Search

The paper describes genetic-algorithm (GA)-based optimization software for reconfiguration of a distribution net- work in order to minimize financial losses due to voltage sags. The developed methodology starts with a selected number of switches which generate various topologies. Load flows are then performed to evaluate the feasibility of topologies. For each feasible topology, fault analysis is performed to first calculate voltage sags at different buses in the network and then to calculate financial losses incurred by voltage sags at buses with sensitive industrial processes. The result of the optimization is the topology which yields the lowest voltage sag cost to customers. The main features of the GA include double-point crossover and adaptive mutation. The developed software tool is applied to the 295-bus generic distribution system. [ABSTRACT FROM AUTHOR]

Power generation companies have to meet consumer demand and reliability criteria. The maintenance of generators needs to be performed regularly to ensure a reliable supply of electricity. An increase in repair frequency will result in greater maintenance cost together with an expected decrease in capital loss due to generator failure. An optimal maintenance schedule is therefore needed to find the best suited trade-off and lower the overall operation cost. Maintenance scheduling is a combinatorial optimisation problem for which an exhaustive search is typically infeasible due to the large size of the solution space. Metaheuristic optimisation techniques are therefore used to approximate global optimum schedules in finite time. This paper compares the uses of the local search methods (hill climbing, tabu search, simulated annealing) against genetic and memetic algorithms in the solution of the generator maintenance scheduling problem. The solution methods improve the previously implemented solution of generator scheduling for the Power Generation Company of Trinidad and Tobago. [ABSTRACT FROM AUTHOR]