Abstract
Different risks are associated with the operation and maintenance of wind farms in cold climate regions, mainly due to the harsh weather conditions that wind farms experience in that region such as the (i) increased stoppage rate of wind turbines due to harsh weather conditions, (ii) limited accessibility to wind farms due to snow cover on roads, and (iii) cold stress to workers at wind farms. In addition, there are risks that are caused by wind farms during their operation, which impact the surrounding environment and community such as the (iv) risk of ice throw from wind turbines, (v) environmental risks caused by the wind farms, and (vi) social opposition risk to installing wind farms in cold climate regions, such as the Arctic. The analysis of these six risks provides an overall view of the potential risks encountered by designers, operators, and decision makers at wind farms. This paper presents a methodology to quantify the aforementioned risks using fuzzy logic method. At first, two criteria were established for the probability and the consequences of each risk; with the use of experts’ judgments, membership functions were graphed to reflect the two established criteria, which represented the input to the risk analysis process. Furthermore, membership functions were created for the risk levels, which represented the output. To test the proposed methodology, a wind farm in Arctic Norway was selected as a case study to quantify its risks. Experts provided their assessments of the probability and consequences of each risk on a scale from 0–10, depending on the description of the wind farm provided to them. Risk levels were calculated using MATLAB fuzzy logic toolbox and ranked accordingly. Limited accessibility to the wind farm was ranked as the highest risk, while the social opposition to the wind farm was ranked as the lowest. In addition, to demonstrate the effects of the Arctic operating conditions on performance and safety of the wind farm, the same methodology was applied to a wind farm located in a non-cold-climate region, which showed that the risks ranked differently.
Highlights
Wind energy applications in cold climate regions (CCRs) have gained more attention recently, and are growing at a rapid rate of approximately 20% per year according to the Global Wind Energy Council [1]
This paper aims at providing an overall analysis and ranking of these risks, which can help designers of wind farms (WFs), risk managers, and operators acquire a holistic image of the potential risks, which will contribute to the prioritizing of their decisions in case of the lack of sufficient data that is usually encountered in CCRs, due to the fact that wind energy applications in that region are relatively new [4]
Each one of these variables contains a number of objects that were previously defined in X as follows: X = {very low, low, medium, moderate, high, very high, moderate-high, extremely high} Input variable = {very low, low, medium, high, very high} Input variable = {low, moderate, high, very high} Output variable = {very low, low, moderate, moderate-high, high, very high, and extremely high}
Summary
Wind energy applications in cold climate regions (CCRs) have gained more attention recently, and are growing at a rapid rate of approximately 20% per year according to the Global Wind Energy Council [1]. Wind turbines (WTs) in CCRs experience temperatures below their standard operational limits and may experience incidents of icing conditions Such weather conditions can result in risks that will have negative impacts on WFs, and can affect their surrounding environment and community. This paper aims at providing an overall analysis and ranking of these risks, which can help designers of WFs, risk managers, and operators acquire a holistic image of the potential risks, which will contribute to the prioritizing of their decisions in case of the lack of sufficient data that is usually encountered in CCRs, due to the fact that wind energy applications in that region are relatively new [4]. This paper utilizes fuzzy logic and experts’ judgments to rank six types of risks to and from WFs in CCRs, mainly in the Arctic region. The risks are ranked depending on the resulting risk level, the highest risk level was assigned a rank of (1) and the lowest risk was assigned a rank of (6)
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