The application of grey wolf optimization technique for multiple FACTS placement is presented in this paper for the reduction of total system losses and minimization of voltage deviation via optimal placement of Flexible AC Transmission System (FACTS) device. Grey wolf optimization (GWO) technique is inspired by social hierarchy and hunting behaviour of wolves and offers a right balance between exploration and exploitation during the search for global optimal. Series-shunt FACTS device; unified power flow controller (UPFC) is considered as a formidable device that can provides an alternative option for the flexible controllability and improvement of power transfer capability of a transmission lines. The analyses were conducted by increasing the number of UPFC in the network in order to evaluate the optimal number of FACTS devices that would give the least loss under maximum loading and contingency conditions. The efficacy of this proposed technique is demonstrated on 31-bus, 330 kV Nigeria National Grid (NNG) using MATLAB environment. The results show that optimal placement of FACTS device along with optimization technique provides a promising solution to the high power loss and voltage deviation bedevilling Nigeria National Grid.

Low voltage power distribution system problems such as system planning, energy loss minimization and restoration usually involve proper load balancing or network reconfiguration procedures. To achieve an appreciable level of load phase balance, feeder reconfiguration using appropriate switching control strategy such as: Simulated Annealing, Tabu Search, Particle Swarm Optimization, and heuristic algorithms are viable preferences. However, the systematic solution to load phase balancing can be greatly enhanced optimally through implementation of an appropriate combinatorial optimization procedure such as Genetic Algorithms and Artificial Neural Network. Accordingly, this paper presents a genetic algorithms procedure to enhance the load phase balancing optimization and then train an artificial neural network to automate the reconfiguration of the distribution network loads, thus ensuring an optimal phase balancing in the system. An Intel® 2.0 GHz, 4GB RAM HP255 computer-based MATLAB® 14 was used for the neural network training, testing, and the implementation of the genetic algorithms. The outputs of the algorithms are the switching sequence for a balanced network. The parameters ΔIph (max - min) and Δ(Iph – Imax) which is the maximum difference between the phase currents, which are ideally zero if there are no imbalances in the network, shows considerable improvement in the balancing when compared with other literatures. This work presents the application examples of the proposed methods using real test data.