Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints

One of the major factors that can increase the efficiency of wind turbines (WTs) is the simultaneous control of the different parts in several operating area. The main problem associated with control design in wind generator is the presence of asymmetric in the dynamic model of the system, which makes a generic supervisory control scheme for the power management of WT complicated. Consequently, supervisory controller can be utilized as the main building block of a wind farm controller (offshore), which meets the grid code requirements and can increased the efficiency and protection of WTs in (region II and III) at the same time. This paper proposes a new effective adaptive supervisory controller for the optimal management and protection simultaneously of a hybrid WT, in both regions (II and III). To this end, the second order sliding mode with the adaptive gain super-twisting control law and fuzzy logic control are used in the machine side, batteries side and grid side converters, to achieve four control objectives: (1) control of the rotor speed to track the optimal value; (2) adaptive control (commutative mode) in order to maximum power point tracking (MPPT) or power limit in various regions; (3)regulate the average DC link voltage near to its nominal value;(4) ensure: a smooth regulation with high quality of power supply injected into the grid, a satisfactory power factor correction and a high harmonic performance in relation to the AC source and eliminating the chattering effect. Results of extensive simulation studies prove that the proposed supervisory control system guarantees to track reference signals with a high harmonic performance despite external disturbance uncertainties.

[1]  Wei Wang,et al.  A novel MPPT method for enhancing energy conversion efficiency taking power smoothing into account , 2015 .

[2]  M.E.H. Benbouzid,et al.  Sliding Mode Power Control of Variable Speed Wind Energy Conversion Systems , 2008, 2007 IEEE International Electric Machines & Drives Conference.

[3]  Gabriel Garcera,et al.  Analysis of the control structure of wind energy generation systems based on a permanent magnet synchronous generator , 2013 .

[4]  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 .

[5]  Quanmin Zhu,et al.  Complex System Modelling and Control Through Intelligent Soft Computations , 2016, Studies in Fuzziness and Soft Computing.

[6]  Bhavna Jain,et al.  Control strategies of grid interfaced wind energy conversion system: An overview , 2015 .

[7]  Mohamed El Hachemi Benbouzid,et al.  Experimental Validation of a Marine Current Turbine Simulator: Application to a Permanent Magnet Synchronous Generator-Based System Second-Order Sliding Mode Control , 2011, IEEE Transactions on Industrial Electronics.

[8]  Jangmyung Lee,et al.  A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM , 2011, IEEE Transactions on Industrial Electronics.

[9]  Krzysztof Patan,et al.  A neural network-based robust unknown input observer design: Application to wind turbine , 2015 .

[10]  Ehsanolah Assareh,et al.  A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm , 2015 .

[11]  Jiahui Wang,et al.  Output-Feedback Based Sliding Mode Control for Fuzzy Systems With Actuator Saturation , 2016, IEEE Transactions on Fuzzy Systems.

[12]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[13]  Fuad E. Alsaadi,et al.  Nonlinear observer design for PEM fuel cell power systems via second order sliding mode technique , 2015, Neurocomputing.

[14]  Mohammad Monfared,et al.  Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems , 2013 .

[15]  Tejavathu Ramesh,et al.  Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive. , 2015, ISA transactions.

[16]  C. Evangelista,et al.  Multivariable 2-sliding mode control for a wind energy system based on a double fed induction generator , 2012 .

[17]  Chih-Ming Hong,et al.  Sliding Mode Control for Variable-speed Wind Turbine Generation Systems Using Artificial Neural Network , 2014 .

[18]  A. Tapia,et al.  Proportional–Integral Regulator-Based Approach to Wind Farm Reactive Power Management for Secondary Voltage Control , 2007, IEEE Transactions on Energy Conversion.

[19]  Ahmad Taher Azar,et al.  A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage , 2017, Neural Computing and Applications.

[20]  Huijun Gao,et al.  Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach , 2014, Autom..

[21]  Debashisha Jena,et al.  Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine , 2015 .

[22]  A. Betka,et al.  Optimal tracking and robust power control of the DFIG wind turbine , 2013 .

[23]  Djalel Dib,et al.  Effective MPPT technique and robust power control of the PMSG wind turbine , 2015 .

[24]  Irfan-Ullah Awan,et al.  A second order sliding mode control design of a switched reluctance motor using super twisting algorithm , 2012, Simul. Model. Pract. Theory.

[25]  Mohamed Abid,et al.  A Fuzzy-PI control to extract an optimal power from wind turbine , 2013 .

[26]  Sundarapandian Vaidyanathan,et al.  Computational Intelligence Applications in Modeling and Control , 2015, Computational Intelligence Applications in Modeling and Control.

[27]  Luis T. Aguilar,et al.  Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization , 2014 .

[28]  Rasit Ata,et al.  Artificial neural networks applications in wind energy systems: a review , 2015 .

[29]  Andreas Sumper,et al.  Power oscillation damping supported by wind power: A review , 2012 .

[30]  Godpromesse Kenné,et al.  A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction , 2016 .

[31]  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 .

[32]  Hak-Keung Lam,et al.  Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[33]  Haoran Zhao,et al.  Review of energy storage system for wind power integration support , 2015 .

[34]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[35]  Sakti Prasad Ghoshal,et al.  Intelligent fuzzy-based reactive power compensation of an isolated hybrid power system , 2014 .

[36]  Ahmed Al-Salaymeh,et al.  Optimal operation of conventional power plants in power system with integrated renewable energy sources , 2013 .

[37]  Quanmin Zhu,et al.  Advances and Applications in Sliding Mode Control Systems , 2014, Advances and Applications in Sliding Mode Control Systems.

[38]  Ahmad Taher Azar,et al.  Adaptive Sliding Mode Control of the Furuta Pendulum , 2015, Advances and Applications in Sliding Mode Control Systems.

[39]  Ali M. Eltamaly,et al.  Maximum power extraction from wind energy system based on fuzzy logic control , 2013 .

[40]  Ahmad Taher Azar,et al.  Overview of Type-2 Fuzzy Logic Systems , 2012, Int. J. Fuzzy Syst. Appl..

[41]  Sadegh Ebrahimkhani,et al.  Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines. , 2016, ISA transactions.

[42]  Roberto Cárdenas,et al.  Sensorless Control of Doubly-Fed Induction Generators Using a Rotor-Current-Based MRAS Observer , 2008, IEEE Transactions on Industrial Electronics.

[43]  Vishal Verma,et al.  Photovoltaic-grid hybrid power fed pump drive operation for curbing the intermittency in PV power generation with grid side limited power conditioning , 2016 .

[44]  Kjetil Uhlen,et al.  Robust control and analysis of a wind-diesel hybrid power plant , 1994 .

[45]  Waldemar Sulkowski,et al.  Dynamic stability study of an isolated wind-diesel hybrid power system with wind power generation using IG, PMIG and PMSG: A comparison , 2013 .

[46]  Chih-Ming Hong,et al.  Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors , 2013 .

[47]  Zhiwei Gao,et al.  Pitch control for wind turbine systems using optimization, estimation and compensation , 2016 .

[48]  Paul Puleston,et al.  Active and Reactive Power Control for Wind Turbine Based on a MIMO 2-Sliding Mode Algorithm With Variable Gains , 2013, IEEE Transactions on Energy Conversion.

[49]  Mohamed Benbouzid,et al.  Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement. , 2014, ISA transactions.

[50]  Yangquan Chen,et al.  Stabilizing and robust fractional order PI controller synthesis for first order plus time delay systems , 2012, Autom..

[51]  Bala Venkatesh,et al.  Two-layer control scheme for a Supercapacitor Energy Storage System coupled to a Doubly Fed Induction Generator , 2012 .

[52]  Djamel Boukhetala,et al.  Sliding Modes for Fault Tolerant Control , 2015, Advances and Applications in Sliding Mode Control Systems.

[53]  Francisco Jurado,et al.  Improving grid integration of wind turbines by using secondary batteries , 2014 .

[54]  Billel Meghni Non-member,et al.  Effective MPPT technique and robust power control of the PMSG wind turbine , 2015 .

[55]  Wei Li,et al.  Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. , 2015, ISA transactions.

[56]  Jon Andreu,et al.  A novel adaptative maximum power point tracking algorithm for small wind turbines , 2014 .

[57]  Gengyin Li,et al.  Modeling of the Wind Turbine with a Permanent Magnet Synchronous Generator for Integration , 2007, 2007 IEEE Power Engineering Society General Meeting.

[58]  J. Gaubert,et al.  Implementation of a new maximum power point tracking control strategy for small wind energy conversion systems without mechanical sensors , 2015 .

[59]  Ahmad Taher Azar,et al.  Design and Modeling of Anti Wind Up PID Controllers , 2015, Complex System Modelling and Control Through Intelligent Soft Computations.

[60]  P Dananjayan,et al.  A novel robust speed controller scheme for PMBLDC motor. , 2007, ISA transactions.

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

[62]  Y. Sozer,et al.  Simulation Comparisons and Implementation of Induction Generator Wind Power Systems , 2013, IEEE Transactions on Industry Applications.

[63]  Ligang Wu,et al.  Observer-based adaptive sliding mode control for nonlinear Markovian jump systems , 2016, Autom..

[64]  Raúl Sarrias,et al.  Coordinate operation of power sources in a doubly-fed induction generator wind turbine/battery hybrid power system , 2012 .

[65]  S. Bacha,et al.  Energy-Reliability Optimization of Wind Energy Conversion Systems by Sliding Mode Control , 2008, IEEE Transactions on Energy Conversion.

[66]  Milutin Jovanovic,et al.  Generic maximum power point tracking controller for small-scale wind turbines , 2012 .

[67]  Oscar Castillo,et al.  A review on interval type-2 fuzzy logic applications in intelligent control , 2014, Inf. Sci..