithm ·Low robustness Sliding mode control [110][111][112][113] ·Simple implementation ·Handle fixed and random delay ·Fast dynamic response ·Robustness under parameter variation and disturbance ·Chattering exists…”

Section: Random Communication Delaymentioning

confidence: 99%

“…It is worth mentioning that, delays are mainly divided into the fixed communication delays and random communication delays, and various delay compensation schemes for fixed communication delays are compared in this paper, such as the neural network predictive control [96], weighted average predictive control [97,98], the gain scheduling [99][100][101][102][103][104][105][106] and synchronization schemes using multi-timer model [114]. Moreover, some compensation methods for random communication delays are also discussed, such as the generalized predictive control (GPC) [84,85], networked predictive control (NPC) [86][87][88][89], model predictive control (MPC) [90][91][92][93], Smith predictor (SP) [90,94,95], H ∞ control [107][108][109] and sliding mode control [110][111][112][113], etc. In addition, if one microgrid plugs in or plug out from the microgrid clusters, the topology and structure of the system will be inevitably different.…”

Section: Introductionmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

367

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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“…Robust MPC based on linear matrix inequalities (LMIs) is a way of computing MPC control laws as a state feedback controller . Recently, decentralized and distributed LMI‐based MPC frameworks are utilized to control systems with distributed linear models . However, the LMIs of these methods may become infeasible or produce very conservative results when they are applied to strongly interacting subsystems.…”

Section: Introductionmentioning

confidence: 99%

“…16,17 Recently, decentralized and distributed LMI-based MPC frameworks are utilized to control systems with distributed linear models. 18,19 However, the LMIs of these methods may become infeasible or produce very conservative results when they are applied to strongly interacting subsystems. To avoid this limitation, cooperative distributed schemes can be considered.…”

Section: Introductionmentioning

confidence: 99%

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Adelipour

Haeri

2019

Optim Control Appl Methods

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Summary In this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two application cases are studied to show the effectiveness of the proposed method.

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LMI-Based Robust Predictive Load Frequency Control for Power Systems With Communication Delays

Cited by 151 publications

References 29 publications

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

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