Assessing transient response of DFIG based wind turbines during voltage dips regarding main flux saturation and rotor deep-bar effect

Song, Zhanfeng; Xia, Changliang; Shi, Tingna

doi:10.1016/j.apenergy.2010.04.009

Cited by 44 publications

(15 citation statements)

References 27 publications

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“…As the flux is nonlinearly related to the magnetizing current, the conventional DFIG model formulated on the basis of linear magnetization characteristics fails to obtain precise results and causes performance predictions nearly inaccuratein many instances. Hence, more precise study of magnetization characteristics is necessarily needed to include the effect of magnetic saturation in the machine modeling [29]. Figure 2 shows the non-linear relationship between the magnetizing current and the opencircuit voltage.…”

Section: Non-linearity Of Magnetization Characteristicsmentioning

confidence: 99%

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Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Jose¹,

Chattopadhyay²

2019

IJECE

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Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.

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Section: Non-linearity Of Magnetization Characteristicsmentioning

confidence: 99%

“…Accounting for magnetizing flux saturation, voltage equations of DFIG can be rewritten as[29].DIFFERENT METHODS TO FIT MAGANETIZATION CHARACTERISTICSTo fit a given set of data points, different mathematical techniques are available in literature. Here, we propose neuro fuzzy algorithm for modeling the magnetization characteristics.…”

mentioning

confidence: 99%

Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Jose¹,

Chattopadhyay²

2019

IJECE

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“…The DFIG grid-side controller is shown in Fig. 6 (b), which is used to decouple rotor and the grid sides by regulating the capacitor voltage of the back-to-back converter and a current control loop operating at the grid AC voltage stationary reference frame (Song 2010).…”

Section: Wind Turbine and Dfig Controlmentioning

confidence: 99%

Flat tie-line power scheduling control of grid-connected hybrid microgrids

et al. 2018

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KeywordsGrid-connected hybrid microgrids, tie-line power flow, flat power scheduling, hierarchical coordination control, central controller, local controller Acronyms Wind turbine (WT), photovoltaic (PV), energy storage units (ESUs), microgrids (MGs), distributed generation (DG), maximum power point tracking (MPPT), active distribution networks (ADN), active power (P), reactive power (Q), doubly-fed induction generator (DFIG), energy management system (EMS) Nomenclature Parameter DescriptionC Usable capacity of the battery [Ah] Ddutycycle Duty cycle of the DC/DC converter [%] Ibat Battery current [A] ICmin Maximum charge current of the battery [A] IDmax Maximum discharge current of the battery [A] K A proportional coefficient of pitch control [-] PB Active power of ESU [W] Pgl Output power of MG [W] Pinv Output active power of PV-inverter [W] Pmpp Maximum output power of PV at certain irradiation [W] PPV Active power of PV [W] Pr Active power of DFIG rotor-side [W] PREF Active power reference of PV-inverter [W] Ps Active power of DFIG stator-side [W] PT Average power during the dispatching time T [W] * Output power reference of MG during T [W] PW Active power of WT [W] Pw * Power reference of WT [W] Pwmax* Maximum power at a certain wind speed [W] QREF SoC Reactive power reference of PV-inverter [var] State of charge of the battery SoC(0) T Initial SoC of the battery [%] Dispatching time [s] v Wind speed [m/s] Vbat Battery voltage [V] VDClink DC bus voltage of the PV array [V] vmax Maximum (cut-out) wind speed [m/s] vmin Minimum (cut-in) wind speed [m/s] Vmpp Maximum power point voltage of the PV array at certain irradiation [V] VOC Vth Open circuit voltage of the PV array [V] Threshold battery voltage [V] WPV Electric energy generated by the PV array [Ws] τβ Time constant of the pitch actuator [s]  Pitch angle of the WT []  * Pitch angle reference of the WT [] AbstractIn future active distribution networks (ADNs), microgrids (MGs) may have the possibility to control the power dispatched to the ADN by coordinating the output power of their multiple renewable generation units and energy storage units (ESUs). In this way, each MG may support the active distribution network, while promoting the penetration of renewable energy sources in a rational way. In this paper, we propose a tie-line power flow control of a hybrid MG, including photovoltaic (PV) generator, small wind turbines (WT), and ESUs.Firstly, the structure of the hybrid PV/WT/ESU MG is presented. In this power architecture, the battery is directly connected to the PV side through a DC/DC converter, thus reducing the number of conversions. Secondly, a hierarchical control is proposed to coordinate all those elements of the MG, making the tie-line power flow constant for a period of time, e.g. 15 minutes. Also, a method to calculate the tie-line power flow to be exchanged between the MG and the ADN is explored, and a power ramp rate is given between different dispatch intervals. Finally, a simulation model of the hybrid MG is built and tested. Simula...

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“…Large scale disconnections will further weaken the grid and cause a considerable impact on the stable grid operation. Therefore, many countries are revising their grid codes in order to keep wind turbines connected to the grid and provide reactive power compensation to the grid in the case of voltage dips [12]. Considerable research has been done on dynamic behavior and ride-through capability of DFIG-based wind turbines under grid faults.…”

Section: Introductionmentioning

confidence: 99%

Proportional-Resonant Control of Doubly-Fed Induction Generator Wind Turbines for Low-Voltage Ride-Through Enhancement

et al. 2012

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Abstract:A novel control strategy is proposed in this paper for the rotor side converter (RSC) of doubly-fed induction generator (DFIG)-based wind power generation systems. It is supposed to enhance the low-voltage ride-through (LVRT) capability of DFIGs during great-level grid voltage dips. The strategy consists of a proportional-resonant (PR) controller and auxiliary PR controllers. The auxiliary controllers compensate the output voltage of the RSC in case of grid faults, thus limiting the rotor inrush current of DFIG and meeting the requirements of LVRT. Sequential-component decompositions of current are not required in the control system to improve the response of system. Since the resonant compensator is a double-side integrator, the auxiliary controllers can be simplified through coordinate transformation. The feasibility of the control strategy is validated by simulation on a 1.5 MW wind-turbine driven DFIG system. The impact of the RSC converter voltage rating on the LVRT capability of DFIG is investigated. Meanwhile, the influence of angular frequency detection and control parameters are also discussed. Compared with traditional vector control schemes based on PI current controllers, the presented control strategy effectively suppress rotor current and reduce oscillations of DFIG power and torque under grid faults. OPEN ACCESS

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Assessing transient response of DFIG based wind turbines during voltage dips regarding main flux saturation and rotor deep-bar effect

Cited by 44 publications

References 27 publications

Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Flat tie-line power scheduling control of grid-connected hybrid microgrids

Proportional-Resonant Control of Doubly-Fed Induction Generator Wind Turbines for Low-Voltage Ride-Through Enhancement

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