Model reference adaptive control of STATCOM for grid integration of wind energy systems

This article proposes a model reference adaptive control (MRAC) of static synchronous compensator (STATCOM) for enhancing the integration of driven wind turbine-based self-excited induction generator (SEIG) into electrical grids. An MRAC-based Massachusetts Institute of Technology adaptive mechanism is utilised to control the reactive power flow of grid-integrated SEIG. The voltage-source inverter sinusoidal pulse width modulation of the STATCOM is used to overcome the abnormal operating conditions in the wind energy conversion system. This accordingly increases the low-voltage ride through (LVRT) capability. The abnormal operating conditions are faults occurring at the point of common coupling between the generator and the grid. The proposed MRAC is compared to proportional-integral (PI) controllers tuned by genetic algorithm (GA). To evaluate the proposed control of STATCOM for enhancing grid-integrated SEIG, a part of the Egyptian 220 kV network integrated with Zafarana Wind Energy System is used. The results provide evidence of an efficient and robust proposed adaptive control performance rather than static PI controller whose parameters tuned by GA in the voltage, currents, and wind generator speed responses. Moreover, the proposed controller increases the capability of continuous operation of the wind turbines under different abnormal operating conditions and also increases the LVRT capability.

[1]  A. Abu-Siada,et al.  Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques , 2014, IEEE Transactions on Power Delivery.

[2]  Zhi-Hong Mao,et al.  Maximum Power Point Tracking Using Model Reference Adaptive Control , 2014, IEEE Transactions on Power Electronics.

[3]  Mohamed A. El-Sayed,et al.  Static synchronous compensator sizing for enhancement of fault ride-through capability and voltage stabilisation of fixed speed wind farms , 2014 .

[4]  Ganesh K. Venayagamoorthy,et al.  Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids , 2011, IEEE Computational Intelligence Magazine.

[5]  Nagy I. Elkalashy,et al.  Integrating adaptive control of renewable distributed Switched Reluctance Generation and feeder protection coordination , 2018 .

[6]  M. S. El-Moursi,et al.  Fault ride through capability enhancement for self-excited induction generator-based wind parks by installing fault current limiters , 2011 .

[7]  B. Singh,et al.  Voltage and Frequency Controller for a Three-Phase Four-Wire Autonomous Wind Energy Conversion System , 2008, IEEE Transactions on Energy Conversion.

[8]  Jiaqi Liang,et al.  Intelligent Local Area Signals Based Damping of Power System Oscillations Using Virtual Generators and Approximate Dynamic Programming , 2013, IEEE Transactions on Smart Grid.

[9]  Josep M. Guerrero,et al.  Tuning of Synchronous-Frame PI Current Controllers in Grid-Connected Converters Operating at a Low Sampling Rate by MIMO Root Locus , 2015, IEEE Transactions on Industrial Electronics.

[10]  Zhe Chen,et al.  Load mitigation of unbalanced wind turbines using PI-R individual pitch control , 2015 .

[11]  Devarajan Nanjundappan,et al.  Enhancement of transient stability of distribution system with SCIG and DFIG based wind farms using STATCOM , 2016 .

[12]  Jon Are Suul,et al.  STATCOM-Based Indirect Torque Control of Induction Machines During Voltage Recovery After Grid Faults , 2010, IEEE Transactions on Power Electronics.

[13]  M.S. El-Moursi,et al.  Novel STATCOM Controller for Mitigating SSR and Damping Power System Oscillations in a Series Compensated Wind Park , 2010, IEEE Transactions on Power Electronics.

[14]  Jon Are Suul,et al.  Low Voltage Ride Through of Wind Farms With Cage Generators: STATCOM Versus SVC , 2008, IEEE Transactions on Power Electronics.

[15]  Yong Min,et al.  Analysis on Applicability Problems of the Aggregation-Based Representation of Wind Farms Considering DFIGs’ LVRT Behaviors , 2016, IEEE Transactions on Power Systems.

[16]  Sukanta Das,et al.  Comparative assessment of two different model reference adaptive system schemes for speed-sensorless control of induction motor drives , 2016 .

[17]  Xiao-Ping Zhang,et al.  Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator , 2007 .

[18]  Mohamed I. Mosaad,et al.  LFC based adaptive PID controller using ANN and ANFIS techniques , 2014 .

[19]  B. Singh,et al.  Analysis and design of STATCOM-based voltage regulator for self-excited induction generators , 2004, IEEE Transactions on Energy Conversion.

[20]  Marta Molinas,et al.  StatCom control at wind farms with fixed-speed induction generators under asymmetrical grid faults , 2013, IEEE Transactions on Industrial Electronics.

[21]  Anders Grauers,et al.  Efficiency of three wind energy generator systems , 1996 .

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

[23]  Massimo Bongiorno,et al.  Novel LVRT Testing Method for Wind Turbines Using Flexible VSC Technology , 2015, IEEE Transactions on Sustainable Energy.

[24]  Paul J. Werbos,et al.  Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities , 2011, IEEE Computational Intelligence Magazine.

[25]  B. Ronner,et al.  Operational experiences of STATCOMs for wind parks , 2009 .

[26]  Janaka Ekanayake,et al.  A three-level advanced static VAr compensator , 1996 .

[27]  Haibo He,et al.  Power System Stability Control for a Wind Farm Based on Adaptive Dynamic Programming , 2015, IEEE Transactions on Smart Grid.

[28]  E.A. DeMeo,et al.  Utility Wind Integration and Operating Impact State of the Art , 2007, IEEE Transactions on Power Systems.