Utilization of adaptive swarm intelligent metaheuristic in designing an efficient photovoltaic interfaced Static Synchronous Series Compensator

To cope with the growing load demand of power system due to world’s rapid socio-economic growth, the system compels to run with increased load. Hence, the system must sustain at maximum loading level, termed as the Maximum Loadability Limit (MLL) of the system, without violating its security limits. To increase the system-MLL further the Static Synchronous Series Compensator (SSSC) among existing Flexible AC Transmission Systems (FACTS) devices has been preferred here for its capability of compensating the real and reactive power losses simultaneously. Initially, the SSSC parameters are optimized which are further validated through the dynamic model of the Photovoltaic (PV) interfaced SSSC (PV-SSSC) in MATLAB/SIMULINK. To make the PV-SSSC more efficient, firstly, the maximum feasible power can be acquired from PV system using a robust Maximum Power Point Tracking (MPPT) technique. Secondly, Firing Angle Optimization (FAO) can improve the output power quality of the inverter in SSSC. Thus, this research handles three optimization problems separately i.e., the SSSC parameter optimization, the FAO (minimization), and the MPPT (maximization) problems. For solving such complex, nonlinear, and nonconvex engineering optimization problems, a recently developed swarm-based metaheuristic namely Levy Flight motivated Adaptive Particle Swarm Optimization (APSOLF) technique is employed. Moreover, the superiority of APSOLF is justified over contemporary state-of-the-art techniques using statistical analyses. The APSOLF-based-MPPT can achieve tracking-efficiency above 99.8% and the lowest settling-time. The APSOLF-based-FAO attains 6.7148% THD satisfying the IEEE-519 standard without any filter circuit The MLL point enhancement and bus voltage profile improvement are also the notable observations. •Utilization of the recently developed Levy Flight motivated Adaptive Particle Swarm Optimization (APSOLF) technique in solving minimization and maximization problems.•Formulation of adaptive inertia weight and constriction factors of the APSOLF technique according to minimization and maximization problems separately.•Combining the Maximum Power Point Tracking (MPPT) and the Firing Angle Optimization (FAO) applications in designing the efficient PV-SSSC.•Formulation of objective function for FAO problem.•Validation of the optimized settings of the injection mode SSSC into the dynamic model of SSSC.•Without a filter circuit, the APSOLF-based FAO yields a significant decrease in