In this manuscript, a hybrid HUA-GPC strategy with unified power flow controller (UPFC) is proposed to enhance that security of power system under the condition of transmission and/or generator failure. The proposed hybrid strategy is joint implementation of Human urbanization algorithm (HUA) and Giza Pyramids Construction (GPC) and commonly named as HUA-GPC approach. GPC approach is utilized to develop that search behavior of HUA.The main aim of the proposal is the operation and control of electrical system is to satisfy the demand continuously in the absence of failures. A new severity function is created with transmission line loads and bus voltage magnitude deviations. The proposed function and generation fuel cost are evaluated in contingency conditions of transmission line (s) and/or generator (s). System safety in contingency conditions is evaluated with optimal power flow issue.To improve system safety in contingency conditions in the presence of UPFC, it is essential to recognize an optimal position to install this device. To recognize an optimal UPFC position, in this proposal, a HUA-GPC technique depending on line overload sensitivity index (LOSI) is evolved. The LOSI is assessed for every transmission lines in contingencies. An energy injection model based on UPFC voltage source, incorporation process and optimal location recognition approach depending on line overload sensitivity indices is proposed. The constraints are the constraints of equality and inequality. The proposed approach is then performed on the MATLAB/Simulink work platform and the efficiency is compared with existing strategies. The quality of the solution for the proposed system is analyzed in terms of best aptitude, worst aptitude, mean aptitude, SD, coefficient of variation and the error of best is 1110936.

The genetic algorithm (GA) and particle swarm optimization (PSO) are search heuristic methods that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution. The project presents a genetic-algorithm (GA) and PSO based OPF algorithm for identifying the optimal values of generator output and enhance power system security. Equality and inequality constraints are considered and severity indices are calculated for over loaded lines. Contingences are ranked based on the severity indices. The proposed method is applied on the standard IEEE 30 bus system. Simulations are obtained and results are presented, with the objectives 1.To calculate the security Index for a given power system network by evaluating various contingencies. 2.Enhancing the Electrical Power System Security levels at the optimum power schedule. I.

No abstract

No abstract