Abstract
This paper proposes a new methodology for dynamic security assessment and preventive control. In the first phase, an artificial neural network (ANN) is trained to provide the security status. ANN inputs are settled by a feature selection approach that takes into account the requisites of the control algorithm, to be applied in the second phase. The adaptive control methodology is based on the steepest descent method, where the usual explicit math functions to be dealt with are emulated by the trained ANN. To illustrate the developed approach, the methodology was applied to the control of dynamic security of Madeira island power system. Results attained so far show that the proposed approach was able to find the optimal control actions.

Abstract
The application of artificial neural networks or other techniques in load forecasting usually outputs quality results in normal conditions. However, in real-world practice, a remarkable number of abnormalities may arise. Among them, the most common are the historical data bugs (due to SCADA or recording failure), anomalous behavior (like holidays or atypical days), sudden scale or shape changes following switching operations, and consumption habits modifications in the face of energy price amendments. Each of these items is a potential factor of forecasting performance degradation. This paper describes the procedures implemented to avoid the performance degradation under such conditions. The proposed techniques are illustrated with real data examples of current, active, and reactive power forecasting at the primary substation level.

Abstract
This paper describes the methodology developed to build estimates of electricity prices having the horizon of one week. This approach uses artificial neural networks and includes a particular treatment of weekends and national holidays as a way to improve the quality of the results. The developed approach was tested using data obtained from the Spanish market operator for the time period of 2006 to 2008. The obtained value of MAPE - Mean Absolute Percentage Error - was 12,62% for workdays and 10,73% for holidays and weekends. The obtained results show that this study has interest to the market agents in question, since realistic forecasting was achieved.

Abstract
Quality prediction of load evolution at different levels of distribution network is a basic requirement for adequate operation planning of modern power systems. This paper describes the models, based on artificial neural networks, developed for active and reactive power forecasting at primary substations' transformers. The main goal consists on defining a regression process characterized by good quality estimates of those future values, based on historical data. Anticipation interval shall include from the next hour to one week in advance. The implemented forecasting tool is able to deal with noisy data, holidays and special occasions and adapts forecasts in case of power network reconfiguration whenever planned. Used techniques and implementation foundations of selected forecasting models are reported. Finally, the potential of the adopted approach is sustained by illustrative examples. © 2003 IEEE.

Abstract
Voltage stability is an important concern of power system managers not only in the net planning phase but also in operation. This issue has become especially critical in recent years due to the deregulation phenomenon because of new exploration policies complying a system operation closer to its security limits. In particular, voltage collapse distance may approach emergency values or, in the worst case, make the system collapse. As voltage profile is extremely dependent on reactive power compensation, most common approaches integrate both objectives in the operation setting phase, trying to optimize reactive power production taking voltage profile into consideration. In this paper, authors propose an evolutionary approach application to the same problem but in the planning phase. It is shown that the cooperative procedure of planning and preventive control provides better solutions that if one deals with these issues one at a time. © 2001 IEEE.

Abstract
This paper deals with a problem of identification of the best subset of variables that should be used for dynamic security assessment of a power system, when this task is pro-vided by artificial neural networks (ANN)- The approach de-scribed here exploits ANN output sensitivities relatively to the inputs and correlation degrees, to identify the most relevant system variables to be used for an effective security assessment task. The ANNs are initially trained with all low-correlated candidate features, which enables the sensitivity analyses for the initial set of system features. Derivatives of the ANN output relatively to each input are obtained by exploiting the chain rule, similar to the one used for weights adaptation on Back-propagation Algorithm. A description of the application of this approach in a real system is present in the paper. Results obtained in the dynamic security assessment problem of the network of the island of Crete were quite successful. © 2001 IEEE.