Distribution Network Optimization by Optimal Sizing and Placement of D-STATCOM using Teaching and Learning Based Optimization Algorithm

Abstract

Distribution system is a part of an electric power system, which links the high voltage transmission networks with the end consumers. This work offers the way of improving the performance of the distribution network by improving voltage profile and reduction of power loss via injecting reactive power through the network. This study has been conducted on Aposto feeder of Yirgalem distribution network (Ethiopia) for steady-state constant load model. In this work, the first condition has aimed to find the best optimal D-STATCOM sizing and placement by using Teaching and Learning Based optimization (TLBO). Results obtained have been compared with those of the conventional optimization techniques reported in literature. As stated, the TLBO method performs better in terms of reducing both real and reactive power losses and improvement of voltage profile. The model has been formulated to minimize the total cost of the network by determining the optima of the substation locations and power, the load transfers between the demand centers, the feeder routes and the load flow in the network subject to a set of constraints. From the point of view of economic evaluations, the proposed approach is cost-effective. Generally, the simulation results show that the proposed technique is effective to maintain all bus voltage magnitudes within the IEEE acceptable limit and thereby reducing power losses significantly. In this research D-STATCOM control is developed based on artificial intelligence (AI) using artificial neural network (ANN), which depends on optimum values obtained by TLBO.