Kernel-Extreme Learning Machine-Based Fault Location in Advanced Series-Compensated Transmission Line

Abstract

The Thyristor-Controlled Series Capacitor plays an important role in high voltage power transmission. However, due to non-linearities introduced by the protective equipment of the Thyristor-Controlled Series Capacitor, fault location in the series compensated line becomes a difficult task. The kernel extreme learning machine-based method for fault location in series compensated line is proposed in this article. Results of the kernel extreme learning machine are compared with other popular single-hidden layer feedforward network-based techniques, such as extreme learning machines, support vector machines, and relevance vector machines. Performances of these single-hidden layer feedforward networks are verified on two power systems: (1) Two-area equivalent system, and (2) 12-bus system modeled in detail. Simulation studies are performed with wide variation in system and fault parameters, such as compensation level, load conditions, fault resistance, fault location, and fault inception angle. The kernel extreme learning machine achieved better accuracy in lesser training time and parameter-tuning time.