With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind fa...With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind farm. Firstly, considering the large number of wind units and the high dimensionality of the scheduling solutions, we analyze the unit load characteristics, from which we extract the unit load characteristic matrix, and then classify the wind power units with the FCM fuzzy clustering algorithm. Secondly, we define the running loss indicator and action loss indicator. Based on the prediction of wind power and the load instructions, we establish a unit commitment model in wind farm, and solve the model using a combination of the fuzzy clustering algorithm and genetic algorithm, which overcomes the difficulty of the high dimensionality of the solution in the wind farm scheduling problem, to obtain the optimal scheduling strategy. Finally, through the simulation of the scheduling strategy for a 45 MW wind farm, we demonstrate the feasibility and effectiveness of the proposed strategy.展开更多
High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an ef...High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.展开更多
基金supported by the National Basic Research Program of China ("973" Project) (Grant No. 2012CB215203)the National Natural Science Major Fund Project (Grant No. 51036002)
文摘With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind farm. Firstly, considering the large number of wind units and the high dimensionality of the scheduling solutions, we analyze the unit load characteristics, from which we extract the unit load characteristic matrix, and then classify the wind power units with the FCM fuzzy clustering algorithm. Secondly, we define the running loss indicator and action loss indicator. Based on the prediction of wind power and the load instructions, we establish a unit commitment model in wind farm, and solve the model using a combination of the fuzzy clustering algorithm and genetic algorithm, which overcomes the difficulty of the high dimensionality of the solution in the wind farm scheduling problem, to obtain the optimal scheduling strategy. Finally, through the simulation of the scheduling strategy for a 45 MW wind farm, we demonstrate the feasibility and effectiveness of the proposed strategy.
文摘High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.
