A Survey of Machine Learning Techniques for Optimal Capacitor Placement and Sizing in Smart Distribution Networks

The increasing complexity of modern power distribution networks necessitates advanced strategies for reactive power compensation, particularly in capacitor placement and sizing. Traditional optimization techniques, while effective, often struggle with dynamic system behaviors, nonlinear loads, and real-time operational constraints. This paper presents a comprehensive review of machine learning (ML)-based methodologies for optimal capacitor placement and sizing, focusing on their ability to enhance voltage stability, minimize power losses, and improve overall grid efficiency in smart distribution networks. Various ML techniques are analyzed, including supervised learning methods such as Support Vector Machines and Random Forests for predictive modeling, unsupervised learning approaches like clustering for system characterization, reinforcement learning for adaptive decision-making, and hybrid approaches, highlighting their strengths and limitations in the context of smart transformer-assisted systems. A critical evaluation of the challenges, computational complexities, and future research directions is provided, emphasizing the role of ML in enabling intelligent, data-driven optimization in smart grid environments. The findings underscore the transformative potential of ML-driven capacitor placement strategies in advancing sustainable, resilient, and efficient power distribution systems.