Abstract

Abstract
This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the cross-entropy (CE) concepts with the standard analytical GCR assessment. A mathematical analysis of the CE equations is carried out to demonstrate that the optimal change of measure or distortion can be obtained by simply dividing the annualized GCR indices for two different configurations of the generating system. Under these hypotheses, a straightforward algorithm based on fast Fourier transform is proposed to systematically obtain the optimal distorted unavailabilities for all generating units in the system. The accuracy and computational performance of the proposed approach are compared with the standard CE optimization process using different generating systems. The IEEE-RTS 79, IEEE-RTS 96, and two configurations of the Brazilian South-Southeastern system are all used for this purpose.

Abstract
This paper presents a model for breaker status identification and power system topology estimation based on a mosaic of local auto-associative neural networks. The approach extracts information from values of the analog electric variables and allows the recovery of missing sensor signals or the correction of erroneous data about breaker status. The results are confirmed by extensive tests conducted on an IEEE benchmark network.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in the high voltage apparatus. Partial discharges are a major source of insulation failure in electric power transformers, and the differentiation from other sources of acoustic emission is of the utmost importance. This paper reports the development of a new sensor concept - a fiber laser sensor based on a phase-shifted chirped fiber grating - for the acoustic emission detection of incipient faults in oil-filled power transformers. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure and improving fault detection and location. The performance of the sensing head is characterized and compared for different surrounding media: air, water, and oil. The results obtained indicate the feasibility of this sensing approach for the industrial development of practical solutions. © 2012 The Author(s).

Abstract
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.

Abstract
The power produced by wind power plants has an extremely random character due to the intermittency of wind. This leads to problems in balancing the power production and demand in the power systems. To overcome this problem, wind power forecast is used. However, as in any prediction tasks, wind power forecasting does not offer perfect results. It is the purpose of this paper to propose a method based on Monte Carlo simulations and artificial intelligence techniques to assess the impact of the deviation of the generated wind power from the predicted values on the power systems when no corrective measures are taken. The method is tested on an IEEE network as well as on a real electric network from the Romanian power system and the results and drawn conclusions are presented here.