Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems

This paper presents a novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network. The basic disadvantage of the original teaching learning based optimization (TLBO) algorithm is that it gives a near optimal solution rather than an optimal one in a limited iteration cycles. In this paper, opposition based learning (OBL) and quasi OBL concepts are introduced in original TLBO algorithm for improving the convergence speed and simulation results of TLBO. In order to show the effectiveness and superiority, the proposed algorithms are tested on 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results of the proposed methods are compared with those obtained by other artificial intelligence techniques like GA/PSO, GA, PSO and loss sensitivity factor simulated annealing (LSFSA). The results show that the QOTLBO surpasses the other techniques in terms of solution quality.