摘要
限流策略、源源交互、故障及负荷水平多变等因素使得快速准确评估多虚拟同步机(VSG)微电网的暂态稳定性十分困难。针对现有难题,提出了基于深度学习的多VSG微电网在线暂态稳定评估方法。首先,通过分析VSG控制特性、电流限幅器、故障程度、负荷水平对系统稳定性的影响,以系统动态参数为主、稳态参数为辅,构建了一组具有强表征能力、可避免维数灾难的原始特征集。基于此,应用深度前馈神经网络及Levenberg-Marquardt算法,提出了多VSG微电网暂态稳定非线性评估模型。在多VSG微电网中的验证结果表明,相比现有方法,所提方法极大地提高了在线暂态稳定评估的准确率,可快速实现多VSG微电网在复杂工况下的稳定性准确判别,具有良好的评估性能。
The factors such as the current limiting strategy,the source-source interaction,the variable fault and load level make it very difficult to quickly and accurately assess the transient stability of the microgrid with multiple virtual synchronous generators(VSGs).Aiming at the existing problems,this paper proposes an online transient stability assessment method for the microgrid with multiple VSGs based on the deep learning.First,by analyzing the influence of VSG control characteristics,current limiter,fault level,and load level on the system stability,a set of original features with the abilities of strong characterization and avoiding dimensionality disasters is constructed with the principle of system dynamic variables as the mainstay and steady-state parameters as the supplement.Based on this,a transient stability nonlinear assessment model for the microgrid with multiple VSGs is proposed with the application of deep feedforward neural network and Levenberg-Marquardt algorithm.The verification results in the microgrid with multiple VSGs show that,compared with the existing methods,the proposed method greatly improves the accuracy of the online transient stability assessment,and can quickly realize the accurate stability judgment of the microgrid with multiple VSGs under complex working conditions,which prove that the proposed method has a good assessment performance.
作者
赵慧敏
帅智康
沈阳
程慧婕
赵峰
沈霞
ZHAO Huimin;SHUAI Zhikang;SHEN Yang;CHENG Huijie;ZHAO Feng;SHEN Xia(National Electric Power Conversion and Control Engineering Technology Research Center(Hunan University),Changsha 410082,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2022年第9期109-117,共9页
Automation of Electric Power Systems
基金
国家自然科学基金面上项目(51977066)
湖南省研究生科研创新项目(QL20210104)。
关键词
虚拟同步机
在线暂态稳定评估
输入特征选择
深度学习
前馈神经网络
virtual synchronous generator(VSG)
online transient stability assessment
input feature selection
deep learning
feedforward neural network
