Implementation of ML Algorithms for the Purpose of Quality Improvement of Power

In our fast industrializing world, the need for more reliable and high-quality power production is highlighted by the growing need for electrical energy and the drive toward automation. In an effort to fulfill the growing need for electrical power, sources of renewable electricity (RE) have surfaced as an eco-friendly substitute for traditional energy sources. However, because of their intrinsically erratic and fluctuating production patterns, integrating these sources of renewable energy into the utility grid poses difficulties for sustaining the quality of electricity (PQ) and system stability. Mitigating PQ difficulties related to the adoption of renewable energy has been made possible by the integration of a distributed flexibly AC transfer system gadgets such as distribute static compensator (DSTATCOM) as dynamic voltage restorer (DVR). The control algorithms that dictate the functioning of those DFACTS devices are substantially responsible for their effectiveness. This exploration leads an extensive examination of the ordinary and adjust control methods utilized in DFACTS frameworks to address power quality issues in utility organizations where sustainable power is being incorporated. The exhaustive assessment incorporates an assessment of in excess of 170 examination papers and offers a cautious classification and outline of the different control techniques. The survey investigates these calculations' plan, trying equipment, signs of execution, and reasonability as archived in the group of surviving exploration. This collection is an invaluable tool that provides engineers and scholars working in the dynamic subject of improving power quality in utility networks amid the increasing impact of renewable energy sources with a rapid reference.