Online Monitoring of Power Systems Using Wavelet Transform

This program utilizes wavelet transform for online monitoring of power systems. Power system online monitoring represents a critical task that enables real-time observation of system operational status, early detection of potential faults, and implementation of appropriate countermeasures. However, detection signals in online monitoring frequently contain significant background noise, presenting substantial challenges for signal processing. To address this issue, we employ wavelet transform as a powerful analytical tool. The wavelet transform decomposes signals into different frequency components, facilitating more effective signal analysis and processing. In this implementation, we apply the db3 wavelet to perform 5-level decomposition of signals, generating detailed signals (capturing high-frequency components) and approximation signals (representing low-frequency trends) at each decomposition level. The algorithm sequentially applies high-pass and low-pass filters corresponding to the db3 wavelet, followed by downsampling operations at each stage. Through analysis of these decomposed signals, we can gain comprehensive insights into power system operation conditions, subsequently optimizing monitoring strategies and enhancing both the accuracy and reliability of the monitoring system. The code structure includes functions for wavelet decomposition, threshold-based noise reduction, and signal reconstruction components.