Abstract
A robust feedback control of an induction motor is proposed. The theoretical basis is a continuous-time machine model defined by a pair of simultaneous complex-coefficients differential equations. The parameter uncertainties and load perturbations are considered in the controller design. The control methodology is based on sliding mode control techniques. The design of a sliding mode controller for tracking the torque and the rotor flux of an induction motor drive is also given. The design includes the hardware structure, control laws for torque control and rotor flux control. The modulation is defined as part of the variable structure control algorithm which enables an optimum choice of the output voltage of the static converter. The validity and robustness of the proposed control system were verified by simulation. The simulation results are presented to demonstrate their agreement with the theoretical predictions of the proposed controller. Copyright (C) 2000 John Wiley & Sons, Ltd.

Abstract
The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.

Abstract
Economic operation of medium-sized and large isolated power systems with a high penetration of renewables, namely wind power, is not well suited neither by the conventional unit commitment/dispatch arrangements for interconnected systems, nor by the simplified procedures used in small systems. In this paper, the economic scheduling functions developed within CARE are described. The main idea is to perform on-line unit commitment in the same cycle with the dispatch, using the most recent forecasts, as described by Hatziargyriou et al. (2000). The Unit Commitment module has two variants: one based on Genetic Algorithms, and one using a Combinatorial Approach. On the other hand, three alternative procedures were developed for the Economic Dispatch: a Linear Programming Optimal Power Flow, an Evolutionary approach and a Genetic Algorithms based procedure. The paper describes the main features of the five approaches, including test results when appropriate.Economic operation of medium-sized and large isolated power systems with a high penetration of renewables, namely wind power, is not well suited neither by the conventional unit commitment/dispatch arrangements for interconnected systems, nor by the simplified procedures used in small systems. In this paper, the economic scheduling functions developed within CARE are described. The main idea is to perform on-line unit commitment in the same cycle with the dispatch, using the most recent forecasts, as described by Hatziargyriou et al. (2000). The Unit Commitment module has two variants: one based on Genetic Algorithms, and one using a Combinatorial Approach. On the other hand, three alternative procedures were developed for the Economic Dispatch: a Linear Programming Optimal Power Flow, an Evolutionary approach and a Genetic Algorithms based procedure. The paper describes the main features of the five approaches, including test results when appropriate.

Abstract

Abstract
Preliminary filtering of a large decision set can be an important issue in multicriteria problems. Besides eliminating dominated solutions, it is usual to define aspiration levels in some or all the attributes, and then eliminate all the solutions that do not respect the imposed limits. This procedure may lead to the undesired elimination of possibly interesting solutions, due to the irrelevant violation of a specified limit. In this paper, a new method is presented that overcomes this problem by defining the concept of accepted solution as a fuzzy set and calculating a degree of acceptance for each solution that takes values in the interval [0, 1] instead of having only the crisp {0, 1} values. The method also deals with fuzzy attributes, and has an additional feature of advising the decision maker regarding the definition of the acceptance level that leads to selection or exclusion of an alternative. To illustrate the application of the fuzzy filtering method (FFM), a planning problem in the area of power distribution systems is used. The example shows how FFM can be used to reduce an initial set of 48 plans, according to the expressed aspiration levels.

Abstract
This paper provides a description of a new approach for steady-state security evaluation, using fuzzy nearest prototype classifiers. The basic method has an offline training phase, used to design the fast classifiers for online purposes, allowing more than the two traditional security classes. A battery of these fuzzy classifiers, valid for a specific configuration of the network, is adopted to produce a global evaluation for all relevant single contingencies. An important feature of this approach is that it selects automatically the most appropriate number of security clusters for each selected contingency. Natural language labeling is also used to produce standardized sentences about the security level of the system, improving in this way the communication process between the system and the operator. The paper is completed by an example on a realistic model of the Hellenic Interconnected power system, where seven contingencies were simulated.