Implementation of Computational Analysis for Enhancing Power Factor in Industries

Abstract

The power factor of electrical systems in industrial settings plays a critical role in energy efficiency and cost savings. Low power factor not only results in increased energy consumption but also leads to poor utilization of electrical infrastructure. This research presents a comprehensive computational analysis aimed at enhancing the power factor in industrial facilities. A significant portion of the research focuses on the development of computational models and algorithms for assessing and optimizing power factors. These models leverage advanced machine learning and data analytics techniques to analyze historical power consumption data, identify power factor anomalies, and predict future power factor trends. Furthermore, the study investigates various power factor correction techniques, including the use of static capacitors, dynamic VAR compensators, and synchronous condensers. Computational simulations are employed to evaluate the effectiveness of each method in enhancing power factors and reducing system losses. The research also takes into account the practical considerations and constraints faced by industries in implementing power factor correction solutions. Factors such as cost, space availability, and compatibility with existing equipment are analyzed to provide practical recommendations for industrial operators. This computational analysis offers a holistic approach to enhancing power factors in industries, providing valuable insights and tools for optimizing energy efficiency and reducing operational costs. The findings and recommendations presented in this study serve as a roadmap for industries seeking to improve their power factor and contribute to a more sustainable energy future.