A Sine and Wormhole Energy Whale Optimization Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems

• Published in: Journal of bionics engineering Vol. 22; no. 4; pp. 2115 - 2134
• Main Authors: Agrawal, Sunilkumar P., Jangir, Pradeep, Arpita, Pandya, Sundaram B., Parmar, Anil, Hourani, Ahmad O., Trivedi, Bhargavi Indrajit
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2025; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Biomaterials; Biomedical Engineering and Bioengineering; Compensators; Control; Dynamic loads; Engineering; Evolutionary algorithms; Evolutionary computation; Flexible AC power transmission systems; Mechanical Engineering; Mechanical loading; Mechatronics; Optimization; Performance evaluation; Performance measurement; Phase shifters; Power flow; Power management; Rank tests; Research Article; Robotics; Thyristors; Trigonometric functions; Wormholes; Wind integration; Optimal power flow (OPF); FACTS devices; Sine and wormhole energy whale optimization algorithm (SWEWOA); Power system optimization
• ISSN: 1672-6529; 2543-2141
• DOI: 10.1007/s42235-025-00702-y

The Sine and Wormhole Energy Whale Optimization Algorithm (SWEWOA) represents an advanced solution method for resolving Optimal Power Flow (OPF) problems in power systems equipped with Flexible AC Transmission System (FACTS) devices which include Thyristor-Controlled Series Compensator (TCSC), Thyristor-Controlled Phase Shifter (TCPS), and Static Var Compensator (SVC). SWEWOA expands Whale Optimization Algorithm (WOA) through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems. A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms: Adaptive Chaotic WOA (ACWOA), WOA, Chaotic WOA (CWOA), Sine Cosine Algorithm Differential Evolution (SCADE), and Hybrid Grey Wolf Optimization (HGWO). The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9% better performance. SWEWOA demonstrates superior power loss performance by achieving ( ) at the lowest level compared to all other tested algorithms which leads to better system energy efficiency. The dynamic loading performance of SWEWOA leads to a 4.38% reduction in gross costs which proves its capability to handle different operating conditions. The algorithm achieves top performance in Friedman Rank Test (FRT) assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands. The repeated simulations show that SWEWOA generates mean costs ( ) and mean power loss values ( ) with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run. SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.