Experimental behaviour analysis for optimally controlled standalone DFIG system

Abstract

Metaheuristic optimisation techniques such as the Grey Wolf optimiser (GWO) and artificial bee colony (ABC) algorithms have been developed for enhancing the dynamic behaviour of wind energy conversion system. The stand-alone doubly fed induction generator (DFIG) control system based on the direct voltage control is experimentally validated for the robust independent control of the stator voltage amplitude and the consequent rotor current regulation. The GWO and ABC are used for selecting the optimal gains of the proportional-integral (PI) regulator to improve the dynamic performance and the robust stability of the DFIG system in the presence of step voltage variation and sudden load operation. Through, MATLAB™/ Simulink numerical simulations, the dynamic performances of the GWO-PI and ABC-PI applied to stand-alone DFIG systems are compared with the conventional PI controller under different disturbances. Using a 3 kW DFIG test bench DSPACE DS1104 card prototype, the experimental validation justifies the superiority of proposed novel GWO-PI and ABC-PI controllers over the conventional PI control in terms of steady-state errors, maximum overshoots, settling time and rise time.

Bacteria foraging optimisation algorithm based optimal control for doubly‐fed induction generator wind energy system

“…The frequency-domain design consists on selecting the system roots by defining the coefficients ξ and ω n and the performance of the step response. Then, these values are used in the calculation of the PI control parameters based on (10) and (11).…”

“…In [9], an optimisation model with non‐dominant sequencing genetic algorithm, based on the reference point, has been applied to the tune of the PI control parameters of a DFIG wind energy system. In [10], metaheuristic optimisation techniques, grey wolf optimiser (GWO) and artificial bee colony (ABC), have been applied for selecting the optimal parameters of the PI controllers for the control of the stator voltage and the current for a standalone DFIG energy system connected to a load. In [11], BFO has been employed to optimise the real power losses and voltage stability limit of a power network and compared with other methods, where it was concluded that BFO provides better results.…”

Hybrid Fuzzy-PID based MPPT Enhancement of Grid-Connected DFIG Wind Energy Eystem with TLBO Optimization

“…The Rotor side control enables the regulation of active and reactive power flow by controlling rotor angular speed. The Grid side converter enables power quality management by synchronizing rotor power to the grid level at constant dc-link voltage and frequency [17]. A three-phase non-linear passive filter is generally used in the rotor circuit before feeding the power to the utility grid with further reduced harmonics.…”

“…There are several AI-based optimization techniques applied for assigning gains of conventional controllers employed in MPPT, including PSO [15], [16], Greywolf [17] and Linearized Biogeography-Based Optimization (LBBO-DE) [18] where the complexity of the approach and computational sensor requirement are among the key factors in deciding on the technique to be implemented.…”

Event analysis and real‐time validation of doubly fed induction generator‐based wind energy system with grid reactive power exchange under sub‐synchronous and super‐synchronous modes

“…For instance, a test system of DFIG is presented with a field‐programmable gate array (FPGA)‐based real‐time simulation platform using dSPACE controller in Reference 34. Metaheuristic optimisation techniques have been investigated for enhancing the dynamic behavior of WECS with experimental validation using a low power DFIG system with dSPACE in Reference 35. Another study is based on the modeling and control of a DFIG driven by a wind turbine on a real‐time digital simulator, developed by RTDS Technologies Inc 36 .…”