基于改进固有时间尺度分解算法的实时次同步振荡监测方法

为解决大规模风力发电并网系统中频繁发生次同步振荡的问题，需要快速准确地识别和检测次同步振荡的方法。次同步振荡发生时具有时变性和不确定性等特征，这给振荡的实时监测带来了挑战，针对该问题，首先提出了基于引入代数估计法改进的固有时间尺度分解算法(intrinsic time-scale decomposition,ITD）的解决方案。该方法不需要任何先验信息，且其性能不受振荡频率构成的影响。其次，利用合成信号、电磁暂态仿真和振荡实测数据进行了综合对比研究，结果表明该方法在信号检查的动态性能和参数估计精度等方面都取得了良好的效果。最后，通过硬件在环测试，验证了该方法的可行性。

A Real-time Subsynchronous Oscillation Monitoring Method Using Improved Intrinsic Time-scale Decomposition Algorithm

To cope with the frequently occurred subsynchronous oscillation (abbr. SSO) in large-scale grid-connected wind power system, it is necessary to develop a method to identify and detect SSO quickly and accurately. During the occurrence of SSO there are such features as time-varying characteristics and uncertainty and these bring the challenge to the realtime monitoring of SSO. In allusion to this problem, firstly, a solution based on leading in intrinsic time-scale decomposition (abbr. ITD) improved by algebraic estimation was proposed, the proposed solution did not need any priori information and its performance was not affected by the frequency constitution of the SSO. Secondly, by use of synthetic signals, simulation results of Electro-Magnetic Transient Program (abbr. EMTP) and the measured data of SSO the research of comprehensive comparison was conducted, and the research results showed that in the aspects of dynamic performance of signal check and the accuracy of parameter estimation the proposed solution achieved good results. Finally, by means of hardware-in-the-loop test the feasibility of the proposed solution is verified.