Nonlinear Controllers Based on Exact Feedback Linearization for Series-Compensated DFIG-Based Wind Parks to Mitigate Sub-Synchronous Control Interaction

Li, Penghan; Wang, Jie; Xiong, Liansong; Wu, Fei

doi:10.3390/en10081182

Cited by 16 publications

(11 citation statements)

References 28 publications

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“…This connection is made via back-to-back voltage source converters. In this connection, via nonlinear control strategies such as passivity-based control [24,25], sliding mode control [26,27] or feedback linearization [28,29], among others, it is possible to control the active and reactive power flow exchanged between the AC grid and the wind turbine system independently. Based on Figure 1, and considering that the power losses in the power conversion system and transformer are negligible, the reactive power inequality constraint reported in (6) is reached as follows: first, we assume that when the maximum active power is obtained from the wind turbine system the power conversion system works at 90%, we name this factor as η, which implies that under this condition, if a wind turbine is located at node i, the per unit representation is as follows:…”

Section: Mathematical Modelingmentioning

confidence: 99%

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

et al. 2020

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This paper presents an optimization model for the optimal placement and sizing of wind turbines, considering their reactive power capacity, wind speed, and demand curves. The optimization model is nonlinear and is focused on minimizing power losses in AC distribution networks. Also, paired wind turbine and power conversion systems are treated via chargeability factor η at the peak hour. This factor represents the percentage of usage of the power conversion system in the nominal wind speed conditions, and allows to support reactive power dynamically during all periods of the day as a function of the distribution system requirements. In addition, an artificial neural network is used for short-term forecasting to deal with uncertainties in wind power generation. We assume that the number of wind power distributed generators could be from zero to three generators integrated into the system, considering unit power factors and reactive power injections to follow up the effect of reactive power compensation in the daily operation. The General Algebraic Modeling System (GAMS) is employed to solve the proposed optimization model.

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Section: Mathematical Modelingmentioning

confidence: 99%

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

et al. 2020

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“…The model for DFIG WTs is presented in Equations 5 and 6 with reference to the dq reference frame

({, \begin{array}{l} u_{sd} = R_{s}^{'} i_{sd} - ω_{1} ψ_{sq} + \frac{d}{normald t} ψ_{sd} \\ u_{sq} = R_{s}^{'} i_{sq} + ω_{1} ψ_{sd} + \frac{d}{normald t} ψ_{sq} \\ u_{rd} = R_{r} i_{rd} - ω_{slip} ψ_{rq} + \frac{d}{normald t} ψ_{rd} \\ u_{rq} = R_{r} i_{rq} + ω_{slip} ψ_{rd} + \frac{d}{normald t} ψ_{rq} \end{array})

({, \begin{array}{l} ψ_{sd} = L_{s}^{'} i_{sd} - L_{m} i_{rd} \\ ψ_{sq} = L_{s}^{'} i_{sq} - L_{m} i_{rq} \\ ψ_{rd} = - L_{m} i_{sd} + L_{r}^{'} i_{rd} \\ ψ_{rq} = - L_{m} i_{sq} + L_{r}^{'} i_{rq} \end{array})

where u , i , ψ, R , and L represent the voltage, current, flux linkage, resistance, and inductance; ω 1 indicates the stator angular frequency; ω slip (= ω 1 − ω r ) is the slip angular frequency. L m represents the mutual inductance.…”

Section: System Model and Controlmentioning

confidence: 99%

“…L m represents the mutual inductance.

L_{s}^{'}

L_{r}^{'}

, and

R_{s}^{'}

, are the equivalent stator inductance, equivalent rotor inductance, and equivalent stator resistance considering the impact of series compensator and transmission line, which can be written as follow :

({, \begin{array}{l} R_{s}^{'} = R_{s} + R_{L} \\ L_{s}^{'} = L_{ls} + L_{tr} + L_{L} - 1 / ((), ω_{1}^{2} C_{sc}) \\ L_{r}^{'} = L_{r} - L_{m}^{2} / L_{s}^{'} \end{array})

where C sc is the compensator capacitance; L L is the transmission line inductance; and R L is the transmission line resistance.…”

Section: System Model and Controlmentioning

confidence: 99%

Nonlinear controller based on state feedback linearization for series-compensated DFIG-based wind power plants to mitigate sub-synchronous control interaction

Wang

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper proposes a nonlinear controller based on state feedback linearization (SFL) method to mitigate sub‐synchronous control interaction (SSCI) in series‐compensated doubly fed induction generator (DFIG)‐based wind power plants (WPPs). To make full use of the converter control of DFIG, the SFL controller is developed for both grid side converter (GSC) and rotor side converter (RSC) through four necessary steps, ie, assessment of the feedback linearizability, coordinate transformation, feedback linearization, and derivation of control laws. The stability of the internal dynamics, which is not transformed into linear autonomous subsystems in the design process of the RSC controller, is ensured by the application of zero‐dynamic theory. A 100‐MW DFIG‐based WPP adapted from the IEEE first benchmark model is utilized to evaluate the effectiveness of the SFL damping controller at different wind speeds and compensation levels, and the capability of the proposed controller in mitigating SSCI is compared with a well‐tuned proportional‐integral controller and a conventional SSCI damping controller. The superior performance of the SFL controller is demonstrated at varied operating conditions through frequency scanning analysis, eigenvalue analysis, and electromagnetic transient simulation. Moreover, the robust stability of the proposed controller is guaranteed under parameter uncertainties using μ analysis.

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“…Furthermore, since DFIG mechanical part is not engaged in SSCI, oscillations develop much faster than conventional SSR incidents [8]. Many efforts have been made to address the problem of SSCI [9]- [12]. Eigenvalue analysis and frequency scanning are widely employed to identify the existence of SSCI and explain its primary reason.…”

Section: Introductionmentioning

confidence: 99%

Robust Nonlinear Controller Design for Damping of Sub-Synchronous Control Interaction in DFIG-Based Wind Farms

Wang

Xiong

et al. 2019

IEEE Access

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A robust controller taking advantage of feedback linearization control (FLC) and sliding mode control (SMC) is proposed with an objective of mitigating the sub-synchronous control interaction (SSCI) in series-compensated doubly fed induction generator (DFIG)-based wind farms (WFs). FLC method is employed to obtain the reduced-order model of the studied system and, hence, smaller computation burden to controller design. Moreover, unlike linear control methods, FLC enables the controlled system to be independent of the pre-specified operations, which is suitable for highly nonlinear DFIG-based WFs. Considering that the FLC can be sensitive to parameter uncertainties, SMC is combined with the FLC to improve the robustness of the system. To evaluate the performance of the proposed feedback-linearized sliding mode controller (FLSMC) as compared to the conventional sub-synchronous resonance damping controller, the electromagnetic transient simulation and small-signal stability analysis are carried out. The designed FLSMC is observed to demonstrate the effectiveness in SSCI mitigation and robustness against the parameter perturbation at varied operating conditions. The experimental tests are performed to validate the veracity of the simulation results. * Superscript denoting reference value ω m , ω 1 Rotor shaft angular speed and system angular line frequency e q , e d ; u q , u d Grid voltage and PCC voltage u sc Voltage across series compensator i d , i q Current through transmissions u g , i g Voltage and current of the GSC loop C sc , R L , L L Compensator capacitance, network equivalent resistance, and inductance C dc DC-link capacitance S gq , S gd Switching signals for GSC

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Nonlinear Controllers Based on Exact Feedback Linearization for Series-Compensated DFIG-Based Wind Parks to Mitigate Sub-Synchronous Control Interaction

Cited by 16 publications

References 28 publications

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

Nonlinear controller based on state feedback linearization for series-compensated DFIG-based wind power plants to mitigate sub-synchronous control interaction

Robust Nonlinear Controller Design for Damping of Sub-Synchronous Control Interaction in DFIG-Based Wind Farms

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