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多约束下光储系统的灵活虚拟惯性控制方法 被引量:17

Multi-Constrained Flexible Virtual Inertial Control Method for Photovoltaic Energy Storage System
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摘要 电网中分布式电源的大量接入降低了系统的旋转惯性,虚拟同步发电机(VSG)技术作为解决这一问题的有效途径,其虚拟惯量的灵活可调具有显著优势,但其灵活调节时必须满足多种约束条件的限制。该文结合实际工况提出一种多约束限制下的灵活虚拟惯性(M-FVI)控制方法,该方法兼顾储能单元的极限工作状态、系统频率变化率、换流器容量以及系统单位时间功率可调量的影响。搭建含光储VSG单元的四端系统模型,并建立该系统的小信号模型,利用根轨迹法分析所提控制策略中关键参数对系统稳定性的影响规律,给出了控制参数的设计方法及原则。最后,通过硬件在环实验平台验证了所提多约束下虚拟惯性控制策略的有效性与实用性。 Rotational inertia of power system is reduced because of the large number of integrated distributed generators.As an effective way to solve this problem,virtual synchronous generator(VSG)technology has significant advantages in flexible adjustment of virtual inertia.However,the flexibility of the adjustment must meet various constraints.In this paper,a multi-constrained flexible virtual inertia(M-FVI)control method is proposed in combination with the actual working conditions.This method considers the limit working state of the energy storage unit,the system frequency change rate,the inverter capacity and the power adjustable amount of the system in unit time.The four-terminal system model with photovoltaic energy storage VSG unit is built,and the small-signal model of the system is established.The influence of key parameters on the system stability is analyzed by root locus method.The method and principle need to be followed for the design of control parameters are given.Finally,the effectiveness and practicability of the multi-constrained virtual inertial control strategy are verified by the hardware-in-the-loop experimental platform.
作者 孟建辉 彭嘉琳 王毅 宋美琪 郭禹 Meng Jianhui;Peng Jialin;Wang Yi;Song Meiqi;Guo Yu(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding 071003 China)
出处 《电工技术学报》 EI CSCD 北大核心 2019年第14期3046-3058,共13页 Transactions of China Electrotechnical Society
基金 国家自然科学基金(51807064) 河北省自然科学基金(E2018502152)资助项目
关键词 光储系统 灵活虚拟惯性 多约束 小信号模型 稳定性分析 Photovoltaic energy storage system flexible virtual inertia multiple constraints small signal model stability analysis
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