Research on Power Signal Processing and Feature Extraction Algorithm Based on Time-Frequency Analysis

Power signal processing is a specialized domain within signal processing that focuses on the analysis, interpretation, and manipulation of signals in electrical power systems. In modern smart grids, Power Quality Disturbances (PQDs) can result in considerable operational disruptions and financial losses for energy stakeholders. Accurate and timely identification of these disturbances is critical to maintaining grid reliability, efficiency, and energy stability. To overcome these challenges, the research proposes a comprehensive framework for PQD identification by leveraging advanced power signal processing techniques and time-frequency-based feature extraction. A Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB) model is introduced for robust recognition of power quality disturbances with energy grid applications. To improve computational efficiency and decrease redundant data collection, a signal-piloted gain device is employed. This device continuously monitors power signals and initiates data acquisition only when abnormalities or potential disturbances are detected. The Z-score normalization is a preprocessing technique for reducing noise. The STFT is utilized to extract discriminative, time-localized features from the power signals, effectively characterizing voltage fluctuations and transient energy anomalies. These extracted features are subsequently used to train and evaluate the ENGB classifier. The proposed STFT-ENGB approach achieves high accuracy (98.75%). Experimental results demonstrate that the proposed framework achieves high classification accuracy while significantly reducing data volume and computational load. The reduction in processing overhead and latency underscores the system's suitability for real-time smart grid applications. The proposed approach offers a promising solution for real-time power signal monitoring in smart grid environments, facilitating intelligent fault diagnosis and improving the overall resilience and responsiveness of modern electrical infrastructure.