In regions with high population densities, the development of wind energy projects situated in an industrial environment or close to cities is a preferred option, since it represents some major advantages. On the othe...In regions with high population densities, the development of wind energy projects situated in an industrial environment or close to cities is a preferred option, since it represents some major advantages. On the other hand, it also represents a drawback in terms of safety during winter conditions. Ice accretion on the wind turbine blades represents a major risk as ice fall may cause incidents, even lethal accidents to people in the vicinity. The current common methodology to identify the potentially risky areas around wind turbines uses a deterministic approach which leads to excessively large zones around the turbines without granularity or circumstantial sub-zones. The approach presented in this paper is a probabilistic risk-based Monte Carlo methodology associated with an acceptance framework. Developed by Engie Tractebel, this methodology allows a much more detailed mapping of the risk zones and also enables to model the impact of mitigating measures. This represents a real risk-based decision tool for windfarm developers and operators. The approach is fully compliant with the IEA (International Energy Agency) Wind “International Recommendations for Ice Fall and Ice Throw Risk Assessments” and recent international safety standards. The tool is available as a cloud-based application called TRiceR.展开更多
文摘In regions with high population densities, the development of wind energy projects situated in an industrial environment or close to cities is a preferred option, since it represents some major advantages. On the other hand, it also represents a drawback in terms of safety during winter conditions. Ice accretion on the wind turbine blades represents a major risk as ice fall may cause incidents, even lethal accidents to people in the vicinity. The current common methodology to identify the potentially risky areas around wind turbines uses a deterministic approach which leads to excessively large zones around the turbines without granularity or circumstantial sub-zones. The approach presented in this paper is a probabilistic risk-based Monte Carlo methodology associated with an acceptance framework. Developed by Engie Tractebel, this methodology allows a much more detailed mapping of the risk zones and also enables to model the impact of mitigating measures. This represents a real risk-based decision tool for windfarm developers and operators. The approach is fully compliant with the IEA (International Energy Agency) Wind “International Recommendations for Ice Fall and Ice Throw Risk Assessments” and recent international safety standards. The tool is available as a cloud-based application called TRiceR.
