Fuzzy-based approach for power smoothing of a full-converter wind turbine generator using a supercapacitor energy storage

Abstract Wind turbine generators (WTGs) are one of the fastest growing renewable energy source technologies. Due to the nature of wind, power fluctuations of WTGs can cause significant problems in the distribution network. In this study a fuzzy-based approach is proposed for a full-converter WTG coupled with a supercapacitor energy storage system. The fuzzy system is designed to smooth out the wind power fluctuations and also maintain an energy reserve of the supercapacitor for short-term grid disturbances. The fuzzy approach is thoroughly tested and compared with a conventional power smoothing technique and with the case without an energy storage system (ESS). Closed-loop digital simulations showed that the proposed fuzzy scheme enhances wind power smoothing and properly manages the state of charge (SOC) of the supercapacitor during faults in the simulated microgrid. Having less power fluctuations and the availability of the ESS to comply with the low-voltage ride through (LVRT) requirement, the WTG and the microgrid operation were considerably improved.

[1]  L. S. Barros,et al.  An internal model control for enhanced grid-connection of direct-driven PMSG-based wind generators , 2017 .

[2]  Ming Cheng,et al.  Pitch angle control for variable speed wind turbines , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[3]  Marcelo Gustavo Molina,et al.  Improving the Integration of Wind Power Generation Into AC Microgrids Using Flywheel Energy Storage , 2012, IEEE Transactions on Smart Grid.

[4]  Md. Murshadul Hoque,et al.  State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations , 2018, IEEE Access.

[5]  H. Ohsaki,et al.  Back-to-back converter design and control for synchronous generator-based wind turbines , 2012, 2012 International Conference on Renewable Energy Research and Applications (ICRERA).

[6]  Abderrazak Ouali,et al.  A fuzzy logic supervisor for active and reactive power control of a variable speed wind energy conversion system associated to a flywheel storage system , 2009 .

[7]  Evgueniy Entchev,et al.  Hybrid battery/supercapacitor energy storage system for the electric vehicles , 2018 .

[8]  Yung-Ruei Chang,et al.  Intelligent wind power smoothing control with BESS , 2017 .

[9]  Andreas Sumper,et al.  Energy management of flywheel-based energy storage device for wind power smoothing , 2013 .

[10]  Wei Li,et al.  Real-Time Simulation of a Wind Turbine Generator Coupled With a Battery Supercapacitor Energy Storage System , 2010, IEEE Transactions on Industrial Electronics.

[11]  Hany M. Hasanien,et al.  Output power smoothing of wind power plants using self-tuned controlled SMES units , 2020 .

[12]  Tomonobu Senjyu,et al.  A review of output power smoothing methods for wind energy conversion systems , 2013 .

[13]  Om Prakash Mahela,et al.  Comprehensive overview of grid interfaced wind energy generation systems , 2016 .

[14]  Geza Joos,et al.  A Short-Term Energy Storage System for Voltage Quality Improvement in Distributed Wind Power , 2014, IEEE Transactions on Energy Conversion.

[15]  Osama Mohammed,et al.  Energy Storage Technologies for High-Power Applications , 2016, IEEE Transactions on Industry Applications.

[16]  Chee Wei Tan,et al.  A review of maximum power point tracking algorithms for wind energy systems , 2012 .

[17]  Hongxing Yang,et al.  Development of hybrid battery-supercapacitor energy storage for remote area renewable energy systems , 2015 .

[18]  Leon M. Tolbert,et al.  Virtual Synchronous Generator Control of Full Converter Wind Turbines With Short-Term Energy Storage , 2017, IEEE Transactions on Industrial Electronics.

[19]  B. Jonkman Turbsim User's Guide: Version 1.50 , 2009 .

[20]  Eduard Muljadi,et al.  Lithium-Ion Capacitor Energy Storage Integrated With Variable Speed Wind Turbines for Power Smoothing , 2013, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[21]  Charis S. Demoulias,et al.  A combined fault ride-through and power smoothing control method for full-converter wind turbines employing Supercapacitor Energy Storage System , 2014 .

[22]  James M. Keller,et al.  Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation , 2016 .

[23]  Jafar Milimonfared,et al.  Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines , 2014 .

[24]  G. Joos,et al.  Supercapacitor Energy Storage for Wind Energy Applications , 2007, IEEE Transactions on Industry Applications.

[25]  Thiago S. Menezes,et al.  Impact of Green Power Distributed Generation to Voltage Profile and Protection Issues by Different Penetration Levels - A Study Developed on ATP Draw , 2016 .

[26]  Junji Tamura,et al.  Stabilization of PMSG based Wind Turbine under Network Disturbance by using New Buck Controller System for DC-Link Protection , 2016 .

[27]  Gevork B. Gharehpetian,et al.  Review of Flywheel Energy Storage Systems structures and applications in power systems and microgrids , 2017 .

[28]  Z Man,et al.  Fuzzy modelling and tracking control of nonlinear systems , 1997 .

[29]  Seyed Hossein Hosseinian,et al.  A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants , 2014 .

[30]  Yun Zhou,et al.  A Novel State of Charge Feedback Strategy in Wind Power Smoothing Based on Short-Term Forecast and Scenario Analysis , 2017, IEEE Transactions on Sustainable Energy.

[31]  Xiaohan Shi,et al.  Wind Power Fluctuation Smoothing with BESS Considering Ultra-short-term Prediction , 2019, 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia).