Abstract
In this paper it is proposed a technique to identify the relevant neighbouring electric power systems using the Horizontal Network scheme. The developed methodology was applied to study the IEEE 118 test power network and the Iberian systems. All simulations were performed using the computational software package PSS®E developed by the Siemens/PTI. From the simulation results, some conclusions that provide a valuable contribution to the understanding of the impact of the cross-border interconnections between the countries will be pointed out.

Abstract
The huge proliferation of wind farms across the world has arisen as an alternative to the traditional power generation and as a result of economic issues which necessitate monitoring systems in order to optimize availability and profits too. Equipments inside a wind turbine are subject to failures which, most of times lead to long downtime periods. When wind turbine is not running due to a failure, no profits are added and operation and maintenance costs increases. The development of advanced techniques to detect the onset of mechanical and electrical faults in wind turbines at a sufficiently early stage is very important for maintenance actions. Neural networks can be used to detect failures in some equipments of wind turbines, but to use them is necessary to create a model to the equipment under surveillance. The training of the neural network represents the big handicap of the developed method that will be presented here. However, after solving this problem, results are very interesting, and failures can be detected with several months in advance. © 2012 IEEE.

Abstract
In the last years, the electric power systems have been restructured, moving from a vertical integrated model to a market oriented environment. The new paradigms associated with the restructuring of the electricity sector made it critical to develop new efficient and reliable methodologies to study and analyze the security of the power networks. In this paper it is proposed a method to evaluate the impact of external elements on the responsibility area of a Transmission System Operator (TSO) using the influence factor method. This approach offers a concrete support in the determination of the observability area, which at the end remains in the responsibility of the single TSO. The influence factor is a numerical value used to quantify the greatest effect of the outage of an external network component on any internal network branch. The developed methodology was applied to study the IEEE 118 bus test power network. All simulations of the transmission systems of the IEEE 118 bus were performed using the PSS®E software package from Siemens PTI, making the division of the network in two countries (areas). From the simulation results, some conclusions that provide a valuable contribution to understanding the influence factor method are pointed out. © 2012 IEEE.

Abstract
Voltage stability is predominantly a load stability phenomenon and solutions to voltage stability can be found by control of the load as seen from the bulk power network. A delay of the load restoration gives time for other corrective actions. The load restoration may be delayed and/or limited by certain countermeasures, such as blocking of Under Load Tap Changers (ULTC). In this paper it was studied the impact of the ULTC on the dynamic voltage collapse of an electric power system with large scale wind generation. It is used the Cigré Electric Power Network with 32 bus and three wind farms equipped with wind turbines, including pitch control coupled with a Fixed Speed Induction Generator (FSIG) and a shunt capacitor bank. The automatic voltage regulators (AVR) of the generating units and the turbine speed governors were modelled. Different load models were used and the ULTC were taken into account. Several significant disturbances were simulated in the test power network, such as the increase of the wind speed, the tripping of an overhead transmission line and three-phase short-circuits. The simulation results were obtained using the EUROSTAG software package. Finally, some conclusions that provide a better understanding of the ULTC effect on the dynamic voltage stability in a system with a large amount of wind power generation are pointed out. © 2012 IEEE.

Abstract
Climate change has arrived: deep, at a global level and sometimes catastrophic. Experts blame the greenhouse gas effect, boosted by the combustion of fossil fuels, which releases these dangerous gases. The purpose of this paper is to analyze the importance of energy efficiency on fighting climate change and how it has been used throughout the world, particularly by Energy Services Companies, also known as ESCOs. © 2012 IEEE.

Abstract
The maintenance costs associated with wind turbines assumes an important weight in the operation of wind parks. The main objective of wind farms operators is to run their parks most economically, to increase their profits. An adequate maintenance planning is essential to an effective operating costs reduction, compared with traditional maintenance techniques. Tools to detect the onset of mechanical and electrical faults in wind turbines at a sufficiently early stage are very important for well planned maintenance actions, because these actions can reduce the outage time and can prevent bigger faults that can lead to machine outage. The amount of information obtained from SCADA systems of wind farms is huge. The use of neural networks to deal with all the information and try to detect faults in some equipment in a early stage is a promising technique. © 2012 IEEE.