Wind turbines power regulation using a low-complexity linear parameter varying-model predictive control approach

Bahmani, Hamidreza; Bayat, Farhad; Golchin, Mohamadjavad

doi:10.1177/0142331219862078

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…The stator active power and reactive power are both dependent on rotor current and can be adjusted by rotor d-axis and q-axis currents, according to Equations ( 23) and (24), respectively. By using Equations ( 21) and ( 22), the generalized structure of LMI based PI controller for RSC in DFIG-WECS is shown in Figure 3.…”

Section: Grid-tied Dfig Modelingmentioning

confidence: 99%

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A new optimal model predictive control scheme for a wind energy conversion system

Pradhan

Subudhi

Ghosh

2021

Int J Numerical Modelling

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The correct management of power exchange between the doubly-Fed induction generator (DFIG) and the grid depends on the effective optimal operation of the DFIG based wind energy conversion system (WECS). A modified optimal model predictive controller (MPC) architecture for WECS is proposed in this paper. The proposed scheme is carried out in two stages, as follows:(a) Using the MPC model and the tracking factor, an optimal control law is created, and (b) the aforementioned optimized issue is addressed using the linear matrix inequality (LMI) approach. Some improvements were made to the conventional LMI-based MPC technique to lower the high computational complexity. To perform active and reactive power control in DFIG, the proposed approach is applied on the rotor-side converter (RSC) control. The control law is divided into two sections. The first section deals with rotor current, while the second section deals with the RSC switching states. The LMI is used in the second portion of the control law to find the best switching state solution. The proposed method was created using the MATLAB/SIMULINK environment and tested in a 1hP DFIG-WECS setup. By comparing the obtained results to conventional LMI-PI, MPC-based WECS, the effectiveness of the obtained results is verified. Variable wind speed conditions are used to vary all the results. Integral absolute error, integral time absolute error, and Integral square error measurements are used to compare the tracking performance index of all methods.doubly fed induction generator, model predictive control, stator power control, wind energy conversion system 1 | INTRODUCTION Among all the renewable resources, wind energy is the fastest-growing resource in the power production market. The doubly-fed induction generators (DFIG) are mostly preferred in power generation plants for variable speed operation. When a wind power system is added to an existing power grid, it causes problems with electricity quality and stability. In a DFIG based WECS, a power electronic converter plays a major role in the control performance of grid-connected WECS. Two converters, the rotor-side converter (RSC) and the grid side converter (GSC), are used in DFIG-WECS to manage power flow between the DFIG and the grid. The schematic diagram of DFIG-WECS is shown in Figure 1. The

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Section: Grid-tied Dfig Modelingmentioning

confidence: 99%

“…However, the LMI‐based MPC mentioned above is not implemented in DFIG‐based WECS. In Reference 24, the power regulation of an LPV‐MPC model for wind turbines is compared to the LMI‐MPC scheme. The LMI‐based MPC approach, on the other hand, fails to reach the desired response as compared to the offline‐based LPV‐MPC method.…”

Section: Introductionmentioning

confidence: 99%

A new optimal model predictive control scheme for a wind energy conversion system

Pradhan

Subudhi

Ghosh

2021

Int J Numerical Modelling

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“…MPC transforms control problems into optimization problems, which provides great convenience for dealing with constraints and nonlinear models. At present, MPC has been widely applied in the control of complex industrial processes such as oil refining (Lu and Tsai, 2008), chemical industry (Kamesh and Rani, 2017), new energy (Bahmani et al, 2020), mineral processing (Reis et al, 2018), and aerospace (Weiss et al, 2015) and has become the mainstream method of process control today. However, MPC has not been widely adopted in the field of motor control.…”

Section: Introductionmentioning

confidence: 99%

Speed control of PMSM based on neural network model predictive control

Mao

Tang

2022

Transactions of the Institute of Measurement and Control

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In order to optimize the control performance of permanent magnet synchronous motor (PMSM) servo system, an improved model predictive control (MPC) scheme based on neural network is investigated in this paper. First, the dynamic characteristics of the PMSM are approximated by echo state network (ESN) to predict the future speed. Particle swarm optimization (PSO) is used to train ESN output weights to solve the problem that instability of output weights caused by pseudo-inverse matrix in ESN weight solving algorithm, called PSO-ESN, which enhances the stability and the accuracy of ESN speed prediction. That provides future plant output for control optimization of the predictive control. Furthermore, in order to reduce the computational cost and improve the response performance of the controller, a fast gradient method (GM) is applied to minimize the quadratic performance index and solve the optimal control input sequences. The simulation results under three different working conditions show that the PSO-ESNMPC controller designed in this paper reduces the overshoot by 5.87% and the rise time by 0.036 s compared with the reference controllers and has better robustness under parameter changes and load disturbances.

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“…The model predictive control approach is an advanced high-performance design technique which is known to be a very efficient method of controlling complex [23]- [25], and non-minimum-phase systems [26], [27]. The simplicity, conceptual easiness and accurate tracking performance of MPC approach have led to widespread successful applications, such as energy and power systems [28]- [30], aero-space [31], [32], and automotive control [33], [34]. Furthermore, MPC is acknowledged for its ability of coping with constraints on the state, output and input variables.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbines Power Regulation Subject to Actuator Constraints, Disturbances and Measurement Noises

et al. 2021

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An accurate understanding of the output power functionality in gas turbines concerning the fuel, inlet valve status and other parameters can provide a general overview of the turbine's performance. In this study, the problem of practical power regulation in gas turbines is investigated in the simultaneous presence of actuator constraints, unknown disturbances, and measurement noises. To this aim, a model predictive controller-based approach is utilized because of its high flexibility combined with a twolayer filtering and anti-filtering procedure to efficiently cope with the measurement noises. Moreover, the proposed structure takes the effects of unknown disturbances into account and thereby it is shown that the proposed controller can efficiently achieve all design objectives while handling actuator constraints, disturbances and measurement noises.

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Wind turbines power regulation using a low-complexity linear parameter varying-model predictive control approach

Cited by 11 publications

References 30 publications

A new optimal model predictive control scheme for a wind energy conversion system

A new optimal model predictive control scheme for a wind energy conversion system

Speed control of PMSM based on neural network model predictive control

Gas Turbines Power Regulation Subject to Actuator Constraints, Disturbances and Measurement Noises

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