Development of ANN Controlled UPFC Based Protection Model for Nigerian 330kv Power Lines

This paper, proposed the use of artificial neural network (ANN) based UPFC for transmission network loss minimization. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. Power loss leads to high cost of power generation, transmission and distribution. Unlike exiting up change and regulating transformer techniques for loss reduction, FACTS devices have fast switching capability and can be subjected to very free control algorithms for more optimal performance in loss reduction application in power systems. The neural network was modeled to output the firing angle to enable the FACTS device effectively control the adsorption and injection of reactive power for transmission loss reduction. The Nigerian 330KV power grid was used as a case study for the evaluation of the proposed power loss reduction system A digital model of the case study power system with the proposed neural network controlled UPFC integrated was created in the MATLAB/SIMULINK programming environment. Genetic algorithm was used for the optimal placement of the FACTS device in the MATLAB/SIMULINK model of the Nigerian 330KV transmission system. Simulation carried out involved alteration of power flow in order to cause different levels and distribution of losses in the network. With each variation, load flows was carried out to evaluate the distribution of losses and the active and reactive loss reduction achieved by the proposed system. The simulation and evaluation were carried out under two scenarios: (i) with the UPFC installed and (ii) without the UPFC installed. With each variation of the load at the bus, load flow is run to determine total system loss either with the UPFC installed or without the UPFC installed. Results obtained showed that the proposed system achieved an average active power loss reduction of 12.8416% and an average reactive power loss reduction of 21.82%