Research on digital system for identifying on-site operations of power infrastructure based on time-stamped measurements (TSM)

Abstract

The effective tracking and management of on-site operations in power infrastructure (PI) is critical for assuring operational reliability and avoiding service interruptions. Traditional monitoring systems are frequently constrained by human error, slow response times, and the inability to give real-time data on system conditions. This research uses a digital system to identify on-site operations of PI, utilizing Time-Stamped Measurements (TSM) to provide real-time monitoring and problem identification. The suggested system architecture is made up of both software and hardware parts that work together to collect, process, and evaluate data concurrently for fault detection and condition estimate in PI. Various types of sensors are installed throughout the power grid to collect data on operating characteristics such as current, temperature, voltage, and performance metrics. The collection of TSM is an important step in the proposed system since it provides precise, time-based data required for reliable problem identification and operational analysis. The sensors communicate TSM to IoT devices or gateways and employ communication protocols such as Wi-Fi to deliver the data to the main server or cloud server. Before the analysis, the raw data is pre-processed, utilizing data normalization and feature extraction using the Fast Fourier Transform (FFT). Intelligent Genetic Energy Valley Optimizer (IntGen-EVO) is used to detect defects in real time using time-stamped data. The framework demonstrates superior accuracy (98.7%), precision (98.3%), recall (98%), and F1-score (98.4%) compared to traditional methods, significantly enhancing fault detection in the PI system. The findings demonstrate the method provides accurate concurrent insights into the infrastructure’s operational state, allowing for preventive maintenance and rapid identification of problems. Therefore, the digital system described in this research provides an effective solution for increasing operational effectiveness and reliability in PI management.

Keywords

power infrastructure (PI)digital system time-stamped measurements (TSM)intelligent genetic energy valley optimizer (IntG-EVO)fault detection

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