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).

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