Enhance Power Quality of Hydro power systems with ALO-ANFIS-based UPQC approach

This paper delineates a novel Ant Lion optimization (ALO) and adaptive neuro-fuzzy inference system (ANFIS) for stand-alone hydro power systems. When a hydro power generating system is fed to the non-linear loads, it experiences unbalanced voltage and current profiles. These unbalanced power levels present a longer duration and significantly impact the critical and sensitive loads. In the other hand, when a hydro power generator, typically a squirrel cage induction generator (SCIG), produces less voltage over the rated voltage, it leads to unbalanced load voltage and currents. In this view, to avoid these undesired power quality issues a unified power quality controller (UPQC) is utilized in this work. For achieving power quality, compensatory topologies are necessary. The series controller of UPQC compensates the voltage sag/swell and the shunt controller compensates the current harmonics and reactive power compensation. For the effective operation of series controller and shunt controller of UPQC a hybrid ALO-ANFIS controller is implemented in this current work. ANFIS is a hybrid intelligent system, it well estimates the compensation voltage based on reasoning capabilities of fuzzy logic. The ALO algorithm continuously monitors the system's performance and optimizes the ANFIS tuning parameters to ensure optimal performance. To validate the proposed control framework simulation, the study analyzes for the hydro power generation when connected to the nonlinear loads.