LQG Design for Megawatt-Class WECS With DFIG Based on Functional Models' Fidelity Prerequisites

Muhando, Endusa Billy; Senjyu, Tomonobu; Uehara, Akie; Funabashi, Toshihisa; Kim, Chul‐Hwan

doi:10.1109/tec.2009.2025338

Cited by 64 publications

(26 citation statements)

References 25 publications

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“…As a result FRENGA has improved responses well. Fig 5(c) shows that our proposed algorithms have produced power with fewer drop than output power of [30] and [31] algorithms, for example, between 45 to 70 sec. Only in sometimes that wind speed intensely has been reduced, output power downfall for a while, take notice this event is unavoidable.…”

Section: Comparisionmentioning

confidence: 96%

“…Consequently, output power has least variations. Based on Based on a perform ability model, a control strategy is devised for maximizing energy conversion in low to medium winds, and maintaining rated output in above rated winds while keeping tensional torque fluctuations to a minimum in [30]. Control is exercised via collective blade pitch control as well as generator torque control.…”

Section: Comparisionmentioning

confidence: 99%

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Fuzzy Rule Extraction from a trained artificial neural network using Genetic Algorithm for WECS control and parameter estimation

Kasiri

Abadeh

Momeni

et al. 2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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New wind turbines typically turn at variable speed. Thus, pitch control of the blades is generally employed to manage the energy captured throughout operation above and below rated wind speed. In this study, a new Genetic Fuzzy System (GFS) has been successfully executed to extract rules from Neural Network (NN). Fuzzy Rule Extraction from Neural network using Genetic Algorithm (FRENGA) recognizes disturbance wind in turbine input. Thus it generates desired pitch angle control. Consequently, output power has been regulated in the nominal range. Results indicate that the new proposed genetic fuzzy rule extraction system outperforms other existing methods in controlling the output during wind fluctuation.

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Section: Comparisionmentioning

confidence: 96%

Section: Comparisionmentioning

confidence: 99%

Fuzzy Rule Extraction from a trained artificial neural network using Genetic Algorithm for WECS control and parameter estimation

Kasiri

Abadeh

Momeni

et al. 2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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show abstract

“…Usually, w and μ are considered zero-mean Gaussian stochastic process and independent each other [4,3].…”

Section: Lqg Control Designmentioning

confidence: 99%

“…That type of control shows useful properties of good performance and robustness in controller design applied to wind energy converters system [1]. Although the application of LQG control for DFIG wind turbines is not new, recent research report using LQG controllers successfully [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Optimal LQG Controller for Variable Speed Wind Turbine Based on Genetic Algorithms

Barrera-Cárdenas

Molinas

2012

Energy Procedia

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Linear Quadratic Gaussian (LQG) control methodology shows useful properties of good performance and robustness in controller design applied to wind turbine. Typically, in the design procedure LQG method is necessary to select weighting matrices in order to solve the Algebraic Riccati Equations and then get the matrices Kalman Filter gain and optimal state-feedback. In order to optimize a LQG control applied to Double-Fed Induction Generator in wind power system, a Genetic Algorithms (GA) adapted to get the best values of the element of weighting matrices is proposed in this paper. The performance indices ISE and ITSE are a good alternative to obtain the fitness function to design LQG controllers with GA. The simulation results show the high effectiveness of this optimal design method.

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“…Various control synthesis methods have been applied in response to the WECS control problems, such as PI control [4][5][6][7][8][9], LQG control [10,11], or fuzzy control [12,13]. Most of the researches [4][5][6][7][8][9][10][11][12][13], provided controllers are designed around an operating point and are valid only for a particular range of operation which are not covered the whole operating region. Most of the cases, wind velocities are chosen within variations ±1 m/s or ±2 m/s of the rated wind velocity, or below the rated wind velocity, and simulation results are provided within single range of wind velocities.…”

Section: Introductionmentioning

confidence: 99%

Design and Implement a Digital H∞ Robust Controller for a MW-Class PMSG-Based Grid-Interactive Wind Energy Conversion System

et al. 2013

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A digital H ∞ controller for a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. Wind energy is an uncertain fluctuating resource which requires a tight control management. So, it is still an exigent task for the control design engineers. The conventional proportional-integral (PI) control is not ideal during high turbulence wind velocities, and the nonlinear behavior of the power converters. These are raising interest towards the robust control concepts. The robust design is to find a controller, for a given system, such that the closed-loop system becomes robust that assurance high-integrity and fault tolerant control system, robust H ∞ control theory has befallen a standard design method of choice over the past two decades in industrial control applications. The robust H ∞ control theory is also gaining eminence in the WECS. Due to the implementation complexity for the continuous H ∞ controller, and availability of the high speedy micro-controllers, the design of a sample-data or a digital H ∞ controller is very important for the realistic implementation. But there isn't a single research to evaluate the performance of the digital H ∞ controller for the WECS. In this paper, the proposed digital H ∞ controller schemes comprise for the both generator and grid interactive power converters, and the control performances are compared with the conventional PI controller and the fuzzy controller. Simulation results confirm the efficacy of the proposed method Energies 2013, 6 2085 which are ensured the WECS stabilities, mitigate shaft stress, and improving the DC-link voltage and output power qualities.

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LQG Design for Megawatt-Class WECS With DFIG Based on Functional Models' Fidelity Prerequisites

Cited by 64 publications

References 25 publications

Fuzzy Rule Extraction from a trained artificial neural network using Genetic Algorithm for WECS control and parameter estimation

Fuzzy Rule Extraction from a trained artificial neural network using Genetic Algorithm for WECS control and parameter estimation

Optimal LQG Controller for Variable Speed Wind Turbine Based on Genetic Algorithms

Design and Implement a Digital H∞ Robust Controller for a MW-Class PMSG-Based Grid-Interactive Wind Energy Conversion System

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