基于卷积神经网络和层次分析的新能源电源调频能力智能预测方法

doi:10.6040/j.issn.1672-3961.0.2022.174

摘要/Abstract

摘要： 针对新型电力系统实时运行期间,各个新能源机组的一次调频能力难以定量评估问题,源于数据驱动理论,提出一种基于卷积神经网络(convolutional neural networks, CNN)和层次分析法的新能源机组调频能力综合评估方法。以新能源机组有功出力、调频持续时间以及机组容量为指标,建立评估指标体系;采用卷积神经网络技术合理预测不同指标的数值,并通过层次分析法确定各指标间的相对权重。在PSCAD/EMTDC仿真软件中搭建包含风电场和光伏电站的IEEE 3机9节点模型进行仿真验证,算例结果表明所提出指标可以在数值上定量反映出各个新能源机组的调频能力,也验证了文中所提评估方法的有效性。

关键词: 新能源电源, 一次调频能力, 数据驱动理论, 评估指标体系, 卷积神经网络, 层次分析法

中图分类号:

TM615

王智伟,徐海超,郭相阳,马炯,褚云龙,陈前昌,卢治. 基于卷积神经网络和层次分析的新能源电源调频能力智能预测方法[J]. 山东大学学报 (工学版), 2022, 52(5): 70-76.

WANG Zhiwei, XU Haichao, GUO Xiangyang, MA Jiong, CHU Yunlong, CHEN Qianchang, LU Zhi. Intelligent method based on convolutional neural network and analytic hierarchy process to predict frequency regulation ability of renewable energy power[J]. Journal of Shandong University(Engineering Science), 2022, 52(5): 70-76.

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