Major attributes of an electrical power network are the service continuity and high reliability. The occurrence of fault in the transmission line causes the disruption of attributes. If any fault occurs in the transmission line, the reactive power is generated to compensate the failure. The reactive power compensation is based on the utilization of flexible AC transmission system devices like static synchronous compensator (STATCOM) for power regulation. Many research works investigated the use of flexible AC transmission system in power systems for effective fault identification, classification, and localization. In traditional methods, the influence of fault on network parameters such as consumer gateway communication overhead, memory usage, consumer computational overhead, and critical time was more. This paper introduces a novel framework to identify, classify, and rectify the faults in the power transmission line and reduces the influence level of network parameters. Initially, the proposed algorithm detects the fault and ant colony optimization (ACO) and particle swarm optimization (PSO) are used to optimize the time and rectify the fault by the reactive power injection from static synchronous compensator. Finally, the identified faults are classified using relevance vector machine. The best fitness value by ACO-PSO assures an effective reactive power generation and fault rectification. The proposed method outperforms the existing models regarding the detection, rectification of fault, and critical time required. The comparative analysis of proposed (ACO-PSO) method with the existing methods confirms the effectiveness in assurance of high reliability and service continuity. Copyright © 2017 John Wiley & Sons, Ltd.