Abstract
This paper proposes an integrated approach of genetic algorithms, Tabu search and simulated annealing for multi-stage (dynamic) transmission network expansion planning. The proposed algorithm integrates the most interesting and best features of the above individual algorithms. The efficiency and reliability of the proposed algorithm is proved with the modified Garver's six-bus network. Finally, a real-world application (Sri Lankan transmission network) of the integrated algorithm is presented for multi-stage transmission expansion planning.

Abstract
This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two different problems in Power Systems. (C) 1997 Elsevier Science Ltd.

Abstract

Abstract
Incipient fault diagnosis is closely related to insulation condition assessment. A great number of methods are available for condition monitoring and diagnosis of power transformer insulation systems, but only few of them can take direct measurements inside the transformer. Fiber optic sensors can be applied to incipient fault diagnosis. In particular, acoustic sensors have been developed for detection and location of partial discharges in oil-filled power transformers. We report the study of extrinsic and intrinsic fiber Fabry-Perot sensors that can be used to detect the acoustic waves that are generated by a partial discharge inside a power transformer. A comparative analysis is done to determine the best sensing head configuration and some methods to improve the parameter readout sensitivity are proposed. The sensing head behaviour when immersed in different fluids (air, water, and oil) is also investigated. (c) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3080752]

Abstract
This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system.

Abstract
This paper presents an application of evolutionary particle swarm optimization (EPSO)-based methods to evaluate power system reliability. Population-based (PB) methods appear as competitors to the traditional Monte Carlo simulation (MCS), because they are computationally efficient in estimating a variety of reliability indices. The work reported in this paper demonstrates that EPSO variants can focus the search in the region of the state space where contributions to the formation of a reliability index may be found, instead of conducting a blind sampling of the space. The results obtained with EPSO are compared to MCS and with other PB methods.