Adaptive Control Strategy for Inertia and Damping of Virtual Synchronous Generator Based on CNN-LSTM Data-Driven Model  

基于CNN-LSTM数据驱动模型的VSG转动惯量和阻尼系数的自适应控制策略

作　　者：LUAN Xiyu ZENG Guohui ZHAO Jinbin TIAN Jiangbin ZHANG Zhenhua LIU Jin 栾希宇;曾国辉;赵晋斌;田江斌;张振华;刘瑾(上海工程技术大学电子电气工程学院,上海201620;上海电力大学海上风电技术教育部工程研究中心,上海200090)

机构地区：[1]School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China [2]Engineering Research Center of Offshore Wind Technology Ministry of Education,Shanghai University of Electric Power,Shanghai 200090,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第6期579-588,共10页武汉大学学报(自然科学英文版)

摘　　要：With the application of distributed power sources,the stability of the power system has been dramatically affected.Therefore,scholars have proposed the concept of a virtual synchronous generator(VSG).However,after the system is disturbed,how to make it respond quickly and effectively to maintain the stability of the system becomes a complex problem.To address this problem,a frequency prediction component is incorporated into the control module of the VSG to enhance its performance.The Convolutional Neural NetworkLong Short-Term Memory(CNN-LSTM)model is used for frequency prediction,ensuring that the maximum energy capacity released by the storage system is maintained.Additionally,it guarantees that the inverter's output power does not exceed its rated capacity,based on the predicted frequency limit after the system experiences a disturbance.The advantage of real-time adjustment of inverter parameters is that the setting intervals for inertia and damping can be increased.The selection criteria for inertia and damping can be derived from the power angle oscillation curve of the synchronous generator.Consequently,an adaptive control strategy for VSG parameters is implemented to enhance the system's frequency restoration following disturbances.The validity and effectiveness of the model are verified through simulations in Matlab/Simulink.虚拟同步发电机(VSG)通过提供惯量和阻尼支撑来提高电力系统的稳定性。然而,系统受到干扰后,如何使VSG快速有效地响应以维持系统稳定成为一个难题。为了提升VSG的性能,本文在VSG的控制模块中加入了频率预测模块,CNN-LSTM模型常用于处理时间序列数据,本文使用此模型来预测系统受到干扰后的频率值。本文在满足储能所释放的最大能量不变和逆变器的输出功率不超过额定容量的前提下,基于系统受到干扰后预测频率的极限值,并利用逆变器参数实时调整的优势,增大了惯量和阻尼的参数区间。惯性和阻尼的选择标准通过同步发电机的功角振荡曲线获得。采用的VSG参数的自适应控制策略增强了系统在受到干扰后的频率恢复能力。在Matlab/Simulink中进行了仿真,试验结果验证了模型的合理性和有效性。

关 键 词：virtual synchronous generator(VSG) adaptive control frequency restoration convolutional neural network-long short-term memory(CNN-LSTM) 

分 类 号：TP203[自动化与计算机技术—检测技术与自动化装置]