Search

Mixed performance control problems have been the objective of much attention lately. These problems allow for capturing different performance specifications without resorting to approximations. In this paper, a mixed H2/H∞ with regional pole placement is used to design controllers for SVC, SSSC and UPFC placed in a small power system to secure system operation against inter-area modes. This multi-objective design allows for more flexible and accurate specification of the desirable closed-loop behaviour, both in the time and frequency domains. The results of the proposed multi-objective approach are compared with pure H∞ method (mixed-sensitivity), which show the effectiveness of this approach. [Copyright &y& Elsevier]

This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with estimation of electricity demand of Iran based on economic indicators using Particle Swarm Optimization (PSO) Algorithm. The estimation is based on Gross Domestic Product (GDP), population, number of customers and average price electricity by developing two different estimation models: a linear model and a non-linear model. The proposed models are obtained based upon available actual data of 21years; since 1980–2000. Then the models obtained are used to estimate the electricity demand of the target years; for a period of time e.g. 2001–2006 and the results obtained are compared with the actual demand during this period. Furthermore, to validate the results obtained by PSO, genetic algorithm (GA) is applied to solve the problem. The results show that the PSO is a useful optimization tool for solving the problem using two developed models and can be used as an alternative solution to estimate the future electricity demand. [Copyright &y& Elsevier]