An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices

Abstract

Summary 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.

Detecting the location and severity of transformer winding deformation by a novel adaptive particle swarm optimization algorithm

“…Particle swarm optimization is 1 of the most used stochastic algorithms in engineering studies . In this paper, an improved version of PSO is proposed which is specially designed for solving the problem of defining the detailed model of the transformer winding parameters.…”

“…Particle swarm optimization is 1 of the most used stochastic algorithms in engineering studies. 29,30 In this paper, an FIGURE 3 The proposed multilevel technique for detecting the fault location and its severity improved version of PSO is proposed which is specially designed for solving the problem of defining the detailed model of the transformer winding parameters. In brief, PSO uses main optimization steps as follows to find the optimal solution: 1) Generate random positions and evaluate fitness of each particle.…”

Controller parameter optimization for a 13‐level cascaded H‐bridge medium voltage static synchronous compensator

“…In the bid to minimize power losses associated with the switching of semiconductor devices, the recent trend in the design of STATCOM has seen a shift towards multilevel converters, especially in medium/high power applications . One of the most desired STATCOM design considers a number of CHB converters, which is modular, high efficient, and generates relatively small harmonics because of stair‐case output voltage waveform . The CHB‐STATCOM diagram with n cells per phase is shown in Figure .…”

“…However, the major issue associated with the PSPWM is the harmonic components, which are generated in the modulator. This issue is addressed in some studies such as Zhang et al, Hu et al, Joos, Moran, and Ziogas, Chen and Hsu, Behrouzian, Bongiorno, and Teodorescu, Li, Xu, and Tomsovic, Li and Li, Kumari and Roy, Luo and Tang, Mohaghegh, Venayagamoorthy, and Harley, Shinde et al, Mauboy, Lie, and Anderson, Calvini et al, Shankar, Veeramanju, and Yathisha, Zhang et al, Wang et al, Farivar et al, Comparatore et al, Marquez et al, Kishore, Suresh Kumar, and Ramesh, Li, Xu, and Tomsovic, and Devasahayam and Veluchamy …”

“…16,30,31 However, the major issue associated with the PSPWM is the harmonic components, which are generated in the modulator. This issue is addressed in some studies such as Zhang 11,33 Particle swarm optimization is a widely used meta-heuristic method for the optimization problems. 10 It has been applied to fine-tune the gains of the controllers.…”