Wind turbine indirect ice detection and operational analysis

Wind energy is one of the most promising and fast growing sector of energy production. Wind is ecologically friendly and relatively cheap energy resource available for development in practically all corners of the world (where only the wind blows). Today wind power gained broad development in the Scandinavian countries. Three important challenges concerning sustainable development, i.e. energy security, climate change and energy access make a compelling case for large-scale utilization of wind energy. In Finland, according to the climate and energy strategy, accepted in 2008, the total consumption of electricity generated by means of wind farms by 2020, should reach 6 - 7% of total consumption in the country [1]. The main challenges associated with wind energy production are harsh operational conditions that often accompany the turbine operation in the climatic conditions of the north and poor accessibility for maintenance and service. One of the major problems that require a solution is the icing of turbine structures. Icing reduces the performance of wind turbines, which in the conditions of a long cold period, can significantly affect the reliability of power supply. In order to predict and control power performance, the process of ice accretion has to be carefully tracked. There are two ways to detect icing – directly or indirectly. The first way applies to the special ice detection instruments. The second one is using indirect characteristics of turbine performance. One of such indirect methods for ice detection and power loss estimation has been proposed and used in this paper. The results were compared to the results directly gained from the ice sensors. The data used was measured in Muukko wind farm, southeast Finland during a project 'Wind power in cold climate and complex terrain'. The project was carried out in 9/2013 - 8/2015 with the partners Lappeenranta university of technology, Alstom renovables España S.L., TuuliMuukko, and TuuliSaimaa.

Uncertainty Management in Acquisitions: What Scenario Analysis Could Offer

This study answers to How scenario analysis could help acquiring companies to reduce uncertainty in the acquisition process? It is due to the mismatch between academic world’s caveat emptor and business world’s eagerness to pursue acquisitions that motivated this study. Acquisitions are as popular as ever, thus, managing the uncertainty surrounding these transactions is relevant. This study creates a generic theoretical model with a strategy-level scope. Thus, the study does not discuss nor does it seek answers to operational issues related in both fields. This study is explorative and constructivist in nature. It discusses briefly the concepts and relatedness of risk and uncertainty and establishes a hierarchy between these two: Risks being a “sub-section” of uncertainty, although not with clear boundaries. Acquisition theory follows the process view that understands acquisitions as a process with various levels – some strategic, some operational. Scenario analysis is presented as tool for management to enrich their strategic discussion and understand their future options. The empirical data collection is done through interviewing. The results are reflected on literature on strategic management, scenario literature, and on a consultancy’s report picturing firm’s strategies in accordance with their acquisition processes. The study has an abductive approach as it tries to combine multiple views and generates discussion between literature review, interviews, the report, and second round of literature. The model suggests three propositions: First, at the strategic decision making level, when the decision whether or not to pursue an acquisition growth strategy has been made, it provides firms new data and enriches the strategic discussion. Second, when the acquisition strategy has been created, it can be applied as a tool to measure possible acquisition targets against the backdrop of the first set of scenarios. Third, due to the scenario analysis’ requirement to include people with various backgrounds and from multiple levels of the corporate hierarchy, it could help managers to avoid biases stemming from hubris.