Abstract
Operation problems that result from MV distribution networks with local asynchronous generation sources connected are examined. In order to analyze steady-state operating situations, a load-flow calculation must be performed. The presence of asynchronous generators imposes the adoption of a consistent bus modelization using a new bus type, the PX bus, where P is the active generated power and X is a nonlinear magnetizing reactance. This approach introduces some modifications in the conventional load-flow algorithms, thereby increasing the total number of iterations.

Abstract
An online recursive algorithm for power system state estimation is presented which is based on the use of the Kalman filtering theory. The recursive form of the algorithm is advantageous in two ways: it permits the inversion of smaller-dimensioned matrices, which results in simpler algorithms, and it provides the opportunity to obtain a partially updated estimate in case the computer is heavily loaded in a given period and there is not enough time to perform a single batch processing of the measurement vector as a whole.

Abstract

Abstract

Abstract
Fuzzy modelling of power systems is proposed, to take in account the qualitative aspects and vagueness or uncertainty that have not, in fact, a random nature and therefore cannot be modelled by a probabilistic approach. An operative procedure to derive fuzzy load diagrams associated with different types of energy consumption is presented. The feasibility of the development of fuzzy linear programming models for power system planning is demonstrated. A DC and an AC fuzzy load flow formulations are developed and the quality and usefulness of the results obtained with them is made evident with the help of an example.

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.