Comparative Analysis of SVM, Random Forest and Reinforcement Learning for Harmonic Mitigation in Smart Homes

The increasing use of non-linear loads in smart homes has led to significant power quality issues due to harmonic distortions. Traditional filtering techniques struggle with dynamic load variations, necessitating intelligent machine learning-based approaches. This study compares Support Vector Machine (SVM), Random Forest (RF), and Reinforcement Learning (RL) for diagnosing and mitigating harmonics in smart home electrical networks. SVM, a widely used classification technique, is evaluated for its efficiency in predicting harmonic distortions based on historical FFT-extracted features. Random Forest, an ensemble-based learning method, is assessed for its ability to rank and classify harmonics with improved interpretability. Reinforcement Learning, in contrast, is explored for its adaptive filtering capabilities, enabling real-time optimization of harmonic mitigation strategies. A MATLAB Simulink model is used to simulate real-time harmonic data, and each method is tested on its ability to reduce Total Harmonic Distortion (THD). The results indicate that RF provides better generalization than SVM, while RL dynamically optimizes filter parameters, leading to the most effective harmonic reduction. The study highlights the tradeoffs between accuracy, computational complexity, and real-time adaptability, demonstrating that RL-based adaptive filtering achieves the lowest THD, making it a promising approach for future smart home power quality management.