Skip to main content

Advertisement

Log in

RETRACTED ARTICLE: Application of semantic speech recognition in designing of robust adaptive model for DFIG wind energy conversion system

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

This article was retracted on 10 October 2022

This article has been updated

Abstract

Application of semantic speech recognition in designing of robust adaptive model for the DFIG wind energy conversion system is proposed in this paper. In order to reduce the labor intensity and environmental impact of the monitoring personnel and improve the speed and efficiency of the wind energy censuses, and reduce the cost of wind energy censuses, an intelligent wireless wind energy monitor with the speech analytic framework is designed, which is a convenient solution to these difficulties. In the speech recognition section, we have two major novelties. (1) In order to ensure the accuracy of substitution, this paper uses phonemes as the basic unit for the substitution of domain words. (2) The Euclidean distance in the feature space is equivalent to the cosine distance. In the test phase, complex similarity can be directly used to calculate scores. We use the Labview to implement the system, and the robustness test is done. The expeirment setting is based on the latest methodology. Through the experiment, after comparing with modern state-of-the-art methodologies, the performance of the proposed model is verified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Change history

References

  • Abolvafaei, M., & Ganjefar, S. (2020). Maximum power extraction from fractional order doubly fed induction generator based wind turbines using homotopy singular perturbation method. International Journal of Electrical Power & Energy Systems, 119, 105889.

    Article  Google Scholar 

  • Acuña, L. G., Lake, M., Padilla, R. V., Lim, Y. Y., Ponzón, E. G., & Too, Y. C. S. (2018). Modelling autonomous hybrid photovoltaic-wind energy systems under a new reliability approach. Energy Conversion and Management, 172, 357–369.

    Article  Google Scholar 

  • Bakshi, S., Sa, P. K., Wang, H., Barpanda, S. S., & Majhi, B. (2018). Fast periocular authentication in handheld devices with reduced phase intensive local pattern. Multimedia Tools and Applications, 77(14), 17595–17623.

    Article  Google Scholar 

  • Boeddeker, C., Erdogan, H., Yoshioka, T., & Haeb-Umbach, R. (2018). Exploring practical aspects of neural mask-based beamforming for far-field speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6697–6701). IEEE.

  • El Akhrif, R., Ahmed, A., Chaymae, L., & Mohamed, F. (2019). dSPACE implementation for a proportional–integral-based root mean square voltage controller used in stand-alone wind energy conversion systems. Wind Engineering, 43(4), 404–419.

    Article  Google Scholar 

  • Elaimani, H., Essadki, A., Elmouhi, N., & Chakib, R. (2019). The modified sliding mode control of a doubly fed induction generator for wind energy conversion during a voltage dip. In 2019 international conference on wireless technologies, embedded and intelligent systems (WITS) (pp. 1–6). IEEE.

  • Fathabadi, H. (2018). Plug-in hybrid electric vehicles: replacing internal combustion engine with clean and renewable energy based auxiliary power sources. IEEE Transactions on Power Electronics, 33(11), 9611–9618.

    Article  Google Scholar 

  • Hadian, H., Sameti, H., Povey, D., & Khudanpur, S. (2018). End-to-end speech recognition using lattice-free MMI. Interspeech, pp. 12–16.

  • Hamzeh, A., & Awad, M. (2020). Wind power generation in Jordan: Current situation and future plans. The age of wind energy (pp. 63–77). Cham: Springer.

    Chapter  Google Scholar 

  • Kadri, A., Marzougui, H., Aouiti, A., & Bacha, F. (2020). Energy management and control strategy for a DFIG wind turbine/fuel cell hybrid system with super capacitor storage system. Energy, 192, 116518.

    Article  Google Scholar 

  • Krpan, M., & Kuzle, I. (2020). Dynamic characteristics of virtual inertial response provision by DFIG-based wind turbines. Electric Power Systems Research, 178, 106005.

    Article  Google Scholar 

  • Li, B., Sainath, T. N., Sim, K. C., Bacchiani, M., Weinstein, E., Nguyen, P., et al. (2018). Multi-dialect speech recognition with a single sequence-to-sequence model. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4749–4753). IEEE.

  • Li, B., Zhao, H., Gao, S., & Hu, S. (2020). Digital real-time co-simulation platform of refined wind energy conversion system. International Journal of Electrical Power & Energy Systems, 117, 105676.

    Article  Google Scholar 

  • Mazouz, F., Belkacem, S., Colak, I., Drid, S., & Harbouche, Y. (2020). Adaptive direct power control for double fed induction generator used in wind turbine. International Journal of Electrical Power & Energy Systems, 114, 105395.

    Article  Google Scholar 

  • Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679–3689.

    Article  Google Scholar 

  • Nobela, O. N., Bansal, R. C., & Justo, J. J. (2019). A review of power quality compatibility of wind energy conversion systems with the South African utility grid. Renewable Energy Focus, 31, 63–72.

    Article  Google Scholar 

  • Pang, B., Dai, H., Li, F., & Nian, H. (2020). Coordinated control of RSC and GSC for DFIG system under harmonically distorted grid considering inter-harmonics. Energies, 13(1), 28.

    Article  Google Scholar 

  • Peña Asensio, A., Arnaltes Gómez, S., Rodriguez-Amenedo, J., García Plaza, M., Eloy-García Carrasco, J., & Alonso-Martínez de las Morenas, J. (2018). A voltage and frequency control strategy for stand-alone full converter wind energy conversion systems. Energies, 11(3), 474.

    Article  Google Scholar 

  • Pundak, G., Sainath, T. N., Prabhavalkar, R., Kannan, A., & Zhao, D. (2018). Deep context: End-to-end contextual speech recognition. In 2018 IEEE spoken language technology workshop (SLT) (pp. 418–425). IEEE.

  • Rached, B., Elharoussi, M., & Abdelmounim, E. (2019). Fuzzy logic control for wind energy conversion system based on DFIG. In 2019 International conference on wireless technologies, embedded and intelligent systems (WITS) (pp. 1–6). IEEE.

  • Rauf, A. M., Khadkikar, V., & El Moursi, M. S. (2019). A new fault ride-through (FRT) topology for induction generator based wind energy conversion systems. IEEE Transactions on Power Delivery, 34(3), 1129–1137.

    Article  Google Scholar 

  • Saihi, L., Berbaoui, B., Glaoui, H., Djilali, L., & Abdeldjalil, S. (2020). Robust sliding mode H∞ controller of DFIG based on variable speed wind energy conversion system. Periodica Polytechnica Electrical Engineering and Computer Science, 64(1), 53–63.

    Article  Google Scholar 

  • Sisay, A., & Jately, V. (2020). Dynamic performance of grid-connected wind farms with and without UPFC: A case study on Ashagoda Wind Farm. Intelligent communication, control and devices (pp. 559–568). Singapore: Springer.

    Chapter  Google Scholar 

  • Sriram, A., Jun, H., Gaur, Y., & Satheesh, S. (2018). Robust speech recognition using generative adversarial networks. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5639–5643). IEEE.

  • Tong, S., Cheng, Z., Cong, F., Tong, Z., & Zhang, Y. (2018). Developing a grid-connected power optimization strategy for the integration of wind power with low-temperature adiabatic compressed air energy storage. Renewable Energy, 125, 73–86.

    Article  Google Scholar 

  • Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A. (2018). The Microsoft 2017 conversational speech recognition system." In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5934–5938). IEEE.

  • Xu, H., Chen, T., Gao, D., Wang, Y., Li, K., Goel, N., et al. (2018). A pruned rnnlm lattice-rescoring algorithm for automatic speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5929–5933). IEEE.

  • Yin, M., Yang, Z., Xu, Y., Liu, J., Zhou, L., & Zou, Y. (2018). Aerodynamic optimization for variable-speed wind turbines based on wind energy capture efficiency. Applied Energy, 221, 508–521.

    Article  Google Scholar 

  • Zhang, Z., Geiger, J., Pohjalainen, J., El-Desoky Mousa, A., Jin, W., & Schuller, B. (2018). Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Transactions on Intelligent Systems and Technology (TIST), 9(5), 49.

    Google Scholar 

  • Zhang, S., Wang, H., & Huang, W. (2017). Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification. Cluster Computing, 20(2), 1517–1525.

    Article  Google Scholar 

Download references

Funding

This funding was provided by Scientific Research Project of Anhui Education Department (Research of Intelligent Building Fire Detection Technology Based on Fuzzy Multi-sensor Information Fusion [Grant Number KJ2018JD13]).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chao Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s10772-022-10005-w

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Wu, C., Hao, J. et al. RETRACTED ARTICLE: Application of semantic speech recognition in designing of robust adaptive model for DFIG wind energy conversion system. Int J Speech Technol 24, 47–56 (2021). https://doi.org/10.1007/s10772-020-09719-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-020-09719-6

Keywords

Navigation