A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines

Mohamed, Mohamed A.; Diab, Ahmed A. Zaki; Rezk, Hegazy; Jin, Tao

doi:10.1007/s00521-019-04205-w

Cited by 63 publications

(34 citation statements)

References 30 publications

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“…The control of the wind turbines generator, usually a double fed induction one, has been widely studied. For instance, Mohamed et al [21], based on a simplified model for the frequency response of DFIG WT, develop an adaptive model predictive controller for the load frequency control of the power system of the WT.…”

Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, . . .) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.INDEX TERMS Floating offshore wind turbines (FOWTs), pitch control, neuro-estimator, neural network, virtual sensor, wind energy.

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Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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“…Furthermore, the execution of these controllers for secondary control becomes furthermore imperative and difficult in case of grid integration of wind energy‐based RESs. Various studies are found in the recent years towards frequency regulations of wind integrated power systems using conventional [2, 38, 39], layered recurrent ANN [25], sliding mode [40], model predictive [41, 42], etc. controls as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

Sharma¹,

Dhundhara

Arya³

et al. 2020

IET Renewable Power Generation

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The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy‐based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two‐area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG‐WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

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Section: Introductionmentioning

confidence: 99%

“…In recent years, attention to robust approaches to controller design has increased strictly [1][2][3][4][5][6]. Meanwhile, model predictive controller is widely used to control industrial systems [4][5][6][7][8] due to its unique features. Given the fact that most operational systems are nonlinear and bounded systems, the predictive controllers are used on the basis of linear or non-linear models with uncertainty [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A new robust LMI-based model predictive control for continuous-time uncertain nonlinear systems

Huofa

Marrani

2020

Automatika

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This paper presents a new robust predictive controller for a special class of continuous-time non-linear systems with uncertainty. These systems have bounded disturbances with unknown upper bound as well as constraints on input states. The controller is designed in the form of an optimization problem of the 'worst-case' objective function over an infinite moving horizon. Through this objective function, constraints and uncertainties can be applied explicitly on the controller design, which guarantees the system stability. Next, LMI tool is used to improve the calculation time and complexity. To do this, in order to find the optimum gain for state-feedback, the optimization problem is solved using LMI method in each time step. Finally, to show the efficiency and effectiveness of the proposed algorithm, a surge phenomenon avoidance problem in centrifugal compressors is solved.

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A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines

Cited by 63 publications

References 30 publications

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

A new robust LMI-based model predictive control for continuous-time uncertain nonlinear systems

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