Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic Capacity Assessment of Lattice Transmission Towers under Strong Wind
Serving as one key component of the most important lifeline infrastructure system, transmission towers are vulnerable to multiple nature hazards including strong wind and could pose severe threats to the power system security with possible blackouts under extreme weather conditions, such as hurricanes, derechoes, or winter storms. For the security and resiliency of the power system, it is important to ensure the structural safety with enough capacity for all possible failure modes, such as structural stability. The study is to develop a probabilistic capacity assessment approach for transmission towers under strong wind loads. Due to the complicated structural details of lattice transmission towers, wind tunnel experiments are carried out to understand the complex interactions of wind and the lattice sections of transmission tower and drag coefficients and the dynamic amplification factor for different panels of the transmission tower are obtained. The wind profile is generated and the wind time histories are simulated as a summation of time-varying mean and fluctuating components. The capacity curve for the transmission towers is obtained from the incremental dynamic analysis (IDA) method. To consider the stochastic nature of wind field, probabilistic capacity curves are generated by implementing IDA analysis for different wind yaw angles and different randomly generated wind speed time histories. After building the limit state functions based on the maximum allowable drift to height ratio, the probabilities of failure are obtained based on the meteorological data at a given site. As the transmission tower serves as the key nodes for the power network, the probabilistic capacity curves can be incorporated into the performance based design of the power transmission network.
Probabilistic Capacity Assessment of Lattice Transmission Towers under Strong Wind
Serving as one key component of the most important lifeline infrastructure system, transmission towers are vulnerable to multiple nature hazards including strong wind and could pose severe threats to the power system security with possible blackouts under extreme weather conditions, such as hurricanes, derechoes, or winter storms. For the security and resiliency of the power system, it is important to ensure the structural safety with enough capacity for all possible failure modes, such as structural stability. The study is to develop a probabilistic capacity assessment approach for transmission towers under strong wind loads. Due to the complicated structural details of lattice transmission towers, wind tunnel experiments are carried out to understand the complex interactions of wind and the lattice sections of transmission tower and drag coefficients and the dynamic amplification factor for different panels of the transmission tower are obtained. The wind profile is generated and the wind time histories are simulated as a summation of time-varying mean and fluctuating components. The capacity curve for the transmission towers is obtained from the incremental dynamic analysis (IDA) method. To consider the stochastic nature of wind field, probabilistic capacity curves are generated by implementing IDA analysis for different wind yaw angles and different randomly generated wind speed time histories. After building the limit state functions based on the maximum allowable drift to height ratio, the probabilities of failure are obtained based on the meteorological data at a given site. As the transmission tower serves as the key nodes for the power network, the probabilistic capacity curves can be incorporated into the performance based design of the power transmission network.
Probabilistic Capacity Assessment of Lattice Transmission Towers under Strong Wind
Wei eZhang (Autor:in) / Jin eZhu (Autor:in) / Huijuan eLiu (Autor:in) / Huawei eNiu (Autor:in)
2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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