Adaptive Non‐Linear Control of UPFC for Stability Enhancement in a Multimachine Power System Operating with a DFIG Based Wind Farm

Routray, Sangram Keshari; Patnaik, R. K.; Dash, P.K.

doi:10.1002/asjc.1473

Cited by 11 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, to implement the desired control law , the measurement of acceleration of both rotor and wind speed is required. However, in practice, the acceleration measurement will not only introduce a large amount of noise and lead to poor performance of the designed controller [], but also increase operational costs []. Therefore, in this study, a high‐gain observer is designed to estimate the rotor acceleration

{\overset{ω}{true}}_{r}

.…”

Section: Controller Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

View full text Add to dashboard Cite

As wind energy becomes one of the fastest growing renewable energy resources, the control of large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable-speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non-affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter-free controller. In addition, a high-gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed-loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.

show abstract

{\overset{ω}{true}}_{r}

.…”

Section: Controller Methodologymentioning

confidence: 99%

“…C q ( λ , β ) is generally determined experimentally and provided by the manufacturers []. In practice, a acceptable numerical approximation can be adopted []

C_{q} false(λ, β false) = \frac{0.5176}{λ} false(\frac{116}{λ_{i}} - 0.4 β - 5 false) e^{- 21 false/ λ_{i}} + 0.0068

where

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1} .

…”

Section: Model Of Vswtmentioning

confidence: 99%

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…The UPFC is also able to improve transient stability and oscillation damping of power system . Therefore, the research on the advanced controller of UPFC has attracted the attention of many researchers …”

Section: Introductionmentioning

confidence: 99%

Global continuous robust finite‐time control design for unified power flow controller

Lei

Fei

Zhang

2018

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary This article proposes a global continuous robust finite‐time control (GCRFTC) for a unified power flow controller (UPFC) to improve the transient stability and oscillation damping of power system. First, by using an appropriate coordinate transformation, the nonlinear system of the UPFC is transformed into a nonlinear port‐controlled dissipative Hamiltonian (PCDH) system. Second, based on the PCDH system, by utilizing the Hamiltonian structural properties and the “energy shaping plus damping injection” technique, a proper form of a Hamiltonian function is obtained for the PCDH system. Third, using the Hamiltonian function method, finite‐time stability criterion and robust control technique, the GCRFTC is designed and theoretically proved, and the chattering phenomena and the high‐order frequencies of the power system are avoided effectively. Lastly, a six‐bus and two power plants power system with a UPFC is used to test the effectiveness and robustness of the GCRFTC. Simulation results show that the speed, overshoot, and settling time of the response and the transient conditions of the GCRFTC are further improved in comparison with that of the robust finite‐time power flow control.

show abstract

“…Over the past several decades, linear and nonlinear control techniques have been successfully proposed and applied in the literature for the control of UPFC based on modern and classical control theories [2][3][4][5][6][7][8][9][10]. However, the main drawback of such techniques is that their application requires the development of mathematical models which are difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Kenné

Kuate

Fombu

et al. 2017

Advances in Electrical Engineering

View full text Add to dashboard Cite

This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

show abstract

Adaptive Non‐Linear Control of UPFC for Stability Enhancement in a Multimachine Power System Operating with a DFIG Based Wind Farm

Cited by 11 publications

References 32 publications

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Global continuous robust finite‐time control design for unified power flow controller

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Contact Info

Product

Resources

About