Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

Abstract

This paper introduces a novel Improved Dwarf Mongoose Optimizer (IDMO) based on an Alpha-Directed Learning Process (ADLP) for dealing with different mathematical benchmark models and engineering problems. The dwarf mongoose’s foraging behavior motivated the DMO’s primary design. Three social groupings are used: the alpha group, babysitters, and scouts. The unique suggested solution includes an upgraded ADLP to boost searching abilities, and its upgrading mechanism is substantially led by the improved alpha. First, the IDMO and DMO are put through their paces using CEC 2017 single objective optimization benchmarks. Also, several recent optimization techniques are taken into contrast, including artificial ecosystem optimization (AEO), aquila optimization (AQU), equilibrium optimization (EO), enhanced slime mould algorithm (ESMA), Gorilla troops optimization (GTO), red kite optimization (RKO), subtraction-average-based algorithm (SAA) and slime mould algorithm (SMA). Further, their application validity is examined for optimal allocation of Thyristor Controlled Series Capacitor (TCSC) devices in transmission power systems. The simulations are implemented on two different IEEE power systems of 30 and 57 buses, and considering different numbers of TCSC devices. The suggested IDMO and DMO are compared to several different current and popular techniques for all applications. The findings from the simulation demonstrate that, in relation to efficiency and effectiveness, the suggested DMO beats not only the standard DMO but also a large number of other contemporary solutions. For the first system, considering three TCSC devices to be optimized and based on the mean acquired losses, the proposed IDMO accomplishes 5.65%, 0.68%, 3.72%, 16.44%, and 5.88% reduction in power losses in compared to DMO, SAA, AEO, Grey Wolf Optimizer (GWO) and AQU, respectively. Similarly, for the second system, the proposed IDMO achieves improvement reduction 28.96%, 54.20%, 9.44%, 60.99% and 48.54%, respectively, compared to the obtained results by the DMO, SAA, AEO, GWO and AQU.

Keywords

• Dwarf Mongoose optimizer
• alpha-directed learning process
• thyristor controlled series capacitor technology
• power systems
• power losses minimization