Enhancing System Stability with FACTS Devices Using Least Mean Square Based Neural Network Algorithm

The main difficulty with the distribution and transmission network is the power quality (PQ) problems. These problems with PQ are categorised as voltage and current-based problems. Numerous compensation approaches are now required in grid-tied PV systems due to the reduction of PQ concerns. Because of its fixed-parameter architecture, power system stabilisers are ineffective. When equipped with a suitable additional damping control, like a Flexible AC Transmission System (FACTS) Controller effectively dampens low frequency oscillations. Under non-linear and linear loads, the soft computing algorithms have eliminated harmonics in the load currents. The complexity of the stability challenges has risen recently as a result of the rising penetration of various renewable energy sources (RESs) into power systems. This paper's main contribution is a thorough examination of microgrid research and voltage stability concepts. On the basis of an unpredictable load, a capacity restriction for power generation, and the characteristics of the slow feedback seen in a few microgrid systems, the small-signal stability has been studied. Widrof-Hof Least Mean Square Based Neural Network Algorithm for enhancing system stability with FACTS Devices is implemented in the suggested study for RES systems.