Transient stability enhancement of DC-connected DFIG and its converter system using fault protective device

Yan, Shaomin; Zhang, Aimin; Zhang, Hang; Wang, Jianhua; Cai, Bin

doi:10.1007/s40565-017-0333-9

Cited by 13 publications

(7 citation statements)

References 33 publications

(37 reference statements)

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“…The modeling equations of DFIG in rotor reference frame coordinates () DQ − are shown from ( 5) to (8),…”

Section: Letmentioning

confidence: 99%

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An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

Saxena¹,

Kumar²,

Singh³

et al. 2022

IEEE Access

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The article presents the design and development of invasive weed optimization (IWO) for the sensor less speed control of doubly fed induction generator (DFIG) under balanced and unbalanced conditions. A healthy condition represents the balanced condition while unbalancing condition is characterized by unhealthy conditions like three-phase fault, single line to ground fault, the line-to-line fault, and double line to ground fault. The DFIG is driven by wind and integrated with the grid. The advantages associated with IWO technique are a simple mathematical approach and less data computation. Normally, DFIG consists of two back-to-back converters namely grid side converter (GSC) and rotor side converter (RSC). The GSC is an uncontrolled converter while the RSC is a controlled converter. The existing methods have poor performance parameters like settling time, peak overshoot for balanced conditions, and poor power quality parameters like total harmonic distortion (THD) for unbalanced conditions. An IWO technique has been applied to overcome such limitations. The effectiveness of the sensor less speed control is also analyzed with other techniques like Adaptive Neuro-Fuzzy Interference System (AN-FIS) & artificial neural network (ANN). The design of ANN is based on the feed-forward method using back propagation delay and the design of ANFIS is based on adaptive control and state space control strategy. It is observed that performance parameters like peak overshoot and settling time for the sensor less speed of DFIG are found to be more profound with IWO in comparison to ANFIS, ANN, and other existing techniques for balanced conditions. Similarly in the unbalanced condition, faulty current approaches are quite closer to their healthy state with the IWO method in comparison to other methods. In addition to this, minimum distortion (%THD) for the grid current under unbalanced conditions is also attained with IWO in comparison to ANFIS, ANN, and other existing techniques. Such application of IWO makes the system highly efficient and robust.

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“…The modeling equations of DFIG in rotor reference frame coordinates () DQ − are shown from ( 5) to (8),…”

Section: Letmentioning

confidence: 99%

“…The conventional approach for the sensor less control of DFIG for the balanced and unbalanced conditions under the application of solar and wind is also shown in [4]- [7]. Enhancing stability analysis & designing of the optimal controller, improvement of dc-link voltage & maximum power extraction, are also discussed tremendously [8], [9].…”

Section: Introductionmentioning

confidence: 99%

An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

Saxena¹,

Kumar²,

Singh³

et al. 2022

IEEE Access

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“…System Configuration. DFIG with its DC-based converters system in a DC grid is shown in Figure 1 [12]. The stator and the rotor of DFIG are connected to a DC grid through SSC and RSC.…”

Section: Modeling Of Dfig Systemmentioning

confidence: 99%

“…But harmonic may reach from 5.97% to 11.66% [8]. An IGBT-based converters system consisting of a rotor side converter (RSC) and a stator side converter (SSC) connects rotor and stator with DC grid, respectively [9][10][11][12][13]. This structure has the advantages that it can reduce the current harmonic effectively and regulate the stator flux and current flexibly according to the needs of the system, not limited to AC grid.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of DFIG with DC-Based Converter System Using Coordinated Feedback Linearization Control

Sun

Yan

Cai

et al. 2018

Mathematical Problems in Engineering

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This paper presents a coordinated feedback linearization strategy (CFLS) for DC-based doubly-fed induction generator (DFIG) system to track the maximum power point. The stator and rotor of DFIG are connected to DC grid directly by two voltage source converters. Compared with a traditional DFIG system, the DC-based DFIG system has more system inputs and coupling, which increases the difficulty of vector control strategy. Accordingly, CFLS is proposed to make DFIG operate at the maximum power point (MPP), and two aspects are improved: first a single-loop control is adopted to make DFIG operate steady and accurate under coordinated the control of RSC and SSC. Second system control laws are obtained by the feedback linearization strategy that achieves DC-based DFIG system decoupling fully during the MPPT and system control. Simulations are carried out the comparison between CFLS and conventional vector control (VC), and it shows that the control performance of CFLS is superior.

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“…These include reactive power supply, power factor limits, and voltage regulation [5]. In literature, there have been many studies that electricity-generating facilities and consumers remain continuously connected to the electrical power grid system during grid fault such as the crowbar system [6-8], static synchronous compensator (STATCOM) [9,10], static VAR compensator (SVC) [11], series dynamic braking resistor (SDBR) [12], dynamic voltage restorer (DVR) [13,14]. Recently, new modified [15] and new structure [16] models have been proposed for the bridge type fault current limiter (BFCL).…”

Section: Introductionmentioning

confidence: 99%

FRT Capability Enhancement of Wind Turbine Based on DFIG Using Machine Learning

Gencer

2022

NÖHÜ Müh. Bilim. Derg.

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Çift beslemeli asenkron generatörü (ÇBAG), şebeke arızası sırasında meydana gelen yüksek gerilimin ve akımın zararlı etkilerine karşı çok hassastır. Makine öğrenmesi (ML) yöntemlerinden biri olan destek vektör makineye (DVM) dayalı bir kapasitif köprü tipi arıza akım sınırlayıcısı (KKTAAS), üç fazlı arızada geçiş (FRT) performansını iyileştirmek için önerilmiştir. Bu çalışmada, normal şebeke koşullarında çalışan ÇBAG tabanlı bir rüzgâr türbininde oluşabilecek faz-toprak (3LG) simetrik şebeke hatası DVM' ye dayalı makine öğrenimi algoritması hem ÇBAG dönüştürücülerin kontrol sistemlerinde hem de KKTAAS' in bir kontrol sisteminde uygulanmıştır. Rotor tarafında, şebeke tarafında dönüştürücüde ve KKTAAS' in devre topolojisinde kullanılan elektronik anahtarlama elemanlarının anahtarlama sinyallerini üretmek için dört farklı DVM sınıflandırıcı algoritması uygulanmıştır. DVM sınıflandırıcılarının eğitiminde İnce Gauss, Kuadratik, Kübik ve Doğrusal çekirdek fonksiyonları tercih edilmiştir. Geliştirilen DVM’ ler, normal ve şebeke arızası koşulları sırasında dönüştürücülerin davranışlarını doğru tahmin etmek ve karar vermek için uygun şekilde eğitilmiştir. İnce gauss ve Doğrusal DVM türlerinin performansı, ÇBAG’ ye dayalı bir rüzgâr türbini için eğitim verimliliğinin etkinliği ile karşılaştırılmıştır. DVM' in İnce Gaussian' in doğruluk oranı %100’dür, Doğrusal DVM' in doğruluk oranı ise %22'dir. Simülasyon sonuçları, İnce Gaussian DVM' in, ÇBAG tabanlı bir rüzgâr türbini için Doğrusal DVM' ye kıyasla 3LG şebeke hatasının zararlı etkilerinden daha verimli bir şekilde koruduğunu göstermektedir.

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Transient stability enhancement of DC-connected DFIG and its converter system using fault protective device

Cited by 13 publications

References 33 publications

An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

Maximum Power Point Tracking of DFIG with DC-Based Converter System Using Coordinated Feedback Linearization Control

FRT Capability Enhancement of Wind Turbine Based on DFIG Using Machine Learning

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