Abstract
The power flow is the fundamental tool for the study of power systems. The data for this problem are subject to uncertainty. This paper uses interval arithmetic to solve the power flow problem. Interval arithmetic takes into consideration the uncertainty of the nodal information, and is able to provide strict bounds for the solutions to the problem: all possible solutions are included within the bounds given by interval arithmetic. Results are compared with those obtainable by Monte Carlo simulations and by the use of stochastic power flows. Object oriented programming techniques make it possible to use interval arithmetic with minimal modifications to existing software. However, to reduce the conservatism inherent in all interval curtest computations, the paper describes an iterative method used to obtain the "hull" of the solution set.

Abstract
The paper describes a new methodology for mapping a neural network into a rule-based fuzzy inference system. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a set of rules. The method is applied in transformer fault diagnosis using dissolved gas-in-oil analysis. Studies on transformer failure diagnosis are reported, illustrating the good results obtained and the knowledge discovery made possible.

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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.

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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