基于数据学习的新能源高渗透电网频率风险评估
摘要：新能源正在逐步代替传统发电厂为用户提供电能，但同时也为电网的安全运行带来了潜在的风险。因此，在规划阶段需要全面地对最大频率偏差越线风险进行概率评估。基于蒙特卡罗仿真（Monte Carlo simulation，MCS）的规划方法效率很低，而人工神经网络（artificial neural network，ANN）可以通过对数据的学习做出快速有效的预测。为此，提出一种基于MCS-ANN的区域频率概率评估方法，以实现对区域最大频率偏差越线风险的快速评估。首先，产生大量的随机扰动，仅对小部分扰动进行仿真；然后将这部分数据送入ANN进行训练，并将剩余的大部分扰动送入训练好的ANN进行输出预测；重复以上训练和预测的过程，将多次预测结果的平均值作为最终的预测输出，得到各个风险区间的概率分布情况。最后，在IEEE 10机39节点的系统上验证了所提方法的有效性。
Abstract:Renewable energy is gradually replacing traditional power plants to provide electricity for users, but it also brings potential risks to the safe operation of the power grid. Thus, in the planning stage, it is necessary to comprehensively evaluate the probability of violation risk of maximum frequency deviation. Planning method based on Monte Carlo simulation (MCS) is inefficient, while artificial neural network (ANN) can make fast and effective prediction by learning data. Therefore, this paper proposed an MCS-ANN algorithm to realize the rapid assessment of violation risk of regional maximum frequency deviation. Firstly, a large number of stochastic disturbances were generated, and only a small part of disturbances were used for MCS. Then, these data were sent to the neural network for training, and most of the remaining disturbances were sent to the trained neural network for output prediction. The above training and prediction processes were repeated. The average of multiple prediction results was used as the final prediction output, and the probability distribution of each risk interval was obtained. Finally, the effectiveness of the proposed MCS-ANN algorithm was verified on IEEE 10-machine 39-node system.
标题：基于数据学习的新能源高渗透电网频率风险评估
title:Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System
作者：温佳鑫, 卜思齐, 陈麒宇, 周博文
authors:Jiaxin WEN, Siqi BU, Qiyu CHEN, Bowen ZHOU
关键词：新能源,电网安全,频率风险评估,蒙特卡罗仿真(MCS),神经网络(ANN),
keywords:renewable energy,power grid security,frequency risk assessment,Monte Carlo simulation (MCS),artificial neural network (ANN),
发表日期：2021-02-28
- 文件大小:
- 685.33 KB
- 下载次数:
- 60
-
高速下载
|
|
