Monitoring and enhancement of power quality in industrial power networks

The aim of this research is a comprehensive analysis of power quality (PQ) issues in industrial power networks, as well as the development and systematization of methods for monitoring, diagnosing, and improving key PQ parameters, such as harmonic distortions, flicker, voltage sags, phase unbalance, and reactive power. The methods. The study employs a combination of theoretical analysis, review of relevant standards (IEC 61000-4-30, EN 50160, IEEE 519), and practical experience in the implementation of modern digital PQ analyzers. The use of filters, compensators, and intelligent systems is analyzed, including DVR, STATCOM, hybrid active filters, and machine learning algorithms for classifying PQ events. Findings. The research outlines the main PQ indicators and presents methods for their measurement, diagnosis, and compensation. Examples of implemented technical solutions for improving power quality are provided, including passive and active harmonic filters, SVC and STATCOM for flicker mitigation, methods for reducing phase unbalance, and protection against short-term voltage disturbances. The originality. The study proposes a concept of distributed power quality compensation using smart inverters and charging stations. The application of neural networks and S-transforms for real-time identification of disturbance sources is discussed. The advantages of integrated approaches to both local and centralized PQ correction are substantiated. Practical implementation. The results can be used by energy management engineers and enterprise personnel to build monitoring systems, protect critical equipment, and enhance energy efficiency. The proposed solutions reduce failure rates, energy losses, and maintenance costs, ensuring compliance with modern sustainable development standards.