MixedH2/H∞pitch control of wind turbine with a Markovian jump model

A robust multivariable strategy for pitch and torque control design of variable-speed variable-pitch wind turbines in the full load region is introduced in this paper. The pitch and torque control loops that share the tracking and active damping of drivetrain torsional mode objectives are designed simultaneously using a novel decomposition scheme. This permits the systematic design of robust multivariable controllers for wind turbines in a manner that facilitates industrial application. We achieve this by making the process of weighting function design fast, intuitive, and simple and by giving the designer a clear insight on the compromising aspects of the various control system objectives. FAST simulation is used to demonstrate application of the method. 1 Nomenclature: I G , I R , generator and rotor moment of inertia; K s , B s , torsional stiffness and damping of drivetrain; q GeAz , q RotAz , generator and rotor azimuth angles; N g , gearbox coefficient; g , r , generator and rotor angular speed; T a , T g , rotor aerodynamic and generator torque; P a , P g , rotor aerodynamic and generator power; T g 0 , g 0 , rated value of generator torque and generator angular speed; a , n a , damping ratio and natural frequency of pitch subsystem; e , n e , damping ratio and natural frequency of electrical subsystem; A, area of rotor; V, wind speed;, pitch angle; , blade tip speed ratio; , air density. 312

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“…For W u g , two and third-order weights are examined, which are derived from the inverse of the suggested filter in (10).…”

Section: Robust Multivariable Designmentioning

confidence: 99%

Mode decomposition approach in robust control design for horizontal axis wind turbines

Poureh

Nobakhti

2019

Wind Energy

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“…In recent years, several control methods are used for the BPC in WECS such as the proportional‐integral‐derivative (PID) controller, state‐feedback controller, H 2 /H ∞ control, sliding mode control, fuzzy logic, neural networks, and model predictive control (MPC) . Among these controllers, the PID controller is used widely in the small and large industrial applications because of its easiest and simplest structure in the implementation .…”

Section: Introductionmentioning

confidence: 99%

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

Elsisi

2019

Wind Energy

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This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency-domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportionalintegral-derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta-heuristic algorithms. KEYWORDS frequency-domain constraints, meta-heuristic algorithms, robust PID controller, wind energy conversion system | INTRODUCTIONRecently, electrical power generation from renewable energy sources (RES) is considered one of the most promising solutions to overcome the energy problems in the world. Furthermore, renewable energy is clean, and it has a low effect on the environment. So new researches in the power systems are focused on the utilizing of renewable energy in electricity generation. 1,2 Among the RES, wind energy is a more effective renewable energy source, and it can produce high power during the conversion into electrical energy. 3,4 However, wind energy produces fluctuated electrical power because of the fluctuations of the wind speed. In addition, it causes many problems in electrical system operation such as generator speed regulation. 5,6 So the wind turbine (WT) speed and its output power must be regulated. The regulation of the WT speed and the output power is developed by varying the pitch angle of its blades. The WT is connected with a blade pitch control (BPC) to enhance the operation of the wind energy conversion system (WECS), and it ensures the energy production limit against the fluctuations of the wind speed. Furthermore, the BPC preserves the quality of the generated power, and it protects the WT from any damaging because of the high wind speed.In recent years, several control methods are used for the BPC in WECS such as the proportional-integral-derivative (PID) controller, 7,8 statefeedback controller, 9 H 2 /H ∞ control, 10 sliding mode control, 11 fuzzy logic, 12 neural networks, 13 and model predictive control (MPC). 14 Among NOMENCLATURE: P, active power (p.u. MW); Q, reactive power (p.u. MVAR); vto, infinite bus voltage (p.u. /journal/we 391 400 ELSISI | CONCLUSIONThe tuning of PID controller gains by a novel AI technique named WOA is suggested in this paper. The proposed method is applied to the BPC of a WT in the WECS. In addition, simple frequency-domain conditions are developed based on Hermite-Biehler theorem to ensure the system stability over the plant parameters uncertai...

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“…, s}). With this procedure, the expectation E w is transformed into a simple sum of all the E w j , which makes an easy way to solve problem (19). In this way, the uncertain SMPC problem can be transformed into a deterministic MPC problem.…”

Section: Control Problem Formulationmentioning

confidence: 99%

“…To solve problem (19), this paper considers the realization and the probability of the disturbance to minimize the quadratic function performance index of state and input. In other words, the common quadratic performance index of scenario j, multiplied by the probability of its realization, is the expectation E w j of scenario j, (j ∈ {1, .…”

Section: Control Problem Formulationmentioning

confidence: 99%

“…For the fault problems of WECSs, in recent years, there is a great deal of research work focusing on the fault-tolerant control of WECS [12][13][14][15][16][17][18][19]. Shahbazi [12] designed a six/five-leg converter to realize active fault-tolerant control for open-circuit switch faults to solve unknown but bounded noise and modeling errors.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

et al. 2018

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Abstract:Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC) fault-tolerant controller with the Conditional Value at Risk (CVaR) objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC) problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

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MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Cited by 24 publications

References 26 publications

Mode decomposition approach in robust control design for horizontal axis wind turbines

Mode decomposition approach in robust control design for horizontal axis wind turbines

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

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