文章摘要
管霖,赵琦,周保荣,等.基于多尺度聚类分析的光伏功率特性建模及预测应用[J].电力系统自动化,2018,42(15):24-30. DOI: 10.7500/AEPS20171027003.
GUAN Lin,ZHAO Qi,ZHOU Baorong, et al.Multi-scale Clustering Analysis Based Modeling of Photovoltaic Power Characteristics and Its Application in Prediction[J].Automation of Electric Power Systems,2018,42(15):24-30. DOI: 10.7500/AEPS20171027003.
基于多尺度聚类分析的光伏功率特性建模及预测应用
Multi-scale Clustering Analysis Based Modeling of Photovoltaic Power Characteristics and Its Application in Prediction
DOI:10.7500/AEPS20171027003
关键词: 光伏模型  太阳辐照度  光伏功率曲线预测  聚类分析  短期波动
KeyWords: photovoltaic model  solar irradiance  photovoltaic power curve prediction  clustering analysis  short-term fluctuation
上网日期:2018-07-03
基金项目:中国南方电网有限责任公司重点科技项目(CSGTRC-K163007)
作者单位E-mail
管霖 华南理工大学电力学院, 广东省广州市 510641  
赵琦 华南理工大学电力学院, 广东省广州市 510641  
周保荣 南方电网科学研究院有限责任公司, 广东省广州市 510663  
吕耀棠 华南理工大学电力学院, 广东省广州市 510641 traccce_tam@163.com 
赵文猛 南方电网科学研究院有限责任公司, 广东省广州市 510663  
姚文峰 南方电网科学研究院有限责任公司, 广东省广州市 510663  
摘要:
      光伏功率曲线的建模和预测是电网运行调度和规划建设普遍关注的问题。对大量光伏电站录波数据的挖掘和利用为波动性光伏功率的建模提供了新的方向。提出了一种多尺度分析策略来实现对光伏功率时间序列的固有和随机特性的挖掘和建模。结合大时间尺度的多子集聚类分析和小时间尺度的标准化变换和概率分布建模方法,实现了对光伏功率波动性和规律性的挖掘,并与气象领域的理论模型进行了交互验证。最后通过实际光伏电站的日前功率预测实例验证了所述方法和模型的有效性。
Abstract:
      The modeling and prediction of photovoltaic(PV)power curve are a widely concerned issue in the power grid operation and planning. Mining and utilization of the big data recorded by the PV stations provide a new and promising direction for the modeling of the fluctuating PV power. A multi-scale analysis scheme is presented to realize the digging and modeling for the fixed and stochastic characteristics of the PV power time sequence. By combining the multiple cluster analysis at large time-scale, the standardized transformations at small time-scale, and the probability distribution modeling, the digging to the fluctuated and regular characteristics of PV power is realized. The clustering results are also verified by cross validation with the theoretical models in the meteorology field. The effectiveness of the proposed method and model is verified by an example of day-ahead PV power forecasting.
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