Tuning a model predictive controller for doubly fed induction generator employing a constrained genetic algorithm

Rodrigues, Lucas Lima; Potts, Alain Segundo; Vilcanqui, Omar A. C.; Filho, Alfeu J. Sguarezi

doi:10.1049/iet-epa.2018.5922

Cited by 18 publications

(6 citation statements)

References 44 publications

(65 reference statements)

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“…In Equation (17), fit p (i ) correspond to fitness value for the agent 'p' with time 'i' and also, for the optimization problem, worst(i) and best(i) are mathematically defined as given in Equations (19) and (20).…”

Section: Proposed Gsa Techniquementioning

confidence: 99%

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Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Bharti

Sarita

Vardhan³

et al. 2021

IET Renewable Power Gen

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This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Proposed Gsa Techniquementioning

confidence: 99%

“…It has control over P and Q for rapid grid synchronism, flexible power directive, and maximum power point tracking [19]. Also MPC based novel constrained GA is anticipated in [20]. Further, examination of the transient and dynamic responses, along with system oscillations are needed for the system to operate within constraints.…”

Section: Introductionmentioning

confidence: 99%

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Bharti

Sarita

Vardhan³

et al. 2021

IET Renewable Power Gen

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“…In [1], multi-objective Genetic Algorithms (GA) are used to determine MPC weights, enhancing closed-loop performance. Additionally, [2] presents a GA approach that handels constraints. (a) The RL agent dynamically adjusts MPC cost function weights online to align with an optimal raceline trajectory on the Monteblanco racetrack.…”

Section: Introductionmentioning

confidence: 99%

Weights-varying MPC for Autonomous Vehicle Guidance: a Deep Reinforcement Learning Approach

Zarrouki

Klös

Heppner

et al. 2021

2021 European Control Conference (ECC)

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Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective Bayesian Optimization (BO) techniques solve this problem by determining a Pareto optimal parameter set for an MPC with static weights. However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime. Deep Reinforcement Learning (RL) algorithms can automatically learn context-dependent optimal parameter sets and dynamically adapt for a Weightsvarying MPC (WMPC). However, learning cost function weights from scratch in a continuous action space may lead to unsafe operating states. To solve this, we propose a novel approach limiting the RL actions within a safe learning space representing a catalog of pre-optimized BO Pareto-optimal weight sets. We conceive a RL agent not to learn in a continuous space but to proactively anticipate upcoming control tasks and to choose the most optimal discrete actions, each corresponding to a single set of Pareto optimal weights, context-dependent. Hence, even an untrained RL agent guarantees a safe and optimal performance. Experimental results demonstrate that an untrained RL-WMPC shows Pareto-optimal closed-loop behavior and training the RL-WMPC helps exhibit a performance beyond the Pareto-front.

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“…The predictive control strategies for WECS have been improved by the introduction of optimization schemes. A constrained genetic algorithm (GA) has been proposed in [8] for the DFIG-based WECS. The weighting factors have been determined using a novel GA, and thus provided a better solution for constrained problems in the DFIG systems.…”

Section: Introductionmentioning

confidence: 99%

A Super Twisting Fractional Order Terminal Sliding Mode Control for DFIG-Based Wind Energy Conversion System

et al. 2020

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The doubly fed induction generator (DFIG)-based wind energy conversion systems (WECSs) are prone to certain uncertainties, nonlinearities, and external disturbances. The maximum power transfer from WECS to the utility grid system requires a high-performance control system in the presence of such nonlinearities and disturbances. This paper presents a nonlinear robust chattering free super twisting fractional order terminal sliding mode control (ST-FOTSMC) strategy for both the grid side and rotor side converters of 2 MW DFIG-WECS. The Lyapunov stability theory was used to ensure the stability of the proposed closed-loop control system. The performance of the proposed control paradigm is validated using extensive numerical simulations carried out in MATLAB/Simulink environment. A detailed comparative analysis of the proposed strategy is presented with the benchmark sliding mode control (SMC) and fractional order terminal sliding mode control (FOTSMC) strategies. The proposed control scheme was found to exhibit superior performance to both the stated strategies under normal mode of operation as well as under lumped parametric uncertainties.

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Tuning a model predictive controller for doubly fed induction generator employing a constrained genetic algorithm

Cited by 18 publications

References 44 publications

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Weights-varying MPC for Autonomous Vehicle Guidance: a Deep Reinforcement Learning Approach

A Super Twisting Fractional Order Terminal Sliding Mode Control for DFIG-Based Wind Energy Conversion System

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