ANN Based LVRT Characteristics Enhancement of Motor-Generator Set Connected to Grid

Authors

  • Kathi Swaroop, Velappagari Sekhar, Dr.K. Ravi

Abstract

The day to day environmental changes and its increased challenges, the renewable energy place center of attraction. Renewable energy has the capability to challenge the problems like Air pollution in our towns and cities, global warming, and acidity in our oceans. Appreciatively, renewable energy is progressively more prevalent and inexpensive everywhere in the world. But these renewable energy sources have solid deficiency ride-through ability to enormous scope disengagement of sustainable power source plants because of frame work flaws at the grid side. The controlling technique for motor-generator set (MGS) system with the Artificial Neural Network proposed here to comprehend its consistent action control at the grid connection. As such a DC voltage analysis controller strategy is projected to achieve dynamic power transfer of economical force source through the MGS System. The reference voltage (Vref) and DC Voltage is resolved reliant on the maximum power point technic. By this ANN based MGS improves the LVRT Characteristics connected to grid are break down dependent on the rotor movement condition. At the point when a fault happens, the MGS System separates the shortcoming at the generators side and shields the environmentally friendly power source from disengagement. The Output of the DC voltage input control and this system improves the efficiency and also reduces total harmonic distortion (THD).

Published

2020-11-01

Issue

Section

Articles