Transactions on Machine Intelligence

Transactions on Machine Intelligence

Design of a Terminal Sliding Mode Controller for Distributed Generation Systems Based on Multi-Agent Systems

Document Type : Original Article

Authors
M. Mazaheri 1
R. Ghasemi 2
1 Master's Student, Electrical Engineering, University of Qom, Qom, Iran
2 Associate Professor, Electrical Engineering, University of Qom, Qom, Iran
10.47176/TMI.2018.57
Abstract
Distributed generation is a new trend in electric power production. These units are placed in substations and distribution feeders near loads, and are used either independently or in parallel with electrical grids. The use of "intelligent agents" refers to agents that must operate robustly in open, unpredictable environments where significant changes may occur. In this paper, wind turbines and photovoltaic systems are considered as distributed generation agents, with each of these elements treated as an individual agent. Given the heterogeneity of these agents, the system is considered heterogeneous, complicating problem-solving. To address this issue and convert it into a homogeneous problem, it is assumed that identical agents have separate leaders, and if all agents are synchronized, the heterogeneous problem can be effectively treated as a homogeneous one. Following the leader is achieved using a terminal sliding mode controller, with the primary advantages of this method being overall system stability, finite-time convergence, and agent synchronization.
Keywords
Multi-Agent Systems
Distributed Generation
Smart Grid
Terminal Sliding Mode Control
Wind Turbine
Photovoltaic
[1]    Xiao, Q., & Huang, Z. K. (2016). Consensus of multi-agent system with distributed control on time scales. Applied Mathematics and Computation, 277, 54-71. https://doi.org/10.1016/j.amc.2015.12.028
[2]    Zhao, L., Yu, J., Lin, C., & Yu, H. (2017). Distributed adaptive fixed-time consensus tracking for second-order multi-agent systems using modified terminal sliding mode. International Journal of Control. https://doi.org/10.1016/j.amc.2017.05.049
[3]    Bidram, A., Lewis, F. L., Davoudi, A., & Ge, S. S. (2013). Adaptive and distributed control of nonlinear and heterogeneous multi-agent systems. 52nd IEEE Conference on Decision and Control. https://doi.org/10.1109/CDC.2013.6760875
[4]    Bidram, A., Davoudi, A., Lewis, F. L., & Guerrero, J. M. (2013). Distributed cooperative secondary control of microgrids using feedback linearization. IEEE Transactions on Power Systems, 28 (3), 3462-3470. https://doi.org/10.1109/TPWRS.2013.2247071
[5]    Lu, X., Lu, R., Chen, S., & Lü, J. (2013). Finite-time distributed tracking control for multi-agent systems with a virtual leader. IEEE Transactions on Circuits and Systems I: Regular Papers, 60 (2), 352-362. https://doi.org/10.1109/TCSI.2012.2215786
[6]    Chen, H.-T., Song, S.-M., & Zhu, Z.-B. (2018). Robust finite-time attitude tracking control of rigid spacecraft under actuator saturation. International Journal of Control, Automation and Systems, 16 (X), 1-15. https://doi.org/10.1007/s12555-016-0768-1
[7]    Liu, H., Cheng, L., Tan, M., & Hou, Z. (2014). Containment control of general linear multi-agent systems with multiple dynamic leaders: A fast sliding mode based approach. IEEE/CAA Journal of Automatica Sinica, 1 (2), 178-187. https://doi.org/10.1109/JAS.2014.7004542
[8]    Wang, X., & Li, S. (2018). Finite-time consensus for disturbed multi-agent systems with unmeasured states via nonsingular terminal sliding-mode control. In Proceedings of the 11th International Conference on Intelligent Robotics and Applications (pp. 134-145). Springer International Publishing. https://doi.org/10.1007/978-3-319-62896-7_11
[9]    Wang, X., & Li, S. (2016). Consensus disturbance rejection control for second-order multi-agent systems via nonsingular terminal sliding-mode control. In 2016 IEEE Conference on Control Applications (pp. 1037-1042). IEEE. https://doi.org/10.1109/CCA.2016.7587912
[10]    Ren, C.-E., & Chen, C. L. P. (2015). Sliding mode leader-following consensus controllers for second-order non-linear multi-agent systems. IET Control Theory & Applications, 9 (11), 1692-1701. https://doi.org/10.1049/iet-cta.2014.0484
[11]    Beainy, A., Maatouk, C., Moubayed, N., & Kaddah, F. (2016). Comparison of different types of generator for wind energy conversion system topologies. In 3rd International Conference on Renewable Energies for Developing Countries (REDEC) (pp. 1-6). IEEE. https://doi.org/10.1109/REDEC.2016.7577535
[12]    Slootweg, J. G., Polinder, H., & Kling, W. L. (2001). Dynamic modelling of a wind turbine with doubly fed induction generator. In 2001 Power engineering society summer meeting. Conference proceedings (Cat. No. 01CH37262) (Vol. 1, pp. 644-649). IEEE.. https://doi.org/10.1109/PESS.2001.970114
[13]    Bao, X., Zhuo, F., Tian, Y., & Tan, P. (2013). Simplified feedback linearization control of three-phase photovoltaic inverter with an LCL filter. IEEE Transactions on Power Electronics, 28 (6), 2739-2752. https://doi.org/10.1109/TPEL.2012.2225076
[14]    Zehfuss, J. G. (1858). Ueber eine gewisse Determinante. Zeitschrift für Mathematik und Physik, 3, 298-301.
[15]    Lewis, F. L., Zhang, H., Hengster-Movric, K., & Das, A. (2013). Cooperative control of multi-agent systems: Optimal and adaptive design approaches. Springer Science & Business Media. https://doi.org/10.1007/978-1-4471-5574-4
Transactions on Machine Intelligence
Volume 1, Issue 2
Spring 2018
Pages 57-69

  • Receive Date 04 March 2018
  • Revise Date 15 May 2018
  • Accept Date 08 June 2018