Belleville Washer Search and enhanced Papilionoidea Optimization Algorithms for real power loss reduction.

In this paper, Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are designed to solve the optimization problem. Key objectives of the paper are the minimization of voltage deviation, voltage stability enhancement and power loss reduction. Belleville washer is coned-disc spring, which can encumber the length of its axis by static and dynamic mode. Belleville washers are helpful for modification since dissimilar breadth can be exchanged within and away and it can be used to attain vast tune of coil rate. They are perfect in circumstances wherever a weighty coil power is necessary with least complimentary distance end to end and firmness previous to attainment of firm elevation. Fundamental law of force has been employed to design the Belleville Washer Search optimization algorithm. Belleville washer impresses a force on the particle in horizontal direction and an external force pressure the particle in anti-clockwise direction. Belleville washer's force is equal to the peripheral force in the period of time the particle will be in balanced condition. Initially, the particle moves certain distance, and Belleville washer's force will tender a resistance over the particle during the time peripheral force acting over the particle. Stiffness property of the Belleville washer is tailored relative to the objective function. Force and movement of the particle have been observed. Papilionoidea Optimization Algorithm is based on performance of the Papilionoidea in nutrition foraging. Opposition-based Learning is a technique integrated with Papilionoidea Optimization Algorithm. Through this, exploration capability of the process will be enriched and convergence rate will be augmented. Then, in this work, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm. Through this integration, the algorithm will balance the exploration and exploitation equally. Then, both the Opposition-based Learning and Chaotic Local Search has been integrated with Papilionoidea Optimization Algorithm. With and without voltage stability, index Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are corroborated in standard IEEE 30-bus test systems. Proposed algorithms condensed the power loss competently with augmentation in voltage stability and minimization of voltage deviation. [ABSTRACT FROM AUTHOR]