The article focuses on innovative measurement and control methods in electrical power engineering, specifically addressing challenges of power quality, signal diagnostics, and automation within smart grids. Emphasis is placed on wavelet analysis, smart metering, IoT integration, and automated control systems. These technologies are examined in the context of enhancing the adaptability and efficiency of modern electrical systems in line with Industry 4.0 requirements. Particular emphasis is placed on wavelet analysis, which serves as a universal tool for diagnosing non-stationary electrical signals, assessing power quality, and detecting harmonic distortions. Thanks to its capability for time-frequency localization, wavelet analysis enables effective signal processing and facilitates tasks such as transient process monitoring, voltage flicker analysis, and improving the accuracy of electrical measurements. This methodology opens new prospects for maintaining the stability of energy systems even under the challenging conditions of renewable energy integration. Special attention is given to the analysis of the role of smart technologies in contemporary energy systems. The advantages of Smart Metering systems—which ensure the automatic collection, analysis, and real-time transmission of energy consumption data—are discussed. This enables efficient management of energy resource distribution, reduces energy losses, and enhances transparency in the relationships between consumers and suppliers. The integration of Smart Metering with Internet of Things (IoT) technologies contributes to the creation of adaptive systems capable of responding to changing conditions in real time, thereby ensuring the stability and efficiency of smart grids. The article also explores the prospects of automated control systems that incorporate intelligent data collection devices and adaptive control algorithms. These systems significantly improve monitoring and diagnostics, facilitate the integration of renewable energy sources, and enhance power quality indicators. In particular, the automation of control processes and the implementation of machine learning technologies open new opportunities for forecasting the behavior of energy systems and increasing their resilience. The solutions presented in the study are aimed at creating adaptive, resilient, and high-tech energy systems that meet the modern challenges of Industry 4.0. Through the integration of wavelet analysis, Smart Metering, IoT, and automated control systems, effective management of energy resources, network stability, and the optimization of energy resource usage in the global energy system can be achieved.
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