Bat inspired algorithm based optimal design of model predictive load frequency control

Elsisi, Mahmoud; Soliman, M.; Aboelela, Magdy A.S.; Mansour, W.

doi:10.1016/j.ijepes.2016.04.036

Cited by 79 publications

(33 citation statements)

References 18 publications

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“…Equation (23) describes the power deviation rate limit of the AGC units in each area. Then, by Equation (17), each controller estimates the future state at time k and broadcasts it in the communication network together with the optimal control sequence over the control horizon. At time k, based on the information from the communication network, the optimization problem (Equation (22)) is solved in each controller.…”

Section: Lower-level Model Predictive Control Controller: Dynamic Frementioning

confidence: 99%

“…In [16], the authors studied the merging of wind turbines in a multi-area power system controlled by a robust AGC based on the MPC technique. In [17], the parameters of the MPC controller were determined by a bat-inspired algorithm to deal with system nonlinearities comprising generation rate constraints (GRCs) and governor dead bands (GDBs). However, these centralized control solutions are often impractical for application to a large-scale power system for computational reasons and the lack of error tolerance.…”

Section: Introductionmentioning

confidence: 99%

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Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

Chuan

Liu

2019

Processes

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With the high degree of wind power penetration integrated into multi-area AC/DC interconnected power grids, the frequency regulation capacity of automatic generation control (AGC) units is not sufficient in the wind power-penetrated area, making it difficult to effectively suppress the frequency stability caused by the fluctuation of wind power. Therefore, a coordinated control strategy for AGC units across areas based on bi-level model predictive control is proposed in this paper to achieve resource sharing. The control scheme uses economic model predictive control to realize steady power optimal allocation of the AGC units across areas in the upper layer and distributed model predictive control to realize dynamic frequency optimization control of the multi-area AGC units in the lower layer. Taking a three-area AC/DC interconnected power grid with a wind farm as an example, the simulation results show that, compared with model predictive control using tie-line frequency bias control (TBC) mode, the proposed control strategy can not only effectively maintain tie-line safety and frequency stability, but can also reduce the regulation cost of multi-area AGC units.

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Section: Lower-level Model Predictive Control Controller: Dynamic Frementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

Chuan

Liu

2019

Processes

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“…In the past decades, model predictive control (MPC) has shown the potential ability of control design for industrial processes [13][14][15]. As a recently developed MPC, state-space predictive functional control (PFC) [3,16,17] provides a novel insight into control design for the industrial processes against partial actuator failures because it not only has theoretical basis for the control design but also has advantages in hardware implementation, computational capability, and control accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

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As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

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“…The algorithm named Model Predictive Control (MPC) belongs to a category of artificial intelligence algorithms that compute in a sequential manner using adjustments of manipulated variables to optimise and predict the future behaviour of the system. MPC is considered as one of the advanced control technique in control area [2,27]. Its theoretical development over years can be seen by the amount of research available in the literature.…”

Section: Introductionmentioning

confidence: 99%

Predictive Active Power Control of Two Interconnected Microgrids

Tungadio

Bansal

Siti

et al. 2018

Technol Econ Smart Grids Sustain Energy

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When dealing with interconnected power systems, any sudden load changes leads to the deviation of active power and frequency in the tie-line. The daily management of the power system is also conditioned by the control of the active power and the frequency. It is a very important task in the conduct of the any electrical network to supply sufficiently and with high reliability the active power to be transmitted to the customers. Maintaining the frequency of each area and tie-line active power flow variation within prescribed value by adjusting the generator active outputs remain one of the main tasks of the network operators. This paper presents a model predictive regulator in controlling the power flow in tie-lines and frequency deviations in the microgrid, which will lead to power balance between the total active power generated and active power demand of the system. The system being studied consists of two microgrids, each made up of a wind farm, conventional thermal and hydro plants generator, photovoltaic (PV) system, storage system and active power demand. Predictive control algorithm is applied to control the power flow between two microgrids.

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Bat inspired algorithm based optimal design of model predictive load frequency control

Cited by 79 publications

References 18 publications

Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Predictive Active Power Control of Two Interconnected Microgrids

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