Abstract
To capture wind energy and to produce electrical power, many conversion systems have been proposed. This work treats also the modeling and the control of dual stator induction generator DSIG integrated in wind energy conversion system. In order to increase the flow of the power to the grid and to ensure an optimum operating point, it is very important to act on the generator side controllers and the conversion system output variables. The Proportional integral PI controllers have been widely used to control alternative machines. In this case, the inverters, which fed the DSIG, are controlled simultaneously with a displaced angle of 30°. So, the synthesis PI gains still difficult. To solve this problem, a nonlinear backstepping control is proposed. For that, the suggested study presents the comparison of the performances of the two strategies. Different simulation tests are conducted to evaluate the efficiency and the validity of the proposed control strategies. We notice that in the steady state, the two controls allow the same performance (tracking). In transient mode, the backstepping command is better in terms of response time and overshoot.
Similar content being viewed by others
References
Ameur F, Kouzi K (2013) Genetic algorithm optimized PI and fuzzy logic speed vector control of dual stator induction generator in wind energy conversion system. In: Proceedings of the 3rd international conference on systems. Algiers, October 29–31
Ameur F, Kouzi K, Ameur A, Kasbadji NM (2016) Robust control of dual stator induction generator used in wind conversion system connectes to the grid usin direct torque control. In: 4th International conference on renewable energy: generation and applications ICREGA’16, At Belfort, Franch
Amimeur H, Aouzellag D, Abdessemed R, Ghedamsi K (2012) Sliding mode control of a dual-stator induction for wind energy conversion systems. Electr Power Energy Syst 42:60–70
Basak S, Chakraborty C, Sinha AK (2014) Dual stator induction generator with controllable reactive power capability. In: 23rd International symposium on industrial electronics (ISIE), IEEE
Bekakra Y, Ben Attous D (2015) Optimizing of IP speed controller using particle swarm optimization for FOC of an induction motor. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-015-0391-1
Bouzidi M, Benaissa A, Barkat S, Bouafia S, Bouzidi A (2016) Virtual flux direct power control of the three-level NPC shunt active power filter based on backstepping control. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-016-0433-3
Bu F, Hu Y, Huang W, Zhuang S, Shi K (2015) Wide-speed-range-operation dual stator-winding induction generator DC generating system for wind power applications. IEEE Trans Power Electron 30(2):561–573
Chekkal S, Aouzellag Lahaçani N, Aouzellag D, Ghedamsi K (2014) Fuzzy logic control strategy of wind generator based on the dual stator induction generator. Electr Power Energy Syst 59:166–175
Elmansouri A, El mhamdi J, Boualouch A (2015) Control by back stepping of the DFIG used in the wind turbine. Int J Emerg Technol Adv Eng 5(2):472–478
Errami Y, Ouassaid M, Maaroufi M (2015) Optimal power control strategy of maximizing wind energy tracking and different operating conditions for permanent magnet synchronous generator wind farm. Energy Procedia 74:477–490
Ghoudelbourk S, Dib D, Omeiri A, Azar AT (2016) MPPT control in wind energy conversion systems and the application of fractional control (PIα) in pitch wind turbine. IntJ Model Identif Control 26(2):140–151
Guediri AK, Ben Attous D (2015) Modeling and fuzzy control of a wind energy system based on double- fed asynchronous machine for supply of power to the electrical network. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-015-0367-1
Herizi A, Balla R, Ahmani SA (2015) Backstepping control of induction motors. El Wahat pour les Recherches et les Etudes 8:132–145
Hossain MM, Mohd HA (2015) Future research directions for the wind turbine generator system. Renew Sustain Energy Rev 49:481–489
Kammoun S, Sallem S, Kammoun MBK (2017) Backstepping control for low-voltage ride through enhancement of DFIG-based wind turbines. Arab J Sci Eng. https://doi.org/10.1007/s13369-017-2606-z
Kumar D, Chatterjee K (2016) A review of conventional and advanced MPPT algorithms for wind energy systems. Renew Sustain Energy Rev 55:957–970
Lekhchine S, Bahi T, Soufi Y (2014) Indirect rotor field oriented control based on fuzzy logic controlled double star induction machine. Electr Power Energy Syst 57:206–211
Mahboub MA, Drid S, Sid MA, Cheikh R (2016) Sliding mode control of grid connected brushless doubly fed induction generator driven by wind turbine in variable speed. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-016-0524-1
Mehammai C, Zidani F, Benaicha S, Nait-Said MS (2014) Research on improvement of FOC system for induction motor using fuzzy logic. Int J Modell Identif Control 21(4):370–377
Miryousefi Aval SM, Ahadi A, Hayati H (2015) A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation. Front Energy. https://doi.org/10.1007/s11708-015-0384-4
Pandit JK, Aware MV, Levi RE (2016) Direct torque control scheme for a six-phase induction motor with reduced torque ripple. IEEE Trans Power Electron. https://doi.org/10.1109/TPEL.2016.2624149
Taheri A (2016) Harmonic reduction of direct torque control of six-phase induction motor. ISA Trans. https://doi.org/10.1016/j.isatra.2016.02.014i
Tamaarat A, Benakcha A (2014) Performance of PI controller for control of active and reactive power in DFIG operating in a grid-connected variable speed wind energy conversion system. Front Energy 8(3):371–378
Taraft S, Rekioua D, Aouzellag D (2013) Wind power control system associated to the flywheel energy storage system connected to the grid. Energy Procedia 36:1147–1157
Tiwari R, Babu NR (2016) Recent developments of control strategies for wind energy conversion system. Renew Sustain Energy Rev 66:268–285
Tria FZ, Srairi K, Benchouia MT, Benbouzid MEH (2017) An integral sliding mode controller with super-twisting algorithm for direct power control of wind generator based on a doubly fed induction generator. DOI, Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-017-0597-5
Author information
Authors and Affiliations
Corresponding author
Appendix: Parameters
Appendix: Parameters
- Radius of the turbine:
-
R = 36 m
- Gear box gain:
-
G = 90
- Maximum power coefficient:
-
\({\text{C}}_{\text{pmax}} = 0.44\)
- Optimal relative wind speed:
-
\({\lambda }_{\text{opt}} = 7.05\)
- DSIG nominal power:
-
\({\text{P}}_{\text{n}} = 1.5\;{\text{MW}}\)
- RMS voltage value:
-
U = 400 V
- Frequency:
-
F = 50 Hz
- Number of pole pairs:
-
\({\text{p}}\) = 2
- Stator resistance:
-
\({\text{R}}_{{{\text{s}}1}}\) = \({\text{R}}_{{{\text{s}}2}}\) = 0.008 Ω
- Stator inductance:
-
\({\text{L}}_{{{\text{s}}1}}\) = \({\text{L}}_{{{\text{s}}2}}\) = 0.134 \({\text{mH}}\)
- Magnetizing inductance:
-
\({\text{L}}_{\text{m}}\) = 0.0045 H
- Rotor resistance:
-
\({\text{R}}_{\text{r}}\) = 0.007 Ω
- Rotor inductance:
-
\({\text{L}}_{\text{r}}\) = 0.067 \({\text{mH}}\)
- Inertia:
-
J = 10 kg.m2
- Viscous coefficient:
-
f = 2.5 Nm s/rd
- Filter inductance:
-
\({\text{L}}_{\text{f}} = 0.001 \;{\text{H}}\)
- Filter resistance:
-
\({\text{R}}_{\text{f}} = 0.01 \;\varOmega\)
- Capacitance of the DC link voltage:
-
\({\text{C}} = 0.072 \;{\text{F}}\)
Rights and permissions
About this article
Cite this article
Benakcha, M., Benalia, L., Ammar, A. et al. Wind energy conversion system based on dual stator induction generator controlled by nonlinear backstepping and pi controllers. Int J Syst Assur Eng Manag 10, 499–509 (2019). https://doi.org/10.1007/s13198-018-0734-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-018-0734-9