Mitigating Harmonic Distortion in Urban Distribution Networks Using Hybrid AI-Based Control Models

This study examines the escalating challenge of power quality degradation in contemporary urban electricity distribution systems, driven by rapid urbanisation, high penetration of nonlinear loads, and widespread deployment of power-electronic interfaces. The primary purpose is to critically evaluate existing harmonic mitigation approaches and to assess the suitability of hybrid artificial intelligence–based control models for managing harmonics under dynamic urban operating conditions. A structured review methodology was adopted, synthesizing theoretical foundations, industry standards, empirical studies, and recent advances in monitoring, modelling, and intelligent control. Particular attention was given to the interaction between harmonic phenomena, urban network complexity, and emerging digital infrastructures.

The analysis reveals that conventional mitigation techniques, including passive and active filtering strategies, remain technically effective but are increasingly constrained by static design assumptions, limited scalability, and reactive operational paradigms. In contrast, hybrid AI-based control models demonstrate superior adaptability by combining physics-based control structures with data-driven learning and optimisation. These models enable real-time harmonic detection, predictive compensation, and coordinated control across distributed urban assets, while also supporting resilience, cybersecurity, and governance requirements. Performance evaluation evidence indicates that successful implementation depends on robust data acquisition, computational efficiency, explainability, and alignment with regulatory frameworks, particularly in developing urban contexts.

The study concludes that intelligent hybrid control represents a transformative pathway for sustainable power quality management in urban distribution networks. It recommends the integration of adaptive AI controllers with advanced monitoring infrastructure, the evolution of standards toward performance-based compliance, and the development of institutional capacity to support secure and responsible deployment. Collectively, these measures provide a foundation for resilient, efficient, and future-ready urban electricity systems. The findings are relevant to utilities, regulators, researchers, and city planners seeking data-driven solutions for complex urban energy environments in worldwide contexts.