Intelligent Ensemble Learning-Based Fault Diagnosis, Location, and Protection of Series-Compensated Transmission Lines for Smart Power Grid Applications

Abstract

Accurate fault diagnosis and protection of series-compensated transmission lines remain challenging due to the nonlinear behavior of series capacitors and associated protective devices, which degrade the performance of conventional protection relays under varying operating conditions. To address these challenges, this paper proposes an intelligent ensemble learning-based protection framework for fault detection, fault classification, fault section identification, and fault location estimation in fixed series-compensated transmission networks. The proposed framework integrates an Artificial Neural Network (ANN) and a random subspace ensemble classifier (RSEC), where the ANN performs fault detection, classification, and location estimation, while the RSEC identifies the faulted section using a majority-weighted voting strategy. In addition, four fault indices are formulated to effectively characterize fault conditions and improve diagnostic performance. The proposed framework is evaluated on a 400 kV, 50 Hz series-compensated transmission system under diverse fault scenarios and varying operating conditions, including different fault types, fault resistances, fault locations, compensation levels, and noisy measurements. The results demonstrate an average fault detection time of 4.05 ms, 100% fault classification accuracy, 98.646% fault section identification efficiency, a mean signed fault location error of −0.02988%, and a mean absolute location error of 0.0791%, indicating negligible systematic bias and high localization accuracy. Furthermore, real-time validation using the OPAL-RT digital real-time simulator confirms the computational feasibility of the proposed framework, demonstrating its potential as a reliable, accurate, and computationally efficient solution for intelligent protection and monitoring of modern smart transmission networks.