Abstract
A novel optimization methodology is proposed for the design of transmission line grounding systems, taking into account technical as well as economical considerations. The problem of designing the grounding systems of transmission fines is stated as a linear-integer programming problem in terms of the construction characteristics and the particular requirements of the tower grounding schemes at the supports of each of the different line sections, in order to minimize the variable investment costs, subject to the maximum allowed line outage rate due to the lightning activity. The mathematical statement of the problem allows solutions in which the transmission tower footing resistance changes along the line, depending on the cost and on the particular characteristics of each tower grounding, assuring however, that the average behavior enforces the desired outage rate due to lightning activity, selecting the complementary electrode scheme required at each tower. The methodology is tested on a real case consisting of a 230 kV transmission line, 85.4 Km long, with 180 towers. The linear programming branch and bound mathematical technique was applied for the solution of the test case. Two different simulation approaches for the calculation of the behavior of the fine subject to lightning phenomena were evaluated without loss of generality: the approach proposed in [1], selected as an initial test due to its simplicity, and the improved version presented in [2]. Results are presented and compared to the design obtained through conventional tower design approaches with important reductions in the investment costs, encouraging the use and further development of the methodology.

Abstract
Decision problems cannot be fully represented without underlying assumptions about the Decision Maker motivations and behavior. This paper describes one technique to build a rule model representing the interaction of preferences of a Decision Maker, by training a Fuzzy Inference System based on examples. © 2005 ISAP.

Abstract
This paper presents a comparison in performance of 3 variants of Genetic Algorithms (GA) vs. 2 variants of Evolutionary Particle Swarm Optimization (EPSO), made in the extremely complex context of a multi-energy market simulation where the behavior of energy retailers is observed. The simulations are on JADE, a FIPA compliant platform based on intelligent autonomous agents running in a cluster of PCs. Each agent formulates its strategy by an inner complex simulation process using a meta-heuristic that tries to define optimum decisions. The results suggest that an EPSO approach is more efficient than GA. © 2005 ISAP.

Abstract
This text introduces a family of Evolutionary Algorithms named EPSO - Evolutionary Particle Swarm Optimization. EPSO algorithms are evolutionary methods that borrow the movement rule from Particle Swarm Optimization methods (PSO) and use it as a recombination operator that evolves under the pressure of selection. This hybrid approach builds up an algorithm that, in several cases, in application to complex problems in Power Systems, has already proven to be more efficient, accurate and robust than classical evolutionary methods or classical PSO. The text presents the description of the method, didactic examples and examples of applications in real world problems. © 2005 ISAP.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high voltage apparatus. A great number of methods are available for condition monitoring and diagnosis of power transformer insulation, but only a few can take direct measurements inside a transformer. Partial discharges are a major source of insulation failure in power transformers and their detection can be achieved through the associated acoustic emissions. This paper reports the development of two sensing head configurations based on extrinsic and intrinsic fiber Fabry-Perot interferometers for the detection of incipient faults in oil-filled power transformers. These sensors can be placed inside the transformer tank without affecting the insulation integrity, improving fault detection and location. The performances of the sensing heads are characterized and compared with the situations where it operates in air, water and oil and promising results are obtained, which will allow the industrial development of practical solutions.

Abstract
The inevitable wind power forecast errors result in differences between forecasted and observed wind power. To mitigate their economic impact, combining the wind power with pumped hydro energy storage may be used. In order to deliver a joint operational strategy for a wind power plant combined with storage, one requires reliable wind power forecasts. The forecasts commonly only consist of a single-value forecast (point forecast) for each look-ahead time. However, additional representative information concerning the forecast uncertainty may be required. In this paper, the influence of uncertainty representation daily operation for a wind power plant combined with pumped hydro storage is discussed. The paper illustrates how uncertainty representations such as Gaussian uncorrelated errors may also not be satisfactory. Based on a case study with a wind power plant combined with pumped hydro storage, organized for illustrative purposes, the paper demonstrates the need to have uncertainty representation including cross-period dependencies, in order to define a correct operation policy. © 2011 IEEE.