基于OKID的损耗因子辨识方法研究

提出了利用观测器/卡尔曼滤波辨识(OKID)方法辨识系统的最小阶功率流模型.首先直接由测量的输入功率和能量的时域数据辨识功率流系统的Markov参数,然后利用特征系统实现算法得到功率流模型的最小实现.同时对统计能量分析模型修正方法进行了改进,将初始模型子系统间的耦合信息作为附加约束条件,并根据约束最优化思想寻求初始损耗因子相对误差矩阵的最小范数解,进一步可得到辨识的损耗因子参数.通过5子系统结构的实例仿真分析,验证了OKID功率流模型辨识方法可行并具有很好的抗噪性能;与传统SEA模型修正算法相比,改进方法对初始SEA模型有更好的适应性,修正参数更好地保持了物理意义.研究的辨识方法可有效提高内损耗因子和耦合损耗因子的实验辨识精度,扩展实验SEA参数识别的工程应用范围.

Research of loss factor identification based on observer Kalman filter identification(OKID)

Reliability of statistical energy analysis(SEA) models depends on good estimates of SEA parameters,such as coupling loss factor,damping loss factor and modal density.The power flow realization method(PRM) combined with SEA model improvement(SMI) is an approach for identifying SEA parameters from experimental data,which need not to excite every subsystem sequentially.However,the pulse response or Markov parameters of power flow system and reasonable initial model are necessary for successful updating.An impr...