Designing optimal controllers for doubly fed induction generators using a genetic algorithm

Inst. de Tecnol., Univ. Fed. do Para, Belem
IET Generation Transmission & Distribution (Impact Factor: 1.35). 06/2009; 3(5):472 - 484. DOI: 10.1049/iet-gtd.2008.0239
Source: IEEE Xplore


This work presents a design procedure based on evolutionary computation, more specifically on a genetic algorithm combined with the formal pole placement project, to obtain optimal controllers to the rotor-side converter of doubly fed induction generators (DFIGs), in variable-speed wind generation systems connected to the electrical grid. With this procedure it is intended to improve the global system dynamic behaviour during and after the fault period, also increasing the transient stability margin of the power system and the fault ride-through capability. The control action of the DFIG converters is accomplished by proportional and integral controllers, whose gainspsila adjustment is not a trivial task, because of the high complexity of the system. The results obtained confirm the efficiency of the proposed control design procedure.

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    • "To ensure the robustness and good performance of the indirect vector control using a PI controller, several approaches have been recently proposed. In [5], the authors proposed to optimize the gains of PI controllers by the genetic algorithm. In [6] and [7], the authors proposed an adaptive control with fuzzy and neuro-fuzzy logic to adjust the gains of PI controllers. "
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    ABSTRACT: In this paper an indirect vector control using fuzzy sliding mode control is proposed for a double-fed induction generator (DFIG), applied for a wind energy conversion system in variable speed. The objective is to independently control the active and reactive power generated by the DFIG, which is decoupled by the orientation of the flux. The sliding mode control finds its strongest justification for the problem concerning the use of a robust nonlinear control law for the model uncertainties. As far as the fuzzy mode control is concerned, it aims at reducing the chattering effect. The obtained results show the increasing interest of such control in this system
    Turkish Journal of Electrical Engineering and Computer Sciences 11/2015; 23(6):1673 – 1686. DOI:10.3906/elk-1404-64 · 0.41 Impact Factor
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    • "Vieira et al. [1] carried out a work on genetic algorithm based optimal controllers to the rotor-side converter of doubly fed induction generators (DFIGs), the DFIG converter control action performed by proportional and integral controllers. The presented approach improves transient stability margin of the power system and also has better global system dynamic behavior during and after the fault period. "
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    ABSTRACT: The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.
    The Scientific World Journal 01/2015; 2015(1). DOI:10.1155/2015/746017 · 1.73 Impact Factor
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    • "However, it has some disadvantages, such as its dependence on the machine parameters variation due to the decoupling terms and high online computation owing to the pulsewidth modulation (PWM) procedure. Moreover, the coefficients of proportional–integral (PI) controllers, in the conventional VC, must be optimally tuned to ensure the system stability within the whole operating range and attain sufficient dynamic response during the transient conditions [6]. This will deteriorate the transient performance of VC and affect the system stability within changing operation conditions. "
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    ABSTRACT: In this paper, a combined vector and direct power control (CVDPC) is proposed for the rotor side converter (RSC) of doubly fed induction generators (DFIGs). The control system is based on a direct current control by selecting appropriate voltage vectors from a switching table. In fact, the proposed CVDPC enjoys the benefits of vector control (VC) and direct power control (DPC) in a compacted control system. Its benefits in comparison with VC include fast dynamic response, robustness against the machine parameters variation, lower computation, and simple implementation. On the other hand, it has benefits in comparison with DPC, including less harmonic distortion and lower power ripple. An extensive simulation study, using MATLAB/Simulink, is conducted on a 9-MW wind farm composed of six 1.5-MW DFIG-based wind turbines. The performance of the proposed CVDPC method is compared with both VC and DPC under steady-state and transient conditions. Simulation results confirm the superiority of the CVDPC over either VC or DPC.
    IEEE Transactions on Sustainable Energy 07/2014; 5(3):767-775. DOI:10.1109/TSTE.2014.2301675 · 3.66 Impact Factor
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