An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

The article presents the design and development of invasive weed optimization (IWO) for the sensor less speed control of doubly fed induction generator (DFIG) under balanced and unbalanced conditions. A healthy condition represents the balanced condition while unbalancing condition is characterized by unhealthy conditions like three-phase fault, single line to ground fault, the line-to-line fault, and double line to ground fault. The DFIG is driven by wind and integrated with the grid. The advantages associated with IWO technique are a simple mathematical approach and less data computation. Normally, DFIG consists of two back-to-back converters namely grid side converter (GSC) and rotor side converter (RSC). The GSC is an uncontrolled converter while the RSC is a controlled converter. The existing methods have poor performance parameters like settling time, peak overshoot for balanced conditions, and poor power quality parameters like total harmonic distortion (THD) for unbalanced conditions. An IWO technique has been applied to overcome such limitations. The effectiveness of the sensor less speed control is also analyzed with other techniques like Adaptive Neuro-Fuzzy Interference System (AN-FIS) & artificial neural network (ANN). The design of ANN is based on the feed-forward method using back propagation delay and the design of ANFIS is based on adaptive control and state space control strategy. It is observed that performance parameters like peak overshoot and settling time for the sensor less speed of DFIG are found to be more profound with IWO in comparison to ANFIS, ANN, and other existing techniques for balanced conditions. Similarly in the unbalanced condition, faulty current approaches are quite closer to their healthy state with the IWO method in comparison to other methods. In addition to this, minimum distortion (%THD) for the grid current under unbalanced conditions is also attained with IWO in comparison to ANFIS, ANN, and other existing techniques. Such application of IWO makes the system highly efficient and robust.