Virtual Inertia Estimation Method of DFIG-based Wind Farm with Additional Frequency Control

Chen, Pengwei; Qi, Chenchen; Chen, Xin

doi:10.35833/mpce.2020.000908

Cited by 23 publications

(4 citation statements)

References 25 publications

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“…The results of simulation were indicated that the proposed frequency control method is useful to enhance the capability of frequency regulation under several patterns of wind speed and the penetration levels especially for power systems with highly wind generation. In [138], Chen et al introduced a technique for the estimation of the virtual moment of inertia given by a DFIG-based wind farm with additional frequency control. Routh approximation was used to derive the explicit formulation of the virtual moment of inertia for grid connected DFIG-based WECS.…”

Section: Recent Frequency Control Methodsmentioning

confidence: 99%

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Mostafa

El-Hay

Elkholy

2022

Arch Computat Methods Eng

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Wind energy is an effective and promising renewable energy source to produce electrical energy. Wind energy conversion systems (WECS) have been developing on a wide scale worldwide. The expansion of wind energy demand tends to produce high-quality output power in terms of grid integration. Due to the intermittent nature of wind energy, great challenges are found regarding WECS modeling, control, and grid integration. This paper introduces a comprehensive review of WECS and their grid-interface systems based on soft computing methods. To achieve this aim, more than 300 articles are organised and only 160 papers are presented in this review. This is intended to cover a broad range of topics concerning the configurations of WECS, electrical generators, and various topologies of power converters used for control and grid integration. Furthermore, international grid codes for wind energy integration with electric grids, particularly frequency, power factor, and low voltage ride through (LVRT) capability are investigated. The major controller approaches and topologies for grid and generator converters are discussed. Different aspects of modern control of WECS are introduced either for grid-side or generator-side. Moreover, control strategies for maximum power point tracking methods are compared along with methods of frequency control. This review paper introduces a comprehensive and a useful summery for the recent work in literature regarding WECS. Detailed modelling, control, and grid integration along with comparisons and discussion are introduced.

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Section: Recent Frequency Control Methodsmentioning

confidence: 99%

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Mostafa

El-Hay

Elkholy

2022

Arch Computat Methods Eng

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“…Considering the orderly synchronization of frequency-regulated power between the DFIG units and the SG units in the grid, the quantification method needs to be determined according to the inertial response characteristics of the SG units. Equation ( 6) expresses the inertial response principle of an SG unit [30,31], where ∆T e , T m , and T e are the electromagnetic torque increment, mechanical torque, and electromagnetic torque of the SG, respectively, and H g represents the inertial time constant, f is the system frequency, ∆P e is the electromagnetic power increment, and superscript * indicates the per-unit value. Methods for controlling the virtual inertia of DFIG units include the P-D algorithm, curveshifting method, and FFR; among these, the frequency regulation characteristics of the P-D algorithm are most similar to those of SG units, as shown by Equation (7) [32].…”

Section: Quantization Of Virtual Inertia At the Dfig Levelmentioning

confidence: 99%

Wind-Storage Combined Virtual Inertial Control Based on Quantization and Regulation Decoupling of Active Power Increments

2022

Energies

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With the increasing proportion of wind turbines in power grids, they are required to have capabilities of active and efficient virtual inertial response to maintain grid frequency stability. However, the virtual inertial control methods currently used in doubly-fed induction generator (DFIG) units suffer from a secondary frequency drop (SFD) problem. Although the SFD can be inhibited by reducing the active power support strength of the DFIG units during inertia response, it will undoubtedly weaken the virtual inertia of the units. Therefore, how to eliminate the SFD while increasing the virtual inertia of the units is a worthy issue for studying. To solve this issue, a wind-storage combined virtual inertial control system based on quantization and regulation decoupling of active power increments is proposed in this paper. First, by setting the parameters of a proportional–differential (P-D) algorithm, the total active power increments required for virtual inertial response are quantified at the DFIG level. Secondly, a curve-shifting method based on the rate of change of frequency is adopted to adjust the active power output of the DFIG units. Finally, a battery energy storage system (BESS) is used to compensate for the power shortages of the units according to the quantized value of the active power increments. Simulations show that the control method can not only eliminate SFD but also effectively increase the system’s virtual inertia.

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“…However, the mismatch between the fast dynamics of DFIGs and the existing phasor measurement reporting rate is ignored in these studies. Note that DFIGs generally have a small capacity and the dynamic states of their rotor, DC capacitor, AC windings, and filter inductance have relatively low time constants [18]. Consequently, the state prediction for the DSE approaches may be inaccurate, as the DSE time step is large, e.g., consistent with the PMU reporting rate of 50 or 60 samples per second [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Zhu¹,

Liu²,

J³

et al. 2023

Journal of Modern Power Systems and Clean Energy

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Dynamic state estimation (DSE) accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time. This paper proposes a DSE approach for a doubly-fed induction generator (DFIG) with unknown inputs based on adaptive interpolation and cubature Kalman filter (AICKF-UI). DFIGs adopt different control strategies in normal and fault conditions; thus, the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases. Consequently, the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs, which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter. Furthermore, as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs, a large estimation error may occur or the DSE approach may diverge. To this end, in this paper, a local-truncation-error-guided adaptive interpolation approach is developed. Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can ① effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient; ② accurately track the dynamic states and unknown inputs of the DFIG; and ③ deal with various types of system operating conditions such as time-varying wind and different system faults.

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Virtual Inertia Estimation Method of DFIG-based Wind Farm with Additional Frequency Control

Cited by 23 publications

References 25 publications

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Wind-Storage Combined Virtual Inertial Control Based on Quantization and Regulation Decoupling of Active Power Increments

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

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