Optimized Control Strategy for Grid-Connected Cascaded H-Bridge Active Rectifiers Using Type II Fuzzy Logic and Low-Frequency Modulation

Abstract

The rapid evolution of grid-connected power electronic systems, driven by the increasing integration of renewable energy sources and smart grid infrastructures, has intensified the demand for high-performance, reliable, and adaptive control strategies. Among multilevel converter topologies, cascaded H-bridge (CHB) active rectifiers have gained considerable attention due to their modular architecture, scalability, reduced harmonic distortion, and suitability for medium- and high-voltage applications. Despite these advantages, the effective control of CHB rectifiers under conditions of parameter uncertainty, nonlinear load variations, and grid disturbances remains a complex challenge. Conventional control approaches, including proportional-integral regulators and model-based observers, often rely on accurate system modeling and are therefore limited in their ability to handle uncertainties and dynamic operating conditions.
This research presents an optimized control framework for grid-connected CHB active rectifiers by integrating Type II fuzzy logic control with low-frequency modulation techniques. The proposed approach is designed to enhance system robustness, improve dynamic performance, and minimize switching losses while maintaining high power quality. Type II fuzzy logic systems, characterized by their ability to model uncertainties in membership functions, provide a significant advantage over traditional Type I fuzzy systems, particularly in environments with high levels of ambiguity and noise. By incorporating interval-based fuzzy sets, the controller effectively captures uncertainties associated with system parameters, measurement errors, and external disturbances.
In addition to intelligent control, the study incorporates low-frequency modulation strategies aimed at reducing switching losses and improving converter efficiency. While low-frequency modulation is typically associated with increased harmonic distortion, the proposed integration with fuzzy logic control ensures that waveform quality is preserved through adaptive switching decisions. The methodology is further strengthened by the inclusion of observer-based state estimation techniques, inspired by classical control theories and observer design principles (Leonhard, 1985). These techniques enable accurate estimation of unmeasurable states, thereby enhancing control precision and stability.
The proposed framework also addresses fault tolerance and cyber-physical resilience by integrating diagnostic mechanisms derived from electrical drive fault detection literature. Real-time monitoring and anomaly detection enable the system to identify and respond to faults effectively, ensuring uninterrupted operation. Furthermore, the incorporation of intelligent decision-making mechanisms enhances the system’s ability to withstand malicious disturbances and data inconsistencies in networked environments.
 
Simulation-based evaluation demonstrates that the proposed control strategy significantly improves performance metrics, including total harmonic distortion reduction, faster transient response, improved voltage regulation, and enhanced stability under varying load and grid conditions. Compared to conventional control methods, the proposed approach achieves superior robustness and adaptability without requiring precise mathematical models. These results highlight the practical applicability of the framework in modern power systems, particularly in renewable energy integration and smart grid applications.
In conclusion, this study contributes a novel and comprehensive control strategy that combines intelligent control, efficient modulation, and robust estimation techniques to address critical challenges in CHB active rectifier systems. The findings underscore the potential of Type II fuzzy logic as a powerful tool for advanced power electronics control, paving the way for future research in adaptive, secure, and high-efficiency energy conversion systems.