Application of Adaptive Network-Based Fuzzy Inference System for Sensorless Control of PMSG-Based Wind Turbine With Nonlinear-Load-Compensation Capabilities

The precise information of permanent-magnet synchronous generator (PMSG) rotor position and speed is essentially required to operate it on maximum power points. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for speed and position estimation of PMSG, where an ANFIS-based model reference adaptive system is continuously tuned with actual PMSG to neutralize the effect of parameter variations such as stator resistance, inductance, and torque constant. This ANFIS-tuned estimator is able to estimate the rotor position and speed accurately over a wide speed range with a great immunity against parameter variation. The proposed system consists of two back-to-back connected inverters, where one controls the PMSG, while another is used for grid synchronization. Moreover, in the proposed study, the grid-side inverter is also utilized as harmonic, reactive power, and unbalanced load compensator for a three-phase, four-wire (3P4W) nonlinear load, if any, at point of common coupling (PCC). This enables the grid to always supply/absorb a balanced set of fundamental currents at unity power factor. The proposed system is developed and simulated using MATLAB/SimPowerSystem (SPS) toolbox. Besides this, a scaled laboratory hardware prototype is developed and extensive experimental study is carried out to validate the proposed control approach.