首页|基于数据驱动的分布式光伏出力两阶段辨识

基于数据驱动的分布式光伏出力两阶段辨识

扫码查看
近年来,户用分布式光伏发展迅猛,其不可观特性给电力部门对电网的规划控制以及用户用能管理带来巨大挑战。鉴于此,该文提出一种基于数据驱动的分布式光伏出力两阶段辨识方法。首先,对智能电表用电数据进行基于特征提取的聚类,以此确定光伏装机的日期范围,获得用户历史负荷信息;其次,提出一种基于数据驱动的分布式光伏出力辨识方法,将分布式光伏出力辨识问题转化为实际负荷缺失数据重建问题,在此基础上,充分考虑净负荷中隐含的光伏特征信息,修正光伏出力辨识结果,实现目标用户光伏出力的精细化辨识。仿真结果表明,与其他数据驱动的光伏分解方法相比,所提方法在标准均方根误差指标上降幅为 1。40和 0。12 kW,在平均绝对百分比误差上降幅为 6。80%和1。36%,准确率增幅为6。00%和2。54%。
Two-stage Identification of Distributed Photovoltaic Active Power Based on Data-driven
In recent years,household distributed photovoltaics have developed rapidly,and the unimpressive characteristics have brought great challenges to the power sector's planning and control of the power grid and the management of user energy consumption.In view of this,this paper proposes a two-stage identification method for distributed photovoltaic output based on missing data reconstruction.First,the net load data of smart meters is clustered based on feature extraction to determine the date range of photovoltaic installation and obtain the historical load information of users.Then,a distributed photovoltaic output identification method based on missing data reconstruction technology is proposed,which transforms the distributed photovoltaic output identification problem into the actual load missing data reconstruction problem.On this basis,the photovoltaic characteristic information hidden in the net load is fully considered,the photovoltaic output identification result is corrected,and the refined identification of the target user's photovoltaic output is realized.The simulation results show that compared with other data-driven PV decomposition methods,the proposed method decreases by 1.40 kW and 0.12 kW in the standard root mean square error index,6.80%and 1.36%in the average absolute percentage error,and 6.00%and 2.54%in the accuracy,respectively.

household distributed photovoltaicdata reconstructionfeature extractionseparation decoupling

杨秀、金煜、顾丹珍、唐乾尧、熊雪君、冯煜尧

展开 >

上海电力大学电气工程学院,上海市 杨浦区 200090

国网上海市电力公司电力科学研究院,上海市 虹口区 200437

户用分布式光伏 数据重建 特征提取 分离解耦

2024

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子:2.712
ISSN:0258-8013
年,卷(期):2024.44(24)