AC power

Electrical engineering

Physics

Control engineering

Nonlinear system

Stator

Mathematics

Induction generator

Gene

Engineering

Voltage

Model predictive control

Artificial intelligence

Control (management)

Chemistry

Grid

Doubly fed electric machine

Geometry

Observer (physics)

Computer science

Quantum mechanics

State observer

Control theory (sociology)

Biochemistry

Robustness (evolution)

Mechanical engineering

IEEE Transactions on Power Electronics

The traditional method of controlling a doubly fed induction generator based on a mathematical model has poor control performance when the motor parameters are inaccurate. To solve this problem, this article proposes a new model-free predictive current control (MFPCC) scheme. In the proposed method, an ultralocal model is used to replace the mathematical model of the motor, and an extended state observer (ESO) is used to estimate the value of the disturbance to improve the control performance. Since only the measured stator voltage and current values are required in the final control expression, the control system achieves good parameter robustness. In addition to superior control performance when the parameters are inaccurate, the proposed method also has good steady state and dynamic performance when the parameters are accurate. The proposed MFPCC scheme based on an ESO is extended to an unbalanced and distorted grid by modifying the stator current reference. The presented experimental results confirm the effectiveness of the proposed method.

Model-Free Predictive Current Control of DFIG Based on an Extended State Observer Under Unbalanced and Distorted Grid