<![CDATA[This paper presents a new method for fault classification and detection in transmission lines using the Chimp Optimization Algorithm (ChOA) integrated with Support Vector Machine (SVM). The adding complexity of modern power schemes demands more accurate and efficient methods for identifying and classifying faults to ensure system reliability and minimize downtime. The proposed method leverages the strengths of ChOA, an optimization procedure motivated by the social actions of chimpanzees, to optimize the parameters of the SVM, enhancing its ability to classify different types of faults accurately. By combining ChOA with SVM, the method not only improves fault classification accuracy but also reduces computational time, making it suitable for real-time applications. Extensive simulations were conducted using various fault scenarios and configurations to justify the execution of the ChOA-SVM model. The outcomes demonstrate that the suggested approach outperforms traditional fault detection procedures in precision, speed, and adaptability to different transmission line conditions. This study aids to the field of power system protection by giving a robust and efficient solution for fault classification and detection, with potential applications in enhancing the resilience and reliability of transmission networks..]]>
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