Wind energy conversion systems control using T-S fuzzy modeling
23 Jun 2010-pp 1365-1370
TL;DR: In this article, a T-S fuzzy model is proposed to deal with the intrinsic nonlinear behavior of WECS, and an H8 output feedback controller is designed by taking into account directly wind speed as perturbation.
Abstract: The paper investigates the wind energy conversion systems (WECS) control problem. To deal with the intrinsic nonlinear behavior of WECS, a T-S fuzzy model is proposed. The method assumes that the wind speed is unmeasurable. Then an H8 output feedback controller is designed by taking into account directly wind speed as perturbation. The design conditions are given in linear matrix inequalities (LMI) terms which can be solved efficiently using existing numerical tools. To illustrate the effectiveness of the given algorithm (wind disturbance rejection), the proposed synthesis techniques are applied to a dynamic model of the WECS.
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TL;DR: In this paper, a fuzzy model based multivariable predictive control (FMMPC) for wind turbine generator is proposed to maintain a satisfactory quality of power in high wind speed operating region by reducing mechanical loads.
161 citations
TL;DR: In order to meet power needs, with concern for economics and environment, wind energy conversion is gradually gaining interest as a suitable source of renewable energy to maximize the power extraction from the wind, optimization techniques are used at the various module of a wind farm starting from wind farm design for siting, sizing, optimal placement and sizing of distributed generation (DG) sources, generation scheduling, tuning of PID controller, control of WECS etc as mentioned in this paper.
Abstract: In order to meet power needs, with concern for economics and environment, wind energy conversion is gradually gaining interest as a suitable source of renewable energy To maximize the power extraction from the wind, optimization techniques are used at the various module of a wind farm starting from wind farm design for siting, sizing, optimal placement and sizing of distributed generation (DG) sources, generation scheduling, tuning of PID controller, control of wind energy conversion system (WECS) etc This paper mainly focuses on the optimization algorithms (mostly the swarm based) in relation to integration of the wind farm with the grid The paper here gives a precise idea about different optimization techniques, their advantage and disadvantage with respect to a wind farm This review will enable the researchers to open the mind to explore possible applications in this field as well as beyond this area
85 citations
TL;DR: To cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory.
Abstract: Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties.
48 citations
TL;DR: In this article, a fault-tolerant control strategy for a wind turbine operating at low wind speed is presented, which uses the proportional multiple integral observer (PMIO) as intermediate block between the system and the controller to provide an estimation of the generator and/or rotor speed sensor(s) faults.
46 citations
TL;DR: In this article, an active sensor fault-tolerant control (FTC) strategy is proposed to maintain nominal wind turbine controller without change in both fault and fault-free cases.
27 citations
References
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01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Abstract: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented. The premise of an implication is the description of fuzzy subspace of inputs and its consequence is a linear input-output relation. The method of identification of a system using its input-output data is then shown. Two applications of the method to industrial processes are also discussed: a water cleaning process and a converter in a steel-making process.
18,803 citations
"Wind energy conversion systems cont..." refers methods in this paper
...In order to enlarge the validity domain and to consider the entire nonlinear behavior of WECS, Takagi-Sugeno (T-S) fuzzy modeling can be used [ 17 ]....
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TL;DR: New relaxed stability conditions and LMI- (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems are applied to design problems of fuzzy regulators and fuzzy observers.
Abstract: This paper presents new relaxed stability conditions and LMI- (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems. They are applied to design problems of fuzzy regulators and fuzzy observers. First, Takagi and Sugeno's fuzzy models and some stability results are recalled. To design fuzzy regulators and fuzzy observers, nonlinear systems are represented by Takagi-Sugeno's (TS) fuzzy models. The concept of parallel distributed compensation is employed to design fuzzy regulators and fuzzy observers from the TS fuzzy models. New stability conditions are obtained by relaxing the stability conditions derived in previous papers, LMI-based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions. Other LMI's with respect to decay rate and constraints on control input and output are also derived and utilized in the design procedures. Design examples for nonlinear systems demonstrate the utility of the relaxed stability conditions and the LMI-based design procedures.
1,625 citations
TL;DR: It is shown that the regulators, the fuzzy observers and the H"~ controller designs based on new observers for the T-S fuzzy systems are very practical and efficient.
664 citations
TL;DR: In this article, a simple control scheme is proposed that allows an induction motor to run a wind turbine at its maximum power coefficient, using a standard V/Hz converter and controlling the frequency to achieve the desired power at a given turbine speed.
Abstract: To optimize the power in a wind turbine, the speed of the turbine should be able to vary with the wind speed. A simple control scheme is proposed that will allow an induction motor to run a turbine at its maximum power coefficient. The control uses a standard V/Hz converter and controls the frequency to achieve the desired power at a given turbine speed.
317 citations