Contents I. Introduction
The emergence of advanced measuring units has significantly changed power system monitoring and control. Despite the remarkable advancements in measuring units, certain challenges and problems persist. One of the prominent challenges is the presence of noise, which refers to unwanted electrical signals or disturbances within power systems. Another important issue associated with modern power system monitoring is packet data drops or missing data in the measurements [1], [2]. Missing data packets refer to instances where the intended data transmission is unsuccessful or fails to reach its destination. Network congestion, transmission errors, equipment malfunctions, and communication disruptions are some common factors contributing to data packet losses. The absence of essential data points can introduce errors and uncertainties, compromising the effectiveness of algorithms designed to optimize power flow, fault detection, or load forecasting. Current interdisciplinary research in power systems is dedicated to tackling challenges using signal processing and data mining techniques. The primary aim is to overcome noise-related problems, which are effectively handled by robust filtering methods such as analog and digital filters, eliminating undesired frequencies. Moreover, advanced algorithms are employed to improve measurement accuracy even when faced with noisy conditions. To address missing data packets, proactive approaches involve the integration of redundant data acquisition and communication systems. However, it is important to note that this could result in increased expenses within the power system network because of the introduction of additional communication channels.
