Adaptive Q‐Wavelet Transform and Kalman Filtering for Power Quality Disturbance Prediction and Carbon Emission Impact Analysis in Smart Grids

Abstract

ABSTRACT Minimizing power quality disturbances (PQDs) and carbon emissions are crucial for sustainable smart grids, especially with increasing renewable energy integration. This paper proposes a novel method combining Adaptive Q‐Wavelet Transform (AQWT) and Kalman Filtering (KF) to predict PQDs and faults while analyzing their impact on carbon emissions. AQWT efficiently extracts time‐frequency features of voltage sags, transients, harmonics, and frequency deviations, ensuring robust detection under noisy conditions. These extracted features are fed into a KF, which predicts disturbances before they escalate into severe faults. A key aspect of this study is the evaluation of carbon emissions associated with PQDs. When grid instabilities occur, increased reliance on fossil‐fuel‐based backup power leads to higher carbon emissions. Our approach quantifies these emissions and identifies scenarios where fast‐response energy storage and demand‐side management can mitigate carbon spikes. The proposed method is validated on a Denmark‐based energy network, demonstrating its effectiveness in ensuring grid stability while maintaining low‐carbon operations. Simulation results show that early detection and predictive control significantly reduce PQDs, improve grid resilience, and optimize carbon footprint management. This research contributes to the development of intelligent, sustainable, and emission‐aware smart grids.