Abstract
In this paper, CARE an advanced control system for the optimal operation and management of isolated power systems with increased wind power integration, is presented. This control system minimises the production costs through an on-line optimal scheduling of the power units, which takes into account the technical constraints of the thermal units, as well as short-term forecasts of the load and the renewable resources. The power system security is maximised through on-line security assessment modules, which predict the power system capacity to withstand pre-selected disturbances caused by power variations from both the renewable and thermal power sources or from faults.In this paper, CARE an advanced control system for the optimal operation and management of isolated power systems with increased wind power integration, is presented. This control system minimizes the production costs through an on-line optimal scheduling of the power units, which takes into account the technical constraints of the thermal units, as well as short-term forecasts of the load and the renewable resources. The power system security is maximized through on-line security assessment modules, which predict the power system capacity to withstand pre-selected disturbances caused by power variations from both the renewable and thermal power sources or from faults.

Abstract
This paper describes the main steps needed to apply neural networks in the domain of dynamic security assessment (DSA). The paper provides also an overview of the application of neural networks in DSA. Special emphasis is given to the application of this approach in a medium size real system where a large penetration of wind power is foreseen.

Abstract
In this paper it is presented an on-line dynamic hybrid transient stability formulation. This efficient and robust approach combines both a time domain integration method to compute the initial system trajectory with the extended equal area criteria to obtain the transient stability margins and the critical clearing times. The computer programs developed by the authors were applied to a multimachine power network. The results obtained with this hybrid formulation were compared with the solutions produced by a direct approch and by the Runge-Kutta method. Finally, some conclusions that provide a valuable contribution to the understanding of the multimachine power system preventive control are pointed out.

Abstract
This paper describes a practical instrument for realtime visualization of the necessary quantities for analysis, diagnosis and investigations of AC machine drives on an oscillographic display. The instrument needs no shaft position or speed sensor, it uses exclusively the acquired stator currents and voltages and provides information, in electrical and visual form, of: starer and rotor flux, torque, reactive power, torque current, flux current, torque angle and slip frequency. In the present version, the instrument circuitry is based on analogue signal processing techniques. The theoretical basis is a continuous-time machine model defined by a pair of simultaneous complex-coefficient differential equations. A formalization of the various calculation methods for the signal estimation is presented as well as one interpretative computer simulation for the stator current pattern. Practical results obtained with the device when applied to test and measure a real motor drive are presented and some conclusions are drawn. Main fields of application of the calculator include: monitoring systems, control units, research tools for induction motor drives, and test shops.

Abstract
This paper proposes a design of a sliding mode controller for direct torque control of an induction motor drive. The design includes the hardware structure, the 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 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 theoretical predictions of the proposed controller. Copyright (C) 1998 IFAC.

Abstract
This paper shows the conceptual differences between adopting a probabilistic weighting of the futures and a risk averse strategy, in power system planning under uncertain scenarios. It is illustrated with a distribution planning problem, where optimal solutions in both cases are determined by a Genetic Algorithm. It shows that the probabilistic approach is less safe and cannot detect some interesting solutions.