Abstract
This paper presents an efficient information debugging methodology, using a dynamic state estimation algorithm which, through the use of a dynamic model for the time evolution of the system state, will meet simultaneously two objectives: the filtering of the incoming data and the forecasting of the state vector one step ahead. Based on this last characteristic a scheme for the detection of suspicious information is built.

Abstract
This paper presents an efficient information debugging methodology, using a dynamic state estimation algorithm which, through the use of a dynamic model for the time evolution of the system state, will meets simultaneously two objectives: the filtering of the incoming data and the forecasting of the state vector one step ahead. Based on this last characteristic a scheme for the detection of suspicious information is built.

Abstract
The phasor visualizer presented in this paper is performs real time calculation in steady-state and transient conditions of simultaneous electromagnetic torque, magnetic flux, voltages and currents of an induction motor supplied with sinusoidal voltages or through frequency converters and provide an oscillographic display. The phasor visualizer is an instrument for analysis, investigations and teaching of AC machine drives. The instruments needs no shaft position or speed sensor, it uses exclusively the acquired stator currents and voltages. The main fields of application are: element of a monitoring system, integration in a control unit, research tool for induction motor drives, and test shop. Practical results of the implementation are be presented and discussed.

Abstract
This paper shows that the compositional rule of inference reduces to the compatibility modification inference, when the antecedent of a fuzzy rule and its input fulfil some properties. Two main types of implications are investigated, those generalizing the classical material conditional (residuated and strong implications), called in this paper m-implications, and those generalizing the classical Cartesian product (t-norms and pseudo-conjunctions), called t-implications. For m-implications, when their maximum modus ponens generating function is used, the compatibility measure depends obviously on the relationship between input and antecedent, but also on the t-norm the implication comes from. Similarly, for t-implications, also used as their own modus ponens generating function, the compatibility measure depends again on the input and antecedent, but also on the t-norm they come from. As it could be expected, in the first case the compatibility measure is a degree of the inclusion of the input into the antecedent, while in the second one it is a degree of the intersection of both.

Abstract
This paper reports a new approach in defining preventive control measures to assure transient stability relatively to one or several contingencies that may occur separately in a power system. Generation dispatch is driven not only by economic functions but also with the derivatives of the transient energy margin value; these derivatives are obtained directly from a trained Artificial Neural Network (ANN), using ri:al time monitorable system values. Results obtained from computer simulations, for several contingencies in the CIGRE test system, confirm the validity of the developed approach.

Abstract
This paper presents a Monte-Carlo algorithm considering loads defined by fuzzy numbers. In this methodology states are sampled according to the probabilistic models governing the life cycle of system components while fuzzy concepts are used to model uncertainty related to future load behavior. This model can be used to evaluate generation/transmission power system reliability for long term planning studies as one uses the more adequate uncertainty models for each type of data. For each sampled state a Fuzzy Optimal Power Flow is run so that one builds its power not supplied membership function. The paper proposes new indices reflecting the integration of probabilistic models and fuzzy concepts and discusses the application of variance reduction techniques if loads are defined by fuzzy numbers. A case-study based on the IEEE 30 bus system illustrates this methodology.