Integrating type-2 fuzzy logic controllers with digital twin and neural networks for advanced hydropower system management.

The increasing complexity of hydropower systems necessitates advanced control strategies to optimize performance and enhance system reliability. This paper presents a novel approach that integrates Type-2 Fuzzy Logic Controllers (T2FLC), Digital Twin technology, and Neural Networks for comprehensive management of hydropower systems. The proposed hybrid system aims to improve load management, fault detection, and operational efficiency. Our method employs a Type-2 Fuzzy Logic Controller to handle uncertainty and imprecision in system control. The Digital Twin creates a dynamic simulation model of the hydropower system, allowing real-time monitoring and predictive analysis. Neural Networks further enhance this system by providing predictive insights based on historical and real-time data. Specifically, the hybrid system achieved a 10.96% increase in load management efficiency and a 12.64% reduction in fault detection time compared to traditional methods. The Digital Twin model contributed to a 18.21% improvement in predictive accuracy, while the Neural Networks enhanced control decisions, resulting in a 8.05% reduction in operational deviations. Furthermore, the proposed approach showed a 11.48% improvement in overall system reliability and a 13.04% reduction in maintenance costs, illustrating the practical benefits of integrating advanced control and predictive technologies. These results underscore the effectiveness of combining T2FLC, Digital Twin, and Neural Networks, offering a substantial advancement in hydropower system management. The proposed method not only enhances system performance but also provides a robust framework for future advancements in intelligent control strategies.