Improved fault ride through capability of DFIG-wind turbines using customized dynamic voltage restorer

Abstract High penetration of wind power generation system into grid network has satisfied enormous energy demand and has necessitated the wind power generation system to remain connected with grid network to maintain essential grid stability, even during grid fault conditions. This paper presents a novel hybrid genetic algorithm optimized Elman neural network controller for proton exchange membrane fuel cell supported customized Dynamic Voltage Restorer (DVR) to improve the fault ride-through capability of Doubly Fed Induction Generator (DFIG) based wind power generation systems. The outstanding aspect of the proposed customized DVR is that it has low heat transmission losses, therefore has a better efficiency up to 3% when compared with the conventional energy storage based DVR. The proposed shrewd DVR adequately rides through the fault by improving voltage regulation and prevents wind turbine termination during grid faults. The effectiveness of the customized DVR and proposed control strategy is analyzed for doubly fed induction generator based wind power generation system under symmetrical and unsymmetrical fault conditions. The operation of customized DVR is confirmed using time-domain simulation study carried out in MATLAB/Simulink environment.

[1]  Wei Qiao,et al.  Feed-Forward Transient Current Control for Low-Voltage Ride-Through Enhancement of DFIG Wind Turbines , 2010, IEEE Transactions on Energy Conversion.

[2]  Shaotao Dai,et al.  Enhancing Low-Voltage Ride-Through Capability and Smoothing Output Power of DFIG With a Superconducting Fault-Current Limiter–Magnetic Energy Storage System , 2012, IEEE Transactions on Energy Conversion.

[3]  Sharad W. Mohod,et al.  A STATCOM-Control Scheme for Grid Connected Wind Energy System for Power Quality Improvement , 2010, IEEE Systems Journal.

[4]  G Pannell,et al.  Minimum-Threshold Crowbar for a Fault-Ride-Through Grid-Code-Compliant DFIG Wind Turbine , 2010, IEEE Transactions on Energy Conversion.

[5]  Akbar Maleki,et al.  Optimization of a grid-connected hybrid solar-wind-hydrogen CHP system for residential applications by efficient metaheuristic approaches , 2017 .

[6]  M. Hadi Amini,et al.  A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm , 2017 .

[7]  R Sitharthan,et al.  An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS , 2017 .

[8]  Mohsen Rahimi,et al.  Grid-fault ride-through analysis and control of wind turbines with doubly fed induction generators , 2010 .

[9]  A. T. Holen,et al.  A Norwegian case study on the production of hydrogen from wind power , 2007 .

[10]  M. Geethanjali,et al.  Adaptive protection scheme for smart microgrid with electronically coupled distributed generations , 2016 .

[11]  Stavros A. Papathanassiou,et al.  A review of grid code technical requirements for wind farms , 2009 .

[12]  Wilfried Hofmann,et al.  Modeling and Ride-Through Control of Doubly Fed Induction Generators During Symmetrical Voltage Sags , 2011, IEEE Transactions on Energy Conversion.

[13]  Marc A. Rosen,et al.  Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach , 2017 .

[14]  Akbar Maleki,et al.  Two heuristic approaches for the optimization of grid-connected hybrid solar–hydrogen systems to supply residential thermal and electrical loads , 2017 .

[15]  R. D. Richardson,et al.  Wind energy systems , 1993, Proc. IEEE.

[16]  Ranjit Roy,et al.  A comprehensive review on the grid integration of doubly fed induction generator , 2013 .

[17]  Kit Po Wong,et al.  Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through , 2012, IEEE Transactions on Power Systems.

[18]  M. Rafiee,et al.  Enhancement of DFIG-Wind Turbine’s LVRT capability using novel DVR based Odd-nary Cascaded Asymmetric Multi-Level Inverter , 2017 .

[19]  Isam Janajreh,et al.  Implementation and economical study of HAWT under different wind scenarios , 2015 .

[20]  K. Ravi,et al.  Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using Bacterial Foraging Optimization Algorithm , 2016 .

[21]  V. Suresh Kumar,et al.  Particle swarm optimization (PSO)-based tuning technique for PI controller for management of a distributed static synchronous compensator (DSTATCOM) for improved dynamic response and power quality , 2017 .

[22]  Emanuel Peled,et al.  The Electrochemical Behavior of Alkali and Alkaline Earth Metals in Nonaqueous Battery Systems—The Solid Electrolyte Interphase Model , 1979 .

[23]  Faa-Jeng Lin,et al.  Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive , 2008 .

[24]  Arindam Ghosh,et al.  A unified power quality conditioner (UPQC) for simultaneous voltage and current compensation , 2001 .

[25]  Skender Kabashi,et al.  Simulation the Wind Grid Code Requirements for Wind Farms Connection in Kosovo Transmission Grid , 2012 .

[26]  Mark Gillott,et al.  Modeling of PV generation, battery and hydrogen storage to investigate the benefits of energy storage for single dwelling , 2014 .

[27]  M. Geethanjali,et al.  Application of the superconducting fault current limiter strategy to improve the fault ride-through capability of a doubly-fed induction generator–based wind energy conversion system , 2015, Simul..

[28]  Niels Kjølstad Poulsen,et al.  Modelling and transient stability of large wind farms , 2003 .

[29]  Akbar Maleki,et al.  Design and optimization of autonomous solar-wind-reverse osmosis desalination systems coupling battery and hydrogen energy storage by an improved bee algorithm , 2017, Desalination.

[30]  Leopoldo García Franquelo,et al.  Selective Harmonic Mitigation Technique for Cascaded H-Bridge Converters With Nonequal DC Link Voltages , 2013, IEEE Transactions on Industrial Electronics.

[31]  M. Ahmadi,et al.  Design of a cost-effective wind/photovoltaic/hydrogen energy system for supplying a desalination unit by a heuristic approach , 2016 .

[32]  Carlos Silva,et al.  Dynamic programming and genetic algorithms to control an HVAC system: Maximizing thermal comfort and minimizing cost with PV production and storage , 2017 .

[33]  Akbar Maleki,et al.  Comparative study of artificial intelligence techniques for sizing of a hydrogen-based stand-alone photovoltaic/wind hybrid system , 2014 .

[34]  C. Sundarabalan,et al.  Real coded GA optimized fuzzy logic controlled PEMFC based Dynamic Voltage Restorer for reparation of voltage disturbances in distribution system , 2017 .

[35]  Seppo J. Ovaska,et al.  A modified Elman neural network model with application to dynamical systems identification , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[36]  Dong-Choon Lee,et al.  A Fault Ride-Through Technique of DFIG Wind Turbine Systems Using Dynamic Voltage Restorers , 2011, IEEE Transactions on Energy Conversion.

[37]  Akbar Maleki,et al.  Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications , 2017 .

[38]  Geng Yang,et al.  An LVRT Control Strategy Based on Flux Linkage Tracking for DFIG-Based WECS , 2013, IEEE Transactions on Industrial Electronics.

[39]  B. Kalyan Kumar,et al.  Improving fault ride-through capability of wind generation system using DVR , 2013 .