Abstract
The authors present a novel automatic transient stability classification approach, based on a two-stage hierarchical classifier obtained through the combination of two different pattern recognition structures. The first-level classifier uses low-cost computational variables evaluated at the fault occurrence moment. The classification structure used in this first level is a linear one and is obtained through the combination of a Fisher discriminant transformation and a Bayesian probabilistic approach for the independent term determination. The second-level classifier uses a new set of transient variables, directly related to kinetic and potential energies at the fault clearance moment. The classification structure in this second level is based on a weighted K nearest-neighbor rule, using a Euclidian metric defined in an optimum discriminant plane, where all data have been previously projected. Results obtained in the CIGRE test system are presented, showing that this method provides an efficient tool for online transient stability assessment of electric power systems.

Abstract
A candidate methodology for online transient stability assessment in power systems is presented. The method uses dynamic system variables as primary features. Linear security functions are derived by applying Fisher discriminant transformations and probabilistic considerations. Test results obtained on the CIGRE system are presented and discussed.

Abstract
Voltage stability has been of the major concern in power system operation. To prevent these problems, technical staff evaluates frequently the distance of the operation state to the voltage collapse point. This distance normally is calculated with power flow equations. This classic technique is very slow for electric power systems with large dimension. In abnormal exploration situations it may introduce serious limitation in the voltage stability analysis process. So, the application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper presents a study of the application of artificial neural network (ANN) to the evaluation of this distance to the voltage collapse point. To detection the point of collapse the new method FSQV was used.

Abstract
The basic function of an electric power system is to satisfy the customer energy requirements as economically as possible and at an adequate level of continuity and quality. Reliability calculations such as those described in this paper can be used with advantage in establishing additional security constraints, that if used in the right context and backed up by the inevitable economic analysis, provide better and more consistent information for system planning or expansion studies. The model described can also be used when the simulation of the actual operating conditions is necessary.

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