Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems

Abstract

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety.