A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a trad...A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a traditional back-propagation(BP)neural network algorithm,the improved grey wolf optimization(IGWO)algorithm has been adopted to optimize its parameters.The performance of the proposed method has been evaluated by experiments.First,the features of the wind farm are described to show the fundamental information of the experiments.A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines.Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points.Then,the retained wind power data were predicted and analysed by using the proposed IGWO-BP algorithm.Analysis of the results proves the practicability and efficiency of the prediction model.Results show that the average accuracy of prediction is~11%greater than the traditional BP method.In this way,the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy.展开更多
针对导弹打击地面目标时的瞄准点优选问题,提出了一种利用改进灰狼优化算法(improved grey wolf op timization,IGWO)选取最优瞄准点的瞄准点选择方法。该算法基于维度学习的狩猎搜索策略(dimension learning-based hunting,DLH),为每...针对导弹打击地面目标时的瞄准点优选问题,提出了一种利用改进灰狼优化算法(improved grey wolf op timization,IGWO)选取最优瞄准点的瞄准点选择方法。该算法基于维度学习的狩猎搜索策略(dimension learning-based hunting,DLH),为每个瞄准点构建相邻的瞄准点集合,集合中的瞄准点可以互相共享信息,增强局部搜索和全局搜索之间的平衡,并保持多样性。在仿真实验中,将毁伤评估模型的评估函数作为瞄准点选取好坏的评估函数,并且设计导弹打击地面目标的实例对瞄准点选择方法进行验证,实验结果表明,该方法求得的瞄准点具有较高的可信度,为火力筹划中瞄准点的寻优提供了新方法。展开更多
以某2×350 MW火电机组为研究对象,采用改进灰狼算法(improved grey wolf algorithm,IGWO)为辨识方法,将石灰石浆液pH值与净烟气SO_(2)浓度作为模型输出,通过构建决策树,选取相关度较高变量作为输入变量,建立传递函数模型。对厂级SI...以某2×350 MW火电机组为研究对象,采用改进灰狼算法(improved grey wolf algorithm,IGWO)为辨识方法,将石灰石浆液pH值与净烟气SO_(2)浓度作为模型输出,通过构建决策树,选取相关度较高变量作为输入变量,建立传递函数模型。对厂级SIS系统数据进行零初始化、粗大值以及平滑处理,使用IGWO完成不同工况下火电厂湿法脱硫系统传递函数参数的辨识。结果表明,使用IGWO辨识所得不同工况模型输出误差较小,较为符合实际工况,为后续湿法脱硫系统控制研究提供了保障。展开更多
基金This work is supported by the science and technology research project of Jilin Provincial Department of Education(No.JJKH20210260KJ)This work is supported by the Jilin Provincial Department of Education(No.JJKH20210260KJ).
文摘A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China.In order to improve the accuracy of the prediction method using a traditional back-propagation(BP)neural network algorithm,the improved grey wolf optimization(IGWO)algorithm has been adopted to optimize its parameters.The performance of the proposed method has been evaluated by experiments.First,the features of the wind farm are described to show the fundamental information of the experiments.A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines.Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points.Then,the retained wind power data were predicted and analysed by using the proposed IGWO-BP algorithm.Analysis of the results proves the practicability and efficiency of the prediction model.Results show that the average accuracy of prediction is~11%greater than the traditional BP method.In this way,the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy.
文摘针对导弹打击地面目标时的瞄准点优选问题,提出了一种利用改进灰狼优化算法(improved grey wolf op timization,IGWO)选取最优瞄准点的瞄准点选择方法。该算法基于维度学习的狩猎搜索策略(dimension learning-based hunting,DLH),为每个瞄准点构建相邻的瞄准点集合,集合中的瞄准点可以互相共享信息,增强局部搜索和全局搜索之间的平衡,并保持多样性。在仿真实验中,将毁伤评估模型的评估函数作为瞄准点选取好坏的评估函数,并且设计导弹打击地面目标的实例对瞄准点选择方法进行验证,实验结果表明,该方法求得的瞄准点具有较高的可信度,为火力筹划中瞄准点的寻优提供了新方法。
文摘以某2×350 MW火电机组为研究对象,采用改进灰狼算法(improved grey wolf algorithm,IGWO)为辨识方法,将石灰石浆液pH值与净烟气SO_(2)浓度作为模型输出,通过构建决策树,选取相关度较高变量作为输入变量,建立传递函数模型。对厂级SIS系统数据进行零初始化、粗大值以及平滑处理,使用IGWO完成不同工况下火电厂湿法脱硫系统传递函数参数的辨识。结果表明,使用IGWO辨识所得不同工况模型输出误差较小,较为符合实际工况,为后续湿法脱硫系统控制研究提供了保障。
