Variable-Gain Super-Twisting Sliding Mode Damping Control of Series-Compensated DFIG-Based Wind Power System for SSCI Mitigation

Ma, Ronglin; Han, Yaozhen; Pan, Weigang

doi:10.3390/en14020382

Cited by 19 publications

(13 citation statements)

References 35 publications

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“…Table 2 shows the predicted results of power load on working days. In the analysis, the complete power load data of the grid for 100 consecutive working days (excluding weekends) were selected [19]. From the prediction results, the prediction results of random forest and the LSTM algorithm are relatively close, more accurate than the SVM but not as good as the authors' method.…”

Section: Resultsmentioning

confidence: 99%

Application of Improved Deep Learning Method in Intelligent Power System

Liu

2022

International Transactions on Electrical Energy Systems

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In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. The authors’ method predicts the best with 14.8. Conclusion. After verification based on actual load data, in the case of uncertain fluctuations in power load, this method can accurately predict the power load, and the accuracy is higher than that of the more popular methods at present, and it is expected to become an important technical support for solving the core problems of smart grid.

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Section: Resultsmentioning

confidence: 99%

Application of Improved Deep Learning Method in Intelligent Power System

Liu

2022

International Transactions on Electrical Energy Systems

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“…Reference [80] investigated the SSCI in DFIG based wind farms using the Eigenvalue analysis method. [81] proposed the reactance crossover-based technique (RCT) to investigate SSCI in the DFIG system using ERCOT real grid scenarios as a case study.More recent studies regarding the SSCI can be found in [82][83][84][85][86][87][88][89].…”

Section: Sscimentioning

confidence: 99%

Recent development and future trends of resonance in doubly fed induction generator system under weak grid

Din

Zhang

Zheng

et al. 2022

IET Renewable Power Gen

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In recent years, the wind energy conversion system faces significant challenges for stable and secure operations, especially in the weak grid due to high penetration of wind energy into the grid. Doubly fed induction generator (DFIG) is one of the most popular wind turbine generators due to its cost‐effective variable speed and low‐cost partial size power converter. Impedance interaction between series or parallel compensated weak grid and the DFIG system causes sub‐synchronous resonance (SSR) and high‐frequency resonance (HFR). A comprehensive review of the state‐of‐the‐art SSR and HFR in the DFIG system under a weak grid is presented. First, based on features, such as supplementary hardware, linear and non‐linear control strategies, the resonance damping techniques are classified into different types. Then, the advantages and disadvantages of SSR analysis and damping techniques techniques are put forward. Moreover, the latest development of state‐of‐the‐art SSR and HFR analysis and damping techniques are supplemented. Finally, the contemporary and future trends of the SSR and HFR techniques are discussed.

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“…The paper authored by Philippe Enrici, Ivan Meny and Daniel Matt, studied a low-power wind system based on a perma-nent magnet Vernier machine, in association with a PWM rectifier, with a diode bridge rectifier [5]. The second paper [6], authored by Ronglin Ma, Yaozhen Han and Weigang Pan, proposes a novel adaptive super-twisting sliding mode subsynchronous control interaction mitigation method, for series-compensated doubly fed induction generatorbased wind power systems. The last paper, authored by Gianluca Brando, Adolfo Dannier and Ivan Spina, analyzes the performance of a full order sensorless control adaptive observer, for a doubly fed induction generator, in the grid-connected operation of a turbine-based wind generation system [7].…”

Section: Power Converter Of Electric Machines Renewable Energy Systems and Transportationmentioning

confidence: 99%

Special Issue on Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Dannier¹,

Brando²,

Coppola³

2022

Energies

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Variable-Gain Super-Twisting Sliding Mode Damping Control of Series-Compensated DFIG-Based Wind Power System for SSCI Mitigation

Cited by 19 publications

References 35 publications

Application of Improved Deep Learning Method in Intelligent Power System

Application of Improved Deep Learning Method in Intelligent Power System

Recent development and future trends of resonance in doubly fed induction generator system under weak grid

Special Issue on Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

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