Optimizing the Dynamic Performance of a Wind Driven Standalone DFIG Using an Advanced Control Algorithm

Abstract

The article seeks to improve the dynamic performance of a standalone doubly fed induction generator (DFIG) which driven by a wind turbine, with the help of an effective control approach. The superiority of the designed predictive controller can be confirmed through evaluating the performance of the DFIG under other control algorithm, which is the model predictive direct torque control (MPDTC), model predictive current control (MPCC) as classic types of control. Firstly, the operating principles of the two controllers are described in details. After that, a comprehensive comparison is performed among the dynamic performances of the designed MPDTC, MPCC techniques and the predictive control strategy, so we can easily present the merits and deficiencies of each control scheme to be able to easily select the most appropriate algorithm to be utilized with the DFIG. The comparison is carried out in terms of system simplicity, dynamic response, ripples’ content, number of performed commutations and total harmonic distortion (THD). The results of the comparison prove the effectiveness and validation of our proposed predictive controller; as it achieves the system simplicity, its dynamic response is faster than that of MPDTC and MPCC, it presents a lower content of ripples compared to MPDTC and MPCC. Moreover, it can minimize the computational burden, remarkably. Furthermore, the numerical results are showing a marked reduction in the THD with a percentage of 2.23 % compared to MPDTC and 1.8 % compared to MPCC. For these reasons, it can be said that the formulated controller is the most convenient to be used with the DFIG to achieve the best dynamic performance.

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

“…Data-driven required data-driven tools to find the optimal parameter of the nonlinear mathematical model. Data-driven tools have various optimization methods, such as the multiagent-based optimization method [58] and single-agentbased optimization. Likewise, in multi-agent-based optimization, a Genetic Algorithm has been utilized in a wellhead back pressure control system [59], Particle Swarm Optimization has been used in quadrotor identification [60], and Sine-cosine algorithm-based optimization has been utilized for automatic optimization voltage regulator system [61], [62].…”