DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Predictive Current Control of Doubly Fed Induction Generators

This paper presents a predictive current control strategy for doubly-fed induction generators (DFIG). The method predicts the DFIG’s rotor current variations in the synchronous reference frame fixed to the stator flux within a fixed sampling period. This is then used to directly calculate the required rotor voltage to eliminate the current errors at the end of the following sampling period. Space vector modulation is used to generate the required switching pulses within the fixed sampling period. The impact of sampling delay on the accuracy of the sampled rotor current is analyzed and detailed compensation methods are proposed to improve the current control accuracy and system stability. Experimental results for a 1.5 kW DFIG system illustrate the effectiveness and robustness of the proposed control strategy during rotor current steps and rotating speed variation. Tests during negative sequence current injection further demonstrate the excellent dynamic performance of the proposed PCC method.

An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Simulated Annealing Algorithm (SAA). The performance of ESPRIT is tested with simulated stator current signal of an induction motor with BRB. It shows that even with a short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3kW, 380V, 50Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.