Abstract
The increasing loads, the great distances between the production and consumption centers as well as the state of the transport and distribution networks turns the Electrical Power System operating near its stability limit. When the system gets instable a blackout situation may occur, which is critical to the system operator and consumers. We have recent examples in United States and Italy where the system was without power for several hours, causing serious problems in hospitals and public transportations. One of the causes of a blackout may be the Voltage Instability, which turns this subject widely discussed and studied. The speed of evolution of the Voltage Stability phenomenon allows us to analyze the problem as a static problem rather than a transitory one [1]. This article will show one way to "measure" locally the proximity of the system to the Voltage Stability Limit.

Abstract
Simple performance index for detecting and predicting stability problems such as the voltage collapse are useful analysis tools for planning and operating in power system. A new method for the detection of the point of collapse and a new index for the definition of the distance of the voltage collapse point are proposed. Numerical experiments using standard IEEE 14 and 57 bus systems show that the proposed method can compute the voltage collapse point using several different scenery of load increase. The methodology proposed may be easily implemented in any power flow program.

Abstract
In this paper it is proposed an efficient steady-state contingency classification using the Rough Set Theory. The developed methodology produces a classification of the system operating in four possible states: normal, alert, emergency I, and emergency II. The methodology developed by the authors was applied to a test power network and the results obtained were analyzed. Finally, some conclusions that provide a valuable contribution to the understanding of the power system security analysis are pointed out.

Abstract
This paper presents a comparative study between a new approach for robust speed estimation in induction motor sensorless control, using a reduced order Extended Kalman Filter (EKF), and the one performed by the full order EKF. The new EKF algorithm uses a reduced order state-space model that is discretized in a particular and innovative way. In this case only the rotor flux components are estimated, besides the rotor speed, while the full order EKF also estimates stator current components. This new approach strongly reduces the execution time and simplifies the tuning of covariance matrices. The performance of speed estimation using both EKF techniques is compared with respect to computation effort, tuning of the algorithms, speed range including low speeds, load torque conditions and robustness relatively to motor parameter sensitivity.

Abstract
This paper presents and proposes a new approach to achieve robust speed estimation in induction motor sensorless control. The estimation method is based on a reduced-order Extended Kalman Filter (EKF), instead of a full order EKF. The EKF algorithm uses a reduced-order statespace model structure that is discretized in a particular and innovative way proposed in this paper. With this model structure, only the rotor flux components are estimated, besides the rotor speed itself. Important practical aspects and new improvements are introduced that enable us to reduce the execution time of the algorithm without difficulties related to the tuning of covariance matrices, since the number of elements to be adjusted is reduced.

Abstract
This paper presents a concept for building up a distributed monitoring and diagnosis system for complex industrial applications. For this purpose, a hierarchical organized model with distributed, cooperating agents was developed. The hierarchical aspect guarantees a predictable behaviour of the system with a high performance and the flexibility of the system is ensured by the federal distribution (Bongaerts 1998). By using this approach, a modular component diagnosis and monitoring (CDM) system is realized that enables the integration of legacy monitoring and diagnostic tools, specific to the application area. Universal applicable mechanisms were found to perform diagnostic processes and to improve the quality of a diagnosis by handling different diagnostic mechanisms in parallel and by applying conflict resolution algorithms. This software architecture for monitoring and diagnosis was developed by the University of Karlsruhe in cooperation with three industrial partners and one research institute within the framework of the EU Esprit Program: 'DIAMOND: DIstributed Architecture for MONitoring and Diagnosis' (DIAMOND 2002).