PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Design of a novel control scheme for the operation of the doubly fed induction generator

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The advancement of ocean renewable energy through Tidal Stream Turbines (TSTs) necessitates the use of a variety of computer models to properly evaluate TST efficiency. The Doubly Fed Induction Generator (DFIG) is the most widely utilized Wind Turbine (WT) in the expanding global wind sector. Grid-tied wind energy systems often use the DFIG to meet conventional grid needs including power quality enhancement, grid stability, grid synchronization, power regulation, and fault ride-through. This paper demonstrates the design of a novel control scheme for the operation of the DFIG. The suggested control scheme consisted of an Improved Recurrent Fuzzy Neural Network (IRFNN) and Ant Colony Optimization with Genetic Algorithms (GACOs). A global control system is created and executed to monitor the changeover between the two operating modes. The plant enters a variable speed mode when the tidal speed is low enough, where the system is controlled to ensure that the turbo-generator module functions at peak power extraction efficiency for any specific tidal velocity. The findings demonstrate the system’s superior efficiency, with the highest power extraction provided despite variations in tidal stream input.
Rocznik
Strony
373--392
Opis fizyczny
Bibliogr. 47 poz., rys., tab., wykr., wz.
Twórcy
  • Electrical Engineering Department, National Institute of Technology Patna, (800005) Bihar, India
  • Electrical Engineering Department, National Institute of Technology Patna, (800005) Bihar, India
Bibliografia
  • [1] Benbouzid M.E.H., Titah-Benbouzid H., Zhou Z., Ocean Energy Technologies, In: Abraham M.A. (Ed.), Encyclopedia of Sustainable Technologies, Elsevier, ISBN: 978-0-128-04677-7, pp. 73–85 (2017), DOI: 10.1016/B978-0-12-409548-9.10097-1.
  • [2] Benbouzid Mohamed, Yassine Amirat, Elhoussin Elbouchikhi, Marine Tidal and Wave Energy Converters: Technologies, Conversions, Grid Interface, Fault Detection, and Fault-Tolerant Control, MDPI (2020).
  • [3] Zainol Mohd Zaifulrizal, Nuraihan Ismail, Ismail Zainol, Abu A., Dahalan W., A review on the status of tidal energy technology worldwide, Sci. Int., vol. 29, no. 3, pp. 659–667 (2017).
  • [4] Toumi Sana, Elhoussin Elbouchikhi, Mohamed Benbouzid, Yassine Amirat, Grid fault-resilient control of a PMSG-based tidal stream turbine, Journal of Electrical Systems, vol. 16, no. 4, pp. 429–447 (2020).
  • [5] Che Hang Seng, Emil Levi, Martin Jones, Wooi-Ping Hew, Nasrudin Abd Rahim, Current control methods for an asymmetrical six-phase induction motor drive, IEEE Transactions on Power Electronics, vol. 29, no. 1, pp. 407–417 (2013), DOI: 10.1109/TPEL.2013.2248170.
  • [6] Zhu Donghai, Xudong Zou, Lu Deng, Qingjun Huang, Shiying Zhou, Yong Kang, Inductance-emulating control for DFIG-based wind turbine to ride-through grid faults, IEEE Transactions on power electronics, vol. 32, no. 11, pp. 8514–8525 (2016), DOI: 10.1109/TPEL.2016.2645791.
  • [7] Piyasinghe Lakshan, Zhixin Miao, Javad Khazaei, Lingling Fan, Impedance model-based SSR analysis for TCSC compensated type-3 wind energy delivery systems, IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 179–187 (2014), DOI: 10.1109/TSTE.2014.2362499.
  • [8] Cardenas Roberto, Rubén Peña, Salvador Alepuz, Greg Asher, Overview of control systems for the operation of DFIGs in wind energy applications, IEEE Transactions on Industrial Electronics, vol. 60, no. 7, pp. 2776–2798 (2013), DOI: 10.1109/TIE.2013.2243372.
  • [9] Nian Heng, Yipeng Song, Direct power control of doubly fed induction generator under distorted grid voltage, IEEE Transactions on Power Electronics, vol. 29, no. 2, pp. 894–905 (2013), DOI: 10.1109/TPEL.2013.2258943.
  • [10] Li Longqi, Heng Nian, Lijie Ding, Bo Zhou, Direct power control of DFIG system without phase-locked loop under unbalanced and harmonically distorted voltage, IEEE Transactions on energy conversion, vol. 33, no. 1, pp. 395–405 (2017), DOI: 10.1109/TEC.2017.2741473.
  • [11] El Daoudi, Soukaina, Loubna Lazrak, Mustapha Ait Lafkih, Sliding mode approach applied to sensorless direct torque control of cage asynchronous motor via multi-level inverter, Protection and Control of Modern Power Systems, vol. 5, pp. 1–10 (2020), DOI: 10.1186/s41601-020-00159-7.
  • [12] Sun Dan, Xiaohe Wang, Low-complexity model predictive direct power control for DFIG under both balanced and unbalanced grid conditions, IEEE Transactions on Industrial Electronics, vol. 63, no. 8, pp. 5186–5196 (2016), DOI: 10.1109/TIE.2016.2570201.
  • [13] Cheng Chenwen, Peng Cheng, Heng Nian, Dan Sun, Model predictive stator current control of doubly fed induction generator during network unbalance, IET Power Electronics, vol. 11, no. 1, pp. 120–128 (2018), DOI: 10.1049/iet-pel.2017.0049.
  • [14] Xiong Pinghua, Dan Sun, Backstepping-based DPC strategy of a wind turbine-driven DFIG under normal and harmonic grid voltage, IEEE Transactions on Power Electronics, vol. 31, no. 6, pp. 4216–4225 (2015), DOI: 10.1109/TPEL.2015.2477442.
  • [15] Boubzizi Saïd, Hafedh Abid, Mohamed Chaabane, Comparative study of three types of controllers for DFIG in wind energy conversion system, Protection and Control of Modern Power Systems, vol. 3, no. 1, pp. 1–12 (2018), DOI: 10.1186/s41601-018-0096-y.
  • [16] Chen Junjie, Yi Liu, Wei Xu, Nonparametric predictive current control for standalone brushless doubly fed induction generators, In 2020 International conference on electrical machines (ICEM), IEEE, vol. 1, pp. 2189–2195 (2020), DOI: 10.1109/ICEM49940.2020.9270712.
  • [17] Benbouhenni Habib, Zinelaabidine Boudjema, Abdelkader Belaidi, Intelligent SVM technique of a multi-level inverter for a DFIG-based wind turbine system, International Journal of Digital Signals and Smart Systems, vol. 3, no. 1–3, pp. 4-19 (2019), DOI: 10.1504/IJDSSS.2019.103372.
  • [18] Wang Yingzhao, Slobodan Ðukanović, Nur Sarma, Siniša Djurović, Implementation and performance evaluation of controller signal embedded sensorless speed estimation for wind turbine doubly fed induction generators, International Journal of Electrical Power & Energy Systems, vol. 148, 108968 (2023), DOI: 10.1016/j.ijepes.2023.108968.
  • [19] Tavoosi Jafar, Ardashir Mohammadzadeh, Bahareh Pahlevanzadeh, Morad Bagherzadeh Kasmani, Shahab S. Band, Rabia Safdar, Amir H. Mosavi, A machine learning approach for active/reactive power control of grid-connected doubly fed induction generators, Ain Shams Engineering Journal, vol. 13, no. 2, 101564 (2022): DOI: 10.1016/j.asej.2021.08.007.
  • [20] Sahri Younes, Salah Tamalouzt, Sofia Lalouni Belaid, Seddik Bacha, Nasim Ullah, Ahmad Aziz Al Ahamdi, Ali Nasser Alzaed, Advanced fuzzy 12 dtc control of doubly fed induction generator for optimal power extraction in wind turbine system under random wind conditions, Sustainability, vol. 13, no. 21, 11593 (2021), DOI: 10.3390/su132111593.
  • [21] Alhato Mohammed Mazen, Soufiene Bouallègue, Thermal exchange optimization-based control of a doubly fed induction generator in wind energy conversion systems, Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1252–1260 (2020), DOI: 10.11591/ijeecs.v20.i3.pp1252-1260.
  • [22] Moreira Adson Bezerra, Tárcio André Dos Santos Barros, Vanessa Siqueira De Castro Teixeira, Ramon Rodrigues De Souza, Marcelo Vinicius De Paula, Ernesto Ruppert Filho, Control of powers for wind power generation and grid current harmonics filtering from doubly fed induction generator: Comparison of two strategies, IEEE, Access 7, pp. 32703–32713 (2019), DOI: 10.1109/ACCESS.2019.2899456.
  • [23] Abdelmalek Samir, Ahmad Taher Azar, and Djalel Dib, A novel actuator fault-tolerant control strategy of dfig-based wind turbines using takagi-sugeno multiple models, International Journal of Control, Automation and Systems, vol. 16, pp. 1415–1424 (2018), DOI: 10.1007/s12555-017-0320-y.
  • [24] Venkatesh M., Jagannath Yadav B., A Novel Approach to Optimal Design of PI Controller of Doubly Fed Induction Generator using Particle Swarm Optimization, Journal of Electrical Engineering & Technology, vol. 8, no. 1, pp. 9–16 (2017).
  • [25] Boudjema Zinelaabidine, Rachid Taleb, Youcef Djeriri, Adil Yahdou, A novel direct torque control using second order continuous sliding mode of a doubly fed induction generator for a wind energy conversion system, Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, no. 2, pp. 965–975 (2017), DOI: 10.3906/elk-1510-89.
  • [26] Ghefiri Khaoula, Soufiene Bouallègue, Izaskun Garrido, Aitor J. Garrido, Joseph Haggège, Complementary power control for doubly fed induction generator-based tidal stream turbine generation plants, Energies, vol. 10, no. 7, 862 (2017), DOI: 10.3390/en10070862.
  • [27] Ebrahimkhani Sadegh, Robust fractional order sliding mode control of doubly fed induction generator (DFIG)-based wind turbines, ISA Transactions, vol. 63, pp. 343–354 (2016), DOI: 10.1016/j.isatra.2016.03.003.
  • [28] Alberdi Mikel, Modesto Amundarain, Aitor Garrido, Izaskun Garrido, Neural control for voltage dips ride-through of oscillating water column-based wave energy converter equipped with doubly fed induction generator, Renewable Energy, vol. 48, pp. 16–26 (2012), DOI: 10.1016/j.renene.2012.04.014.
  • [29] Dorigo Marco, Vittorio Maniezzo, Alberto Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29–41 (1996), DOI: 10.1109/3477.484436.
  • [30] Jiang Song, Minjie Lian, Caiwu Lu, Qinghua Gu, Shunling Ruan, Xuecai Xie, Ensemble prediction algorithm of anomaly monitoring based on big data analysis platform of open-pit mine slope, Complexity 2018 (2018), DOI: 10.1155/2018/1048756.
  • [31] Luo Jie, Huiling Chen, Yueting Xu, Hui Huang, Xuehua Zhao, An improved grasshopper optimization algorithm with application to financial stress prediction, Applied Mathematical Modelling, vol. 64, pp. 654–668 (2018), DOI: 10.1016/j.apm.2018.07.044.
  • [32] Ren Zhengru, Roger Skjetne, Zhen Gao, A crane overload protection controller for blade lifting operation based on model predictive control, Energies, vol. 12, no. 1, 50 (2018), DOI: 10.3390/en12010050.
  • [33] Zhang Qian, Huiling Chen, Jie Luo, Yueting Xu, Chengwen Wu, Chengye Li, Chaos enhanced bacterial foraging optimization for global optimization, IEEE Access 6, pp. 64905–64919 (2018), DOI: 10.1109/ACCESS.2018.2876996.
  • [34] Guo Shikai, Rong Chen, Hui Li, Jian Gao, Yaqing Liu, Crowdsourced Web application testing under real-time constraints, International Journal of Software Engineering and Knowledge Engineering, vol. 28, no. 6, pp. 751–779 (2018), DOI: 10.1142/S0218194018500213.
  • [35] Ren Zhengru, Roger Skjetne, Zhiyu Jiang, Zhen Gao, Amrit Shankar Verma, Integrated GNSS/IMU hub motion estimator for offshore wind turbine blade installation, Mechanical Systems and Signal Processing, vol. 123, pp. 222–243 (2019), DOI: 10.1016/j.ymssp.2019.01.008.
  • [36] Guo Shikai, Rong Chen, Miaomiao Wei, Hui Li, Yaqing Liu, Ensemble data reduction techniques and multi-RSMOTE via fuzzy integral for bug report classification, IEEE Access 6, pp. 45934–45950 (2018), DOI: 10.1109/ACCESS.2018.2865780.
  • [37] Wu Daqing, Jiazhen Huo, Gefu Zhang, Weihua Zhang, Minimization of logistics cost and carbon emissions based on quantum particle swarm optimization, Sustainability, vol. 10, no. 10, 3791 (2018), DOI: 10.3390/su10103791.
  • [38] Zhou Yingrui, Taiyong Li, Jiayi Shi, Zijie Qian, A CEEMDAN and XGBOOST-based approach to forecast crude oil prices, Complexity 2019, pp. 1–15 (2019), DOI: 10.1155/2019/4392785.
  • [39] Datta A., Nandakumar S., A survey on bio inspired meta heuristic-based clustering protocols for wireless sensor networks, In IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 263, no. 5, 052026 (2017), DOI: 10.1088/1757-899X/263/5/052026/meta.
  • [40] Zhou Junchao, Zixue Du, Yinghua Liao, Aihua Tang, An optimization design of vehicle axle system based on multiobjective cooperative optimization algorithm, Journal of the Chinese Institute of Engineers, vol. 41, no. 8, pp. 635–642 (2018), DOI: 10.1080/02533839.2018.1534559.
  • [41] Yang Ai-Min, Xiao-Lei Yang, Jin-Cai Chang, Bin Bai, Fan-Bei Kong, Qing-Bo Ran, Research on a fusion scheme of cellular network and wireless sensor for cyber physical social systems, IEEE Access 6, pp. 18786–18794 (2018), DOI: 10.1109/ACCESS.2018.2816565.
  • [42] Liu Yaqing, Xiaokai Yi, Rong Chen, Zhengguo Zhai, Jingxuan Gu, Feature extraction based on information gain and sequential pattern for English question classification, IET Software, vol. 12, no. 6, pp. 520–526 (2018), DOI: 10.1049/iet-sen.2018.0006.
  • [43] Huang Faming, Chi Yao, Weiping Liu, Yijing Li, Xiaowen Liu, Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine, Geomatics, Natural Hazards and Risk, vol. 9, no. 1, pp. 919–938 (2018), DOI: 10.1080/19475705.2018.1482963.
  • [44] Guo Shikai, Yaqing Liu, Rong Chen, Xiao Sun, Xiangxin Wang, Improved SMOTE algorithm to deal with imbalanced activity classes in smart homes, Neural Processing Letters, vol. 50, pp. 1503–1526 (2019), DOI: 10.1007/s11063-018-9940-3.
  • [45] Sun Fengrui, Yuedong Yao, Xiangfang Li, The heat and mass transfer characteristics of superheated steam coupled with non-condensing gases in horizontal wells with multi-point injection technique, Energy, vol. 143, pp. 995–1005 (2018), DOI: 10.1016/j.energy.2017.11.028.
  • [46] Deng Wu, Junjie Xu, Huimin Zhao, An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem, IEEE access, vol. 7, pp. 20281–20292 (2019), DOI: 10.1109/ACCESS.2019.2897580.
  • [47] Lu Kai-Hung, Hsin-Chuan Chen, Chiou-Jye Huang, Zhi-Feng Huang, Design of IRFNN for reconfigured UPFC to power flow control and stability improvement, In 2017 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, vol. 2, pp. 436–443 (2017), DOI: 10.1109/ICMLC.2017.8108959.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c3b15e46-7929-4a21-b43a-dbdce456ae44
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.