*Result*: Intelligent Congestion Management for Power Transmission Systems: Integration of Machine Learning and FACTS Devices.

*It has become more challenging to manage congestion in power transmission lines due to the increasing usage of renewable energy sources and increased electricity consumption. To address issues with congestion management, this study examines the usage of an Advanced Interline Power Flow Controller (IPFC) in conjunction with AI and ML methods. The objective is to keep the electricity system running reliably and efficiently while keeping the cost of congestion management to a minimum. Models for congestion prediction and control are created using AI/ML techniques. Optimization methods are employed to determine the optimal strategies for IPFC operation and congestion control. To evaluate the proposed approach, the IEEE 30 bus system is utilized as a test case. The proposed AI/ML-based approach is compared to more traditional approaches to congestion management, and the results are analyzed side by side. Incorporating an IPFC and AI/ML methods results in less congestion on the power transmission lines of the IEEE 30 bus system. Significantly lower congestion levels, improved power flow optimization, and cost savings are shown in compared to earlier techniques. [ABSTRACT FROM AUTHOR]*