Abstract
This paper discusses risk management, contracting, and bidding for a wind power producer. A majority of the wind power in the United States is sold on long-term power purchase agreements, which hedge the wind power producer against future price risks. However, a significant amount is sold as merchant power and therefore is exposed to fluctuations in future electricity prices (day-ahead and real-time) and potential imbalance penalties. Wind power forecasting can serve as a tool to increase the profit and reduce the risk from participating in the wholesale electricity market. We propose a methodology to derive optimal day-ahead bids for a wind power producer under uncertainty in realized wind power and market prices. We also present an initial illustrative case study from a hypothetical wind site in the United States, where we compare the results of different day-ahead bidding strategies. The results show that the optimal day-ahead bid is highly dependent on the expected day-ahead and real-time prices, and also on the risk preferences of the wind power producer. A deviation penalty between day-ahead bid and real-time delivery tends to drive the bids closer to the expected generation for the next day.

Abstract
This paper reports new results in adopting information theoretic learning concepts in the training of neural networks to perform wind power forecasts. The forecast "goodness" is discussed under two paradigms: one is only concerned in measuring the deviation between the forecasted and realized values, the other is related with the value of the forecast in the electricity market for different agents. The results and conclusions are supported by a real case example.

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Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high-voltage apparatus. Partial discharges are a source of failure in power transformers, and the differentiation from other sources of acoustic emissions is of the utmost importance. This paper reports the development of a new sensor concept-mandrel-based fiber-optic sensor-for the detection of incipient faults in oil-filled power transformers, taking direct measurements inside a transformer. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure, and improving fault detection and location. The applicability of these acoustic sensors in air, water, and oil is investigated and the paper presents the promising results obtained, which will enable the industrial development of practical solutions.

Abstract
A natural movement towards artificial intelligence (AI) techniques took place in the last years in power system analysis. Many research works have used AI topics like search techniques, knowledge representation, reasoning and learning systems, as well as heuristic tools to address power system problems. This paper focuses the discussion on power system reliability evaluation and this natural transition from AI topics to a more sophisticated software design, known as intelligent agent (IA) technology. Instead of applying AI techniques to improve a single stage of the Monte Carlo Simulation (MCS), the IA architecture explores new ways to support AI topics. However, this natural movement needs to be managed through the proposal of a modern framework of power system tools, where several different techniques have to be combined in order to maximize each one's benefits and advantages. © 2010 IEEE.