Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine

Yang, Bo; Zhang, Xiaoshun; Yu, Tao; Shu, Hongchun; Fang, Zihao

doi:10.1016/j.enconman.2016.10.062

Cited by 338 publications

(146 citation statements)

References 32 publications

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“…Thus far, wind energy conversion system (WECS) plays a very crucial role and becomes very popular because of its elegant merits of cleanness, abundance, and wide distribution . Variable speed wind turbine systems are normally based on either (a) doubly fed induction generator (DFIG) or (b) permanent magnetic synchronous generator (PMSG) .…”

Section: Introductionmentioning

confidence: 99%

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Yang

Shu

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper designs a novel adaptive fractional‐order PID (AFOPID) control of a permanent magnetic synchronous generator (PMSG)‐based wind energy conversion system, which attempts to extract the maximum wind power by using a linear perturbation observer. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, and stochastic wind speed variation is aggregated into a perturbation, which is then estimated in real time by a linear extended‐state observer called high‐gain state and perturbation observer. Besides, the perturbation estimate is used as an auxiliary control signal which is fully compensated by a fractional‐order PID (FOPID) controller to achieve a globally robust control consistency, simple structure and high reliability, as well as an improved tracking performance compared to that of PID control. In addition, AFOPID does not require an accurate PMSG model while only the measurement of d‐axis current and mechanical rotation speed is required, in which parameter is optimally tuned by particle swarm optimization. Four case studies are carried out, including step change of wind speed, low‐turbulence stochastic wind speed, high‐turbulence stochastic wind speed, and generator parameter uncertainties, respectively. Simulation results verify the effectiveness and superiority of AFOPID compared to that of PID, FOPID, and nonlinear control.

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Section: Introductionmentioning

confidence: 99%

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Yang

Shu

et al. 2018

Int Trans Electr Energ Syst

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“…Here, an induction generator and a wind turbine are connected with a mechanical shaft system, which is directly connected to the power grid with its stator and a back-to-back converter with its rotor, respectively. The RSC controller aims to regulate the rotor speed and reactive power; while the grid side converter (GSC) controller attempts to maintain a constant DC link voltage from the variation of rotor power [11]. Note that the modelling of GSC is ignored as this paper focuses on active power regulation.…”

Section: System Modelling Of Dfig-based Wind Turbinementioning

confidence: 99%

“…Consider the control inputs may exceed the admissible capacity of RSC at some operation point, therefore their values must be limited. Here [11]. In addition, the RPC parameters are tabulated in Table 1.…”

Section: Case Studiesmentioning

confidence: 99%

Nonlinear Observer-Based Robust Passive Control of Doubly-Fed Induction Generators for Power System Stability Enhancement via Energy Reshaping

Dong

Li²,

Wu³

et al. 2017

Energies

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Abstract:The large-scale penetration of wind power might lead to degradation of the power system stability due to its inherent feature of randomness. Hence, proper control designs which can effectively handle various uncertainties become very crucial. This paper designs a novel robust passive control (RPC) scheme of a doubly-fed induction generator (DFIG) for power system stability enhancement. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, wind speed randomness, is aggregated into a perturbation, which is rapidly estimated by a nonlinear extended state observer (ESO) in real-time. Then, the perturbation estimate is fully compensated by a robust passive controller to realize a globally consistent control performance, in which the energy of the closed-loop system is carefully reshaped through output feedback passification, such that a considerable system damping can be injected to improve the transient responses of DFIG in various operation conditions of power systems. Six case studies are carried out while simulation results verify that RPC can rapidly stabilize the disturbed DFIG system much faster with less overshoot, as well as supress power oscillations more effectively compared to that of linear proportional-integral-derivative (PID) control and nonlinear feedback linearization control (FLC).

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“…The first two stages are dedicated to exploration and the last one covers the exploitation. The reduced number of search parameters is an important advantage of GWO algorithms reflected in various applications, which include blackout risk prevention in smart grids [13], training multi-layer perceptrons [14], optimization of reactive power dispatch [15], solutions to benchmarks generally used to test optimization algorithms [16], hyperspectral band selection [17], maximum power point tracking [18], and optimal tuning of PI-and PID-fuzzy controllers [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

et al. 2017

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This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.

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Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine

Cited by 338 publications

References 32 publications

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Nonlinear Observer-Based Robust Passive Control of Doubly-Fed Induction Generators for Power System Stability Enhancement via Energy Reshaping

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

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