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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    • "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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    • "However, it would be interesting to see which would be the response of this control system and what changes should possibly be done, for the case where the DFIG would be connected to a weaker bus, instead of an infinite bus. Reference [21] proposes a control strategy based on genetic algorithms (GA) for the acquisition of the optimal gains of the PI controllers to the rotor-side converter of the DFIG. The GA fitness function is defined with the objective to reduce the over-current in the rotor circuit, in order to maintain the converter in operation during the fault period . "
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    ABSTRACT: This paper proposes a new computational intel ligence-based control strategy, to enhance the low voltage ride-through capability of grid-connected wind turbines (WTs) with doubly fed induction generators (DFIGs). Grid codes world-wide require that WTs should supply reactive power to the grid during and after the fault, in order to support the grid voltage. The conventional crowbar-based systems that were initially applied in order to protect the rotor-side converter at the occurrence of grid faults, do not fulfill this requirement, as during the connection of the crowbar, the DFIG behaves as a squirrel cage machine, absorbing reactive power from the grid. This drawback led to the design of control systems that eliminate or even avoid the use of the crowbar. In order to conform to the above-mentioned requirement, this paper proposes a coordinated control strategy of the DFIG converters during a grid fault, managing to ride-through the fault without the use of any auxiliary hardware. The coordination of the two controllers is achieved via a fuzzy controller which is properly tuned using genetic algorithms. To validate the proposed control strategy, a case study of a 1.5-MW DFIG supplying a relatively weak electrical system is carried out by simulation.
    IEEE Transactions on Power Systems 05/2014; 29(3):1325-1334. DOI:10.1109/TPWRS.2013.2290622 · 2.81 Impact Factor
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    • "Recently optimization methods have been utilized in controller parameter tuning for the DFIG wind turbine system. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been applied to optimize the controller for the rotor side converter in time domain in [5] and [6], respectively. The objective function is to reduce the overcurrent as well as overvoltage in the rotor circuit. "
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    ABSTRACT: Multi-objective optimal controller design of a doubly fed induction generator (DFIG) wind turbine system using Differential Evolution (DE) is presented in this chapter. A detailed mathematical model of DFIG wind turbine with a close loop vector control system is developed. Based on this, objective functions, addressing the steady state stability and dynamic performance at different operating conditions are implemented to optimize the controller parameters of both the rotor and grid side converters. A superior ε-constraint method and method of adaptive penalties are applied to handle the multi-objective problem and the constraint with DE. Eigenvalue analysis and simulation are performed on the single machine infinite bus (SMIB) system to demonstrate the control performance of the system with the optimized controller parameters.
    09/2010: pages 167-190;
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