University of Mohaghegh Ardabili Journal of Operation and Automation in Power Engineering 2322-4576 2025 11 27 A New Relaying Method for Protecting Shunt Compensated Transmission Line Integrated with DFIG Wind Farm 4263 10.22098/joape.2025.15883.2220 EN Omkar Koduri Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalainagar, 608002, Chidambaram, Tamil Nadu, India. R Ramachandran Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalainagar, 608002, Chidambaram, Tamil Nadu, India. 0000-0003-4307-3800 M. Saiveerraju Department of Electrical and Electronics Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, 534202, A.P, India. Journal Article 2024 09 19 The distance relays on transmission lines connecting Doubly Fed Induction Generator (DFIG) wind farms with a Static Synchronous Compensator (STATCOM) face challenges in ensuring reliable protection due to the system unique fault characteristics and varying operating modes. This work presents a new relaying method that integrates Particle Swarm Optimization (PSO) with Variational Mode Decomposition (VMD) and Dominant Mode Filtering-Teager-Kaiser Energy Operator (DMF-TKEO) for fault detection. For fault classification, it employs a combination of PSO-VMD and a Modified Jellyfish Optimization-tuned Random Vector Functional Link (MJO-RVFL) network. The fault detection technique focuses on optimizing the parameters (α and K) of the existing variational mode decomposition by minimizing the mean envelope entropy. The optimal Intrinsic Mode Functions (IMFs) are then derived, from which the dominant mode is identified using the Pearson correlation and fault detection is accomplished through the Teager-Kaiser energy operator. In the proposed fault classification framework, the grid-side currents are decomposed using the particle swarm optimization-based variational mode decomposition. The resulting optimal IMFs are employed to identify the most appropriate IMF, which is subsequently used to extract energy features. These features are then provided as input to MJO-RVFL network for fault classification. To assess the effectiveness of the proposed protection scheme, different fault and non -fault scenarios are created on a two-bus test power system through MATLAB/Simulink. The results demonstrate the effectiveness of the proposed protection scheme, confirming its suitability for securing such critical transmission lines. The proposed method gives 100% fault detection accuracy and 99.97% accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.995), Recall (0.992) and F1-Score (0.994), providing quantitative insights into its accuracy and dependability Finally, a proposed algorithm is compared with similar works in literature. https://joape.uma.ac.ir/article_4263_1b3476efe7ccd66848731656825fdaaf.pdf