Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation

Abstract

In this work, we address the problem of allocating optical links for connecting automatic circuit breakers in a utility power grid. We consider the application of multi-objective optimization for improving costs and power network reliability. To this end, we propose a novel heuristic for attributing reliability values to the optical links, which makes the optimization converge to network topologies in which nodes with higher power outage indexes receive greater communication resources. We combine our heuristic with a genetic algorithm in order to solve the optimization problem. In order to validate the proposed method, simulations are carried out with real data from the local utility. The obtained results validate the allocation heuristic and show that the proposed algorithm outperforms gradient descent optimization in terms of the provided Pareto front.

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

“…Deep neural network is shown to outperform linear regression and (shallow) neural network for a short-term natural gas load forecasting application [16]. A problem of allocating optical links for connecting automatic circuit breakers in a utility power grid has been solved using a multi-objective genetic algorithm (NSGA-II) [17] in [18]. Energy optimization under performance constraints in chip multiprocessor systems has been addressed in [19] where deep neural network is shown to outperform reinforcement learning (e.g., [20]) and Kalman filtering (e.g., [21]).…”