A Swarm Intelligent Metaheuristic Approach for Efficient Series Compensation Resulting in System Loadability Enhancement

Abstract

Real-life engineering issues requiring optimization are quite often discontinuous, non-linear, and non-convex in nature. For such practical problems, most of the derivative-based traditional optimization methods either fall short of providing the desired solution or do so only after easing the nonlinearities. Therefore, population-based meta-heuristic methods have been well-liked recently in handling such issues because of their derivative free nature. Although they are insensitive to problem-complexity, they may not be completely free from the local optima trapping limitation. Hence, appropriate tactic must be adopted to develop any new metaheuristic algorithm capable of addressing such issues with noticeable accuracy. In view of this, the recently developed Levy Flight motivated Adaptive Particle Swarm Optimization (APSOLF) algorithm is further modified by incorporating the Self-Pollination (SP) strategy; thereby, the SP aided APSOLF (SPAPSOLF) algorithm is proposed. This SPAPSOLF is particularly developed to apply and test in an intricate engineering problem like Firing Angle Optimization (FAO) issue. The SPAPSOLF-based-FAO aided 11-level Multilevel Inverter has been implemented in designing dynamic model of Static Synchronous Series Compensator (SSSC) and the efficacy of the SPAPSOLF is observed to be noteworthy in comparison to other state-of-the-art swarm-based metaheuristics and associated statistical analyses help to infer from this comparative investigation. Moreover, the dynamic model of SSSC using 11-level inverter is applied on model of IEEE-5-bus-system. Furthermore, remarkable enhancement in system’s Maximum Loadability Limit, owing to reduced switching losses, has been noted in FAO-aided-Reduced Switch 11 level inverter-based-SSSC than SSSCs with other existing topologies.