VOLTAGE CONTROL OF WIND SYSTEM USING ADAPTIVE FUZZY SLIDING METHOD WITH IOT MONITORING
DOI:
https://doi.org/10.29284/ijasis.9.1.2023.1-9Keywords:
Wind system, fuzzy logic, sliding mode, internet of things, grid system, adaptive control.Abstract
: The idea of employing an Adaptive Fuzzy Sliding (AFS)-operated matrix converter in a grid-connected Wind Energy Conversion System (WECS) for controlling voltage and frequency, with additional support for grid monitoring provided by an Internet of Things (IoT) server. The matrix converter is a power electronic device that facilitates direct voltage and frequency conversion, allowing a variable-speed turbine generator to be connected to the power grid. Matrix converter reliability and efficiency are ensured by the AFS control method's use of adaptive control, fuzzy logic, and sliding mode control. The fundamental function of the control system is to stabilize and guarantee the quality of the electricity being sent from the wind turbine generator to the grid. This system's capabilities are expanded by monitoring the grid status through an IoT server, in addition to controlling voltage and frequency. The system's responsiveness to grid fluctuations, grid faults, and abnormal situations is dynamic due to the constant monitoring of grid status. The AFS control approach, in integration with data from the IoT server, enables adaptive control modifications that facilitate the WES's smooth incorporation into the grid, which in turn helps to preserve grid stability and improves system dependability.
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Y.K. Wu, S.M. Chang and P. Mandal “Grid-connected wind power plants: A survey on the integration requirements in modern grid codes,” IEEE Transactions on Industry Applications, vol. 55, no. 6, pp. 5584-5593, 2019.
H. Abubakr, J.C. Vasquez, K. Mahmoud, M.M. Darwish and J.M. Guerrero“Comprehensive review on renewable energy sources in Egypt—current status, grid codes and future vision,” IEEE Access, vol. 10, pp. 4081-4101, 2022.
M. Gu, L. Meegahapola and K.L. Wong “Coordinated voltage and frequency control in hybrid AC/MT-HVDC power grids for stability improvement,” IEEE Transactions on Power Systems, vol. 36, no. 1, pp. 635-47, 2020.
P. Kou, D. Liang, L. Yu and L. Gao “Nonlinear model predictive control of wind farm for system frequency support,” IEEE Transactions on Power Systems, vol. 34, no. 5, pp. 3547-3561, 2019.
A. Chaithanakulwat, “Development of DC voltage control from wind turbines using proportions and integrals for Three-phase grid-connected inverters,” International Journal of Electrical & Computer Engineering, vol. 10, no. 2, 2020.
A. Fathy and A.M. Kassem “Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine,” ISA transactions, vol. 87, pp.282-96, 2019.
A. Saidi, A. Harrouz, I. Colak, K. Kayisli and R. Bayindir “Performance Enhancement of Hybrid Solar PV-Wind System Based on Fuzzy Power Management Strategy: A Case Study,” International Conference on Smart Grid, 2019, pp. 126-131.
O. Zamzoum, A. Derouich, S. Motahhir, Y. El Mourabit and A. El Ghzizal “Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation,” Journal of cleaner production, vol. 265, 2020, pp. 1-21.
K. Sun, W. Yao, J. Fang, X. Ai, J. Wen and S. Cheng “Impedance modeling and stability analysis of grid-connected DFIG-based wind farm with a VSC-HVDC,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 2, 2019, pp. 1375-1390.
D. Cherifi and Y. Miloud “Hybrid control using adaptive fuzzy sliding mode control of doubly fed induction generator for wind energy conversion system,” Periodica Polytechnica Electrical Engineering and Computer Science, vol. 64, no. 4, 2020, pp. 374-381.
H. Komurcugil, S. Biricik, S. Bayhan and Z. Zhang “Sliding mode control: Overview of its applications in power converters,” IEEE Industrial Electronics Magazine, vol. 15, no. 1, 2020, pp. 40-49.
S. Miqoi, A. El Ougli and B. Tidhaf “Adaptive fuzzy sliding mode based MPPT controller for a photovoltaic water pumping system,” International Journal of Power Electronics and Drive Systems, vol. 10, no. 1, 2019, pp. 414-420.
J. Fei and Y. Zhu “Adaptive fuzzy sliding control of single-phase PV grid-connected inverter,” Plos one, vol. 12, no. 8, 2017, pp. 1-8.
S. Krishnan, P. Umasankar and P. Mohana “A smart FPGA-based design and implementation of grid-connected direct matrix converter with IoT communication,” Microprocessors and Microsystems, vol. 76, 2020, pp. 1-12.
A. H. Markazi, M. Maadani, S. H. Zabihifar and N. Doost-Mohammadi “Adaptive fuzzy sliding mode control of under-actuated nonlinear systems,” International Journal of Automation and Computing, 2018, pp. 364-376.
H. M. Nguyen, H. H. Lee and T. W. Chun “Input power factor compensation algorithms using a new direct-SVM method for matrix converter,” IEEE Transactions on Industrial Electronics, vol. 58, no. 1, 2010, pp. 232-243.
A. S. Kumar, S. Kumar, S. C. Gupta, A. Shrivastava, K. Kumar and R. Jain “IoT Communication for Grid-Tie Matrix Converter with Power Factor Control Using the Adaptive Fuzzy Sliding (AFS) Method,” Scientific Programming, vol. 2022, 2022, pp. 1-11.
S. Karad and R. Thakur “Efficient monitoring and control of wind energy conversion systems using Internet of things (IoT): a comprehensive review,” Environment, development and sustainability, vol. 23, 2021, pp.14197-14214.
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Copyright (c) 2023 Selvam Sambasivam, Merkeb Fitwei
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
