Three-phase Induction generators are widely used to extract power from wind both in grid-connected and isolated conditions. This paper proposes an induction generator-based generation scheme with innovative power point tracking capability for standalone operation. The scheme is suitable for use in remote and grid inaccessible areas as a means of extracting electric power from wind. An inverter connected across the generator acts as a source of variable excitation and regulates the load voltage during changing loads or low wind speed conditions. The obtained power is converted to DC and the same is again fed to loads via a three-phase inverter run at a fixed frequency. Optimal power generation is ensured using an artificial neural network (ANN) and an interval Type-2 Fuzzy inference system enabled maximum power point tracking (MPPT)-based controller. For the wind turbine system, ANN is used to estimate the maximum output voltage value and interval type-2 fuzzy logic is used for the generation of optimum duty cycle for pulses of the converter. A smart load controller is correspondingly proposed based on ANN which can consistently isolate loads with incipient faults. The novelty of the scheme lies in the ease of implementation, proposal of a new MPPT strategy with smart load control. Appropriate simulation and experimental results validate the proposed strategy, along with suitable comparisons.

Three-phase Induction generators are widely used to extract power from wind both in grid-connected and isolated conditions. This paper proposes an induction generator for standalone operation which is suitable for microgeneration schemes in remote and grid inaccessible areas as a means of extracting electric power from wind. An inverter connected across the generator acts as a source of variable excitation and regulates the load voltage during changing loads or low wind speed conditions. The obtained power is converted to DC and the same is again fed to loads via a three-phase inverter run at fixed frequency. Optimal power generation is ensured using an artificial neural network (ANN) and an interval Type-2 Fuzzy inference system enabled maximum power point tracking (MPPT) based controller. A smart load controller is also proposed based on ANN which can also isolate loads with incipient faults. The novelty of the scheme lies in the ease of implementation, proposal of a new MPPT strategy with smart load control. Appropriate simulation and experimental results validate the proposed strategy along with suitable comparisons.