Multiple Model Predictive Control: A State Estimation based Approach

With the large-scale grid connection of new energy power, the random fluctuation existing in the power system is intensified, which leads to frequent fluctuation of load instructions of thermal power units. It is of great significance to improve the variable load performance of the coal-fired units. It is more difficult to control the superheated steam temperature (SST). In order to improve the control performance of SST, a state variable fuzzy predictive control method is proposed in this paper. Firstly, Takagi-Sugeno fuzzy state observer is used to approximate the non-linear plant of the SST. At the same time, based on the state observer, a fuzzy state feedback controller is designed to improve its dynamic characteristics. Thirdly, based on the extended predictive model of the state feedback controller, a model predictive controller is designed to realize the SST tracking control. Dynamic simulation shows the effectiveness of the strategy.

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“…   (11) where the expressions of S c , S e , and S x and the detailed derivation of this section can be found in [14].…”

Section: T T T T E C C E U E C X C U S S S S W S S R S S Umentioning

confidence: 99%

State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

Shi

Yao

et al. 2021

THERM SCI

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“…(5) Substitute P and ρ into (49)-(51) to confirm the stability and verify those inequalities. (6) Construct the fuzzy observer in (8) and mixed H 2 /H ∞ FPI controller in (12) according to the pre-selected membership functions and fuzzy plant rules.…”

Section: H 2 Tracking Controller Designmentioning

confidence: 99%

“…A major difference from the hardware methods is that the 'soft' methods provide DFIG with a continuing network support during fault conditions [11]. In those 'software' methods, most control strategies are designed based on linear mode, and a few of weighting methods are proposed to approximate the non-linear model of DFIG, such as Bayes' probability theorem [12] and Takagi-Sugeno (T-S) fuzzy modelling method [13]. However, existing linear approximations cannot eliminate approximation error, which has adverse impacts on control performance.…”

Section: Introductionmentioning

confidence: 99%

Approximate error considered fuzzy proportional–integral control of DFIG with regional pole placement for FRT improvement

Su²,

Zhang

et al. 2017

IET Generation, Transmission & Distribution

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“…The parameters of RPS, included DFIG, transmission lines, transformers, SGs, excitation systems and governors are given in Appendix A. Simulations are performed in comparison with the proposed ADM-MPC, Bayesian probability based decentralized coordinated multiple MPC (BDM-MPC), and rotor-side conventional PI controller. The BDM-MPC has the same control structure with ADM-MPC, however the weighting of BDM-MPC is calculated by using a Bayesian probability based method [28].…”

Section: Dominant Eigenvalue Analysis and Simulationmentioning

confidence: 99%

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

Li¹,

Zhang²,

Lin³

et al. 2016

Journal of Electrical Engineering and Technology

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-For low investment and flexible control, doubly fed induction generator (DFIG) is becoming the dominant type that is been used in the wind farms (WFs) today. The report researches about the rotor-side controller of DFIG where all design is based on the single machine infinite bus (SMIB) model. The interactions between the different generators have not been considered in the SMIB model, and the desired performance cannot be obtained by using the controller based on this model. In this situation, an adaptive decentralized-coordinated multiple model predictive control (ADM-MPC) is proposed. First, the interaction measurement method is developed to obtain the interaction measurement model of DFIG, where the interactions between the different generators have been considered. Next, an adaptive multiple MPC based on the obtained interaction measurement method of DFIG is employed to control the rotor-side converter of DFIG. In order to cope with the stochastic disturbance of wind turbine, the augment state structure is employed to improve the tracking control performance. An artificial neural network (ANN) trained online is employed as a weighting controller to cope with the nonlinearities and large operating range of DFIG. A simple, generic renewable power system (RPS) is used to demonstrate contributions. The results of both dominant eigenvalue analysis and time response simulations are represented to illustrate contributions to system damping and transient stability that the DFIG based WF can make with the proposed ADM-MPC controller.

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Multiple Model Predictive Control: A State Estimation based Approach

Cited by 19 publications

References 10 publications

State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

Approximate error considered fuzzy proportional–integral control of DFIG with regional pole placement for FRT improvement

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

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