Multifunctional Control of Wind-Turbine Based Nano-Grid Connected to a Distorted Utility-Grid BTech EEE Academic projects

ABSTRACT:

 This paper proposes a multifunctional control strategy and associated control algorithms for distributed wind-turbine (WT) based nano-grids connected to a distorted utility-grid. The contribution is on a new strategy with innovative control algorithms to coordinate multiple converters for a multitasking operation of the nano-grids. The novelty is on a unique control design with feasibilities:maximizing the generated power from WT, maintaining power quality in both ac– and dc-sides under critical conditions of the power grid, and improving power quality against distortion from local nonlinear loads under a reduced switching frequency. A robust fast-dynamic predictive control method is developed for current controllers to fulfil the multifunction. Unconstrained deadbeat control inputs are derived in twofold targets: ensuring fast dynamic response and significantly reducing both the computation and switching frequency for finite predictive control. The control system is applied on a permanent-magnet synchronous generator (PMSG) WT-based nano-grid connected to a distorted utility-grid. An OPAL-RT-based real-time platform is used for comparative studies among the proportional integration (PI) control, finite predictive control (FS-MPC), and proposed control method. The performance verification exhibits the power quality improvement in both the nano- and utility-grids under critical conditions via high-performed regulation of currents, voltages, reactive power, and rotor speed of the PMSG-WT.

KEYWORDS:

  1. Deadbeat control
  2. Distortion
  3. Finite predictive control
  4. Harmonic
  5. Permanent magnet synchronous generator (PMSG)
  6. Wind energy conversion system
  7. Unbalanced and distorted grid

SOFTWARE: MATLAB/SIMULINK

SCHEMATIC DIAGRAM:

Fig. 1. A generic diagram of a WT-based nano-grid connected to a distribution grid.

EXPECTED SIMULATION RESULTS:

Fig. 2. Rotor-speed dynamic tracking-performances of the WT for maximum power extraction under wind-speed turbulence (7%). (a) The conventional PI control (PI.), (b) The proposed multifunctional control with fast-dynamic response design (Pro.).

Fig. 3. Comparative speed and current regulation of the MSC control between the conventional FCS-MPC (Fcs.) – Left and proposed control method (Pro.) – Right, (a) Comparative dynamic performances under step-change of reference speed and current, (b) The current regulation and average switching frequency.

Fig. 4. Comparative switching behaviors in the MSC control, (a) The conventional FCS-MPC (Fcs.), (b) The proposed control method (Pro.).

Fig. 5. Comparative performances of the grid-injected current regulation in the grid-interface converter under critical disturbances from two-phase-to-ground fault at a medium voltage bus, (a) The PI control, (b) The Pro. control.

Fig. 6. Comparative performances from Matlab/Simulink on the grid-injected current regulation under critical disturbances in the grid-voltages (i.e., harmonics and unbalance), (a) The PI control, (b) The Pro. control.

Fig. 7. Verification results obtained from OPAL-RT-based real-time systems for the current regulation of Pro. under critical utility-grid conditions. (a) Two- phases-to-ground grid-fault (a and b), (b) Unbalanced and harmonic distortion in the utility-grid voltage.

Fig. 8. Power quality compensation of the multifunction control in the nano-grid for distorted local loads. (a) Unbalanced and harmonic compensation for the local load currents, (b) Improvement of grid-voltages and grid-injected currents in a long-feeder connected to the nano-grid, reactive power compensation for the local loads.

CONCLUSION:

In this paper, a new multifunctional control strategy for WT-based nano-grids was proposed with an innovative direct control method for fast-dynamic response and reduced switching frequency. The approach in a context of a nano-grid enables multitasking operation by coordinating multiple converters in the nano-grid and efficiently utilizes the full-scaled capacity of the PMSG system. The new algorithm combines the fastest digital control (i.e., deadbeat laws) and finite set control to neglect the conventional exhaustive search, complicated prediction, and objective function evaluation while significantly reducing the average switching frequency. Fast-dynamic control components are embedded into conventional local controllers for multitasking operation. The coordination among converters in the nano-grid improves the performance of the WT-based nano-grid on optimal power tracking, voltage and current regulation, dc-voltage regulations, and power quality compensation in the utility-grid. This paper proves the benefits of a new approach by coordinating multiple converters in a broader context of nano-grids in multitasking operation where the rated power of converters can be efficiently exploited in comparison with the conventional grid-connected WT-systems. Comparative results demonstrate the efficacy of the proposed strategy and its potential to extensively apply to various applications in power electronics and power systems.

REFERENCES:

[1] V. Yaramasu and B. Wu, Model Predictive Control of Wind Energy Conversion Systems. IEEE Press, 2016.

[2] F. Blaabjerg and Z. Chen, POWER ELECTRONICS FOR MODERN WIND, vol. 1, no. 1. 2005.

[3] H. The N., A. S. Al-Sumaiti, V. P. Vu, A. Al-Durra, and T. D. Do, “Optimal power tracking of PMSG based wind energy conversion systems by constrained direct control with fast convergence rates,” Int. J. Electr. Power Energy Syst., 2020.

[4] A. Uehara et al., “A coordinated control method to smooth wind power fluctuations of a PMSG-Based WECS,” IEEE Trans. Energy Convers., 2011.

[5] V. Yaramasu, A. Dekka, M. J. Durán, S. Kouro, and B. Wu, “PMSG-based wind energy conversion systems: Survey on power converters and controls,” IET Electr. Power Appl., vol. 11, no. 6, pp. 956–968, 2017.

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