学术论文

      日风速随机模糊不确定模型

      Random Fuzzy Uncertain Model for Daily Wind Speed

      摘要:
      传统风速不确定模型无法兼顾其随机性和模糊性共存特征,该文考虑风速概率分布特征及其参数模糊性的随机模糊变量特征,提出一种日风速随机模糊不确定模型.考虑风速地域、季节和日特征差异,以美国 NREL 和我国某风电场实测风速为实例,提取多年同月日96时段和同月日同时段风速随机性特征,发现实例数据中接近或大于 70%的时段服从两参数Weibull分布且同时段具有相似性,而形状参数k和尺度参数c存在模糊性;利用极大似然法挖掘k和c的模糊隶属函数特征,并在一定置信区间内定义其边界;依据不确定理论,定义风速为随机模糊变量且获取其机会测度分布函数,从而建立日风速不确定模型;最后,给出相应建模步骤及结合随机模糊模拟技术和逆变换法的日风速随机模糊模拟方法,5 000 次模拟结果表明各时段风速处于历史相应时段风速上下限的概率大于 94.13%,可有效用于日风速仿真.
      Abstract:
      The traditional wind speed uncertain model is limited in taking its coexisting randomness and fuzziness together into account. Therefore, this paper considered the probability distribution of wind speed and fuzziness of its parameters, and proposed a random fuzzy uncertain model for daily wind speed. By considering regional, seasonal and daily feature and taking measured data from NREL in America and a wind farm in northeast China as cases, random features of daily wind speed data of the same months from several years split into 96 periods and wind speed data of the same period of days at the same months from different years were extracted. It was found that, measured data in close to or more than 70% periods obeys Weibull distribution with double parameters, and data from the same periods possesses similarity, but shape parameterk and scale parameterc are of fuzziness. After data digging about fuzzy membership function characters ofk andc by maximum likelihood method, limits of them were defined under a certain confidence level. Then, according to uncertain programming theory, wind speed was defined as a random fuzzy variable and chance measure distribution function for wind speed was obtained, thereby the wind speed uncertain model was established. Finally, modeling process and daily wind speed random fuzzy simulation method based on random fuzzy simulation technology and inverse transformation method were provided. 5000 times of simulation results show that the probability when simulation data is in historical data limits of corresponding periods is more than 94.13%, proving the effectiveness of the model in simulating random fuzzy wind speed.
      Author: MA Rui ZHANG Qiang WU Xia LI Xuan
      作者单位: 长沙理工大学电气与信息工程学院,湖南省长沙市,410114
      刊 名: 中国电机工程学报 ISTICEIPKU
      年,卷(期): 2015, 35(24)
      分类号: TM614
      在线出版日期: 2016年2月25日
      基金项目: 国家自然科学基金项目(51277015). Project Supported by National Natural Science Foundation of China