基于灰色关联决策的间歇式电源数据采集粒度标定

Data Collection Granularity Selecting in Intermittent Energy Power Generation Based on Grey Incidence Decision

针对目前间歇式电源出力特性研究中,所采用的发电数据采集粒度λ不一致且缺乏确切理论依据的情况,该文以光伏发电为例,提出一种电站功率持续变动状态的识别方法,提取光伏电站若干个持续出力状态,用以挖掘光伏电站持续的功率变动特征,以每个持续状态的能量变化、曲线变动斜率、状态持续时间和功率变动差值为模型输入量,建立灰色多属性决策模型,通过兼顾每个持续状态中的能量变化、功率变化率、持续时间和变动幅值以寻找模型次优解的方法挖掘"刚性关注需求"变动状态,以此界定光伏发电数据最佳采集粒度λ。

Considering the difference and lack of theoretical basis situation in data collection granularity 2 selecting in intermittent energy power generation, this paper presented a photovoltaic （PV） power plant power sustained movement recognition method. The method extracts a number of sustained movement states to mine the PV power plant power changing characteristics, then, established the Grey multiple attribute decision making model by using the energy change, curve change slope, state duration time and power changes of each state as the model input. Mining the ＇Rigid Focus Demand＇ through the method of looking for secondary optimal solution of model, by balancing the energy change, power change rate, duration time and changing amplitude of each sustained movement state. Then selecting the best data collection granularity λ.