A Frequency-Domain Framework for Detection and Analysis of Power Quality Disturbances in Smart Energy Systems

Abstract

There are a growing number of renewable energy sources, power electronic converters, distributed energy generation units, and dynamic load conditions that have led to power quality disturbances (PQDs) becoming a major issue in smart energy systems. These events have negative impacts on the energy system, including voltage sags and/or swells, harmonic distortion, flicker, interruption and transient events, among others, which impact reliability, operational efficiency and energy delivery. To guarantee stable and reliable operation of today's power networks, accurate detection and analysis of PQDs are then essential. In this study, authors introduced a frequency-domain approach for power quality disturbance detection and analysis in smart energy system. The suggested framework uses Fast Fourier Transform (FFT) for transforming timedomain voltage signals to the frequencydomain and thus reveal the frequencydomain characteristics associated with disturbances. The modules included in the framework include signal preprocessing, harmonic spectrum analysis, spectral energy estimation, frequency domain feature extraction and disturbance identification to make comprehensive assessment of the power quality conditions. Different types of disturbances (composite and single) are analysed under different operating conditions and noise conditions. Evaluation metrics include detection accuracy, precision, recall, F1-score, computational efficiency and robustness to measurement noise. Experimental results prove the proposed methodology can identify characteristic frequency signatures of various types of disturbances with high detection reliability with low complexity and the ability to handle the large number of unknowns. Since comparison with traditional signal processing methods revealed the advantage of using the frequency domain technique in real-time power quality applications, the results from this comparison are presented here. The proposed framework provides a cost-effective and efficient approach for smart grid monitoring, integration of renewable energy sources, power quality management for industry and intelligent power infrastructure, leading to better smart grid operations awareness and reliability.