Reliability optimization on power systems network using genetic algorithm

Abstract

In this study, reliability optimization of a non-linear transmission network using Genetic Algorithm (GA) based optimization approach is presented and proposed. A GA based algorithm was developed for Koko, Guinness, Nekpenekpen, Ikpoba-Dam, Switch station, Etete and GRA 33kV tertiary transmission feeders within Benin Metropolis, Nigeria and was used to determine the optimal performance of the feeders’ reliability and availability through the minimization of downtime and the Mean Time between Failure (MTBF) by the appropriate selection of the objective functions and constraints. The equality and inequality constraints for each feeder on the network were defined, thereafter, codes were written on the Matlab 2016a environment to optimize the selected parameters. The results from the study showed a reduction in downtime of 5.63%, 26.87%, 34.20%, 5.42% 8.37%, 5.18% and 10.97% and an increment increased in MTBF by 4.95%, 19.87%, 4.58%, 3.85%, 4.88%, 5.77% and 13.56% for Guinness, Etete, Nekpenekpen, GRA, Switch station and Ikpoba-Dam feeders respectively. The obtained results, therefore, yielded an average corresponding improvement on the network’s reliability and availability by 1.85% and 2.83% respectively. Conclusively, the desired result reached in this paper validates the robustness of the GA tool in reliability studies. However, conscious effort must be geared concerning the ways and manners the system is operated in order to achieve desired results.

Reliability Optimization Method for Gas–Electric Integrated Energy Systems Considering Cyber–Physical Interactions

“…It used the non-sequential Monte Carlo simulation based on branch reliability to evaluate the reliability of network configurations. In Reference [13], a non-linear transmission network reliability optimization method based on a generic algorithm was proposed, and the optimal performance of the feeder reliability and availability was determined by minimizing downtime and mean time between failures through the proper selection of objective functions and constraints. An approach of optimal reliability indices planning for power systems based on a non-sequential Monte Carlo simulation and particle swarm optimization algorithm was presented in Reference [14], which included optimization problems in minimizing system interruption costs and component investment costs.…”