438 408
Full Length Article
Fusion: Practice and Applications
Volume 12 , Issue 2, PP: 193-205 , 2023 | Cite this article as | XML | Html |PDF

Title

An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System

  Nirmal Kumar Agarwal 1 * ,   Manish Prateek 2 ,   Neeta Singh 3 ,   Abhinav Saxena 4

1  SOES at G D Goenka University-Sohna, Gurugram (Haryana) 122103, India
    (nirmalnitham@gmail.com)

2  SOES at G D Goenka University-Sohna, Gurugram (Haryana) 122103, India
    (manish.prateek@gdgu.org)

3  USAR, Guru Gobind Singh Indraprastha University, (New Delhi) 110032, India
    (neeta.usar@ipu.ac.in)

4  Department of Electrical Engineering, JSS Academy of Technical Education, Noida
    (abhinaviitroorkee@gmail.com)

Full Text Download


Doi   :   https://doi.org/10.54216/FPA.120216

Received: January 25, 2023 Revised: April 28, 2023 Accepted: June 24, 2023

Abstract :

The article presents the design and control of the adaptive neuro fuzzy Inference system (ANFIS) for the wind-driven permanent magnet synchronous generator (PMSG) in the grid connected system. The rectifier and inverter are connected with the PMSG output and the grid for maintaining the voltage at the grid under variable wind operations. Such interconnections have many challenges, like high harmonics at the output and an improper voltage profile. The harmonics are measured in terms of total harmonic distortion (THD). Performance parameters like peak overshoot and settling time of DC link voltage and rotor speed have been measured. The control of the rectifier and inverter has been assessed with the ANFIS and PID controllers. A closed strategic mechanism has been developed for the ANFIS and PID controllers for improving the performance parameters and harmonics.. Finally, it is observed that the peak overshoot (%) and settling time (sec) of the DC link voltage with ANFIS are 5.2% and 2.9 sec, which are found to be less in comparison to the PID controller with the values of 6.1% and 3.8 sec and other existing methods. Similarly, the settling time (sec) of rotor speed with ANFIS is 1.1 sec, which is less than the settling time (2.6 sec) of the PID controller. Another advantage of ANFIS is the reduction of THD (%) of 5.1% with respect to THD (%) of PID controllers of 6.2% and other existing methods. The reduced THD shows the improved version of the voltage profile.

Keywords :

PMSG; Wind; FLC; FO-PID; THD

References :

 

[1]    David Cortes-Vega; Fernando Ornelas-Tellez; Juan Anzurez-Marin,Nonlinear Optimal Control for PMSG-Based Wind Energy Conversion Systems, IEEE Latin America Transactions, Vol.19, Issue.7, pp. 1191 – 1198,July 2021

[2]    Kaiqin Peng, Anqi He, Transient Stability Assessment of Power System with PMSG Involved, IEEE International Conference on Advances in Electrical Engineering and Computer Applications,vol.1, pp.753-756, 2022.

[3]    Carlos Andres Ramos-Paja, Elkin Edilberto Henao-Bravo, Andres Julian Saavedra-Montes, MPPT Solution for Commercial Small Wind Generation Systems with Grid Connection, Energies, vol.16, no.2, pp.719, 2023.

[4]    Huaren Wu,Coordinated Control of an Islanded Microintegrated Energy System with an Electrolyzer and Micro-Gas Turbine, International Journal of Photoenergy, vol.2022, no. 6195807, 2022.

[5]    Jianhang Qian, Zhijie Liu, Ke-Jun Li, Liangzi Li, ZhongLin Guo,Reducing Submodule Capacitance for Modular Multilevel Converter-Based Medium-Voltage Wind Power Converter, Frontiers in Energy Research, vol.10, 2022

[6]    Wang, C., Liu, X., & Chen, Z,Incipient Stator Insulation Fault Detection of Permanent Magnet Synchronous Wind Generators Based on Hilbert–Huang Transformation,IEEE Transactions on Magnetics, vol.50, no.11, pp. 1–4,2014

[7]    J. Arellano-Padilla, M. Sumner, and C. Gerada,Winding condition monitoring scheme for a permanent magnet machine using highfrequency injection, IET Elect. Power Appl., vol. 5, no. 1, pp. 89–99, Jan. 2011.

[8]    K. H. Kim,Simple online fault detecting scheme for short-circuited turn in a PMSM through current harmonic monitoring,IEEE Trans.Ind. Electron., vol. 58, no. 6, pp. 2565–2568, Jun. 2011.

[9]    N. Leboeuf, T. Boileau, B. Nahid-Mobarakeh, N. Takorabet, F. Meibody-Tabar, and G. Clerc, Inductance calculations in permanent magnet motors under fault conditions,IEEE Trans. Magn., vol. 48, no. 10, pp. 2605–2616, Oct. 2012..

[10]  N. Leboeuf, T. Boileau, B. Nahid-Mobarakeh, N. Takorabet, F. Meibody-Tabar, and G. Clerc, Estimating permanent-magnet motor parameters under inter-turn fault conditions,IEEE Trans. Magn.,vol. 48, no. 2, pp. 963–966, Feb. 2012.

[11]  S. Saha, M. E. Haque, C. P. Tan, and M. A. Mahmud,Sensor Fault Resilient Operation of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System, vol.55, no. 4,pp. 4298 – 4308, 2019

[12]  N. R. Ullah, K. Bhattacharya and T. Thiringer, Wind farms as reactive power ancillary service providers—Technical and economic issues, IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 661-672, Sep. 2009.

[13]  E. Prieto-Araujo, A. Junyent-Ferre, D. Lavernia-Ferrer and O. Gomis-Bellmunt,Decentralized control of a nine–phase permanent magnet generator for offshore wind turbines, IEEE Trans. Energy Convers., vol. 30, no. 3, pp. 1103-1112, Sep. 2015.

[14]  V. Yaramasu and B. Wu, Predictive control of a three–level boost converter and an NPC inverter for high–power PMSG–based medium voltage wind energy conversion systems, IEEE Trans. Power Electron., vol. 29, no. 10, pp. 5308-5322, Oct. 2014.

[15] H. H. Alhelou, M. H. Golshan and J. Askari-Marnani, Robust sensor fault detection and isolation scheme for interconnected smart power systems in presence of RER and EVs using unknown input observer, Int. J. Elect. Power Energy Syst., vol. 99, pp. 682-694, 2018.

[16] Yaru Sheng,Chao Li,Hanru Jia,Bin Liu,Bin Li,Tim A. Coombs, Investigation on FRT Capability of PMSG-Based Offshore Wind Farm Using the SFCL, IEEE Transactions on Applied Superconductivity, vol.31, no.8, November 2021

[17]  R. Li, L. Yu and L. Xu, Offshore AC fault protection of diode rectifier unit-based HVDC system for wind energy transmission, IEEE Trans. Ind. Electron., vol. 66, no. 7, pp. 5289-5299, Jul. 2019.

[18]  R. Basak, G. Bhuvaneswari and R. Rahul, Low voltage ride-through of a synchronous generator based variable speed grid-Inferenced wind energy conversion system, IEEE Trans. Ind. Appl., vol. 56, no. 1, pp. 752-762, Oct. 2019.

[19] B. Xiang, Jinhui Luo, Lei Gao, Zhiyuan Liu, Yingsan Geng, Jianhua Wang, Satoru Yanabu Study on the parameter requirements for resistive-type superconducting fault current limiters combined with mechanical DC circuit breakers in hybrid AC/DC transmission grids, IEEE Trans. Power Del., vol. 35, no. 6, pp. 2865-2875, Dec. 2020.

 [20] B. Li, C. Li and F. Guo, Application studies on the active SISFCL in electric transmission system and its impact on line distance protection, IEEE Trans. Appl. Superconductor, vol. 25, no. 2, Apr. 2015

[21] Manju Khari, Rubén González Crespo, Guest editorial of the special issue ‘human-centric, decentralised, and hyper automated cyber-physical systems, Journal of Control and Decision, vol.10.no.1, pp. 1-2,2023.

[22] Manju Khari, Renu Dalal, Efficacious implementation of deep Q-routing in opportunistic network, Soft Comput, vol.27, pp.9459–9477 ,2023.

[23] Manju Khari, Ankit Kumar, Efficient Video Anomaly Detection using Residual Variational Autoencoder, International Conference on Communication System, Computing and IT Applications, Mumbai, India, pp. 50-55,2023

[24] Mohamed Saber, Pushan K. Dutta,’ Uniform and Nonuniform Filter Banks Design Based on Fusion Optimization’, Fusion: Practice and Applications, vol. 09, no. 01. pp. 29-37, 2022

[25] Omar Saad Ahmed, Fay Fadhil, Laith H. Jasim Alzubaidi, Riyadh Al-Obaidi,’ Fusion Processing Techniques and Bio-inspired Algorithm for ECommunication and Knowledge Transfer’, Fusion: Practice and Applications, Vol. 10, No. 01.pp. 143-155, 2023

 

Cite this Article as :
Style #
MLA Nirmal Kumar Agarwal , Manish Prateek, Neeta Singh, Abhinav Saxena. "An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System." Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
APA Nirmal Kumar Agarwal , Manish Prateek, Neeta Singh, Abhinav Saxena. (2023). An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System. Journal of Fusion: Practice and Applications, 12 ( 2 ), 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
Chicago Nirmal Kumar Agarwal , Manish Prateek, Neeta Singh, Abhinav Saxena. "An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System." Journal of Fusion: Practice and Applications, 12 no. 2 (2023): 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
Harvard Nirmal Kumar Agarwal , Manish Prateek, Neeta Singh, Abhinav Saxena. (2023). An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System. Journal of Fusion: Practice and Applications, 12 ( 2 ), 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
Vancouver Nirmal Kumar Agarwal , Manish Prateek, Neeta Singh, Abhinav Saxena. An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System. Journal of Fusion: Practice and Applications, (2023); 12 ( 2 ): 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
IEEE Nirmal Kumar Agarwal, Manish Prateek, Neeta Singh, Abhinav Saxena, An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System, Journal of Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 193-205 (Doi   :  https://doi.org/10.54216/FPA.120216)
↑