基于电力系统同步多参量测量的自记忆轨迹预测算法

基于大气运动自记忆预测方法,导出了使用历史观测的多状态量预测电力系统微分方程中某一状态量的自记忆数值预测计算公式,并提出了基于广域测量系统的电力系统功角综合预测方案。该方案在每一采样时刻首先基于临界机组不平衡功率的拟正弦特性使用正弦三角函数滚动预测临界机组不平衡功率的变化,然后使用多个同步状态量的自记忆预测公式预测临界等值机组的角速度和功角。IEEE145节点系统和某省网实际系统仿真结果显示该方案具有精度高和稳定性好的优点,其能够稳定可靠地预测未来0.4s系统轨迹的变化。

Self-memory prediction based on synchronous measurement of power system multi-variable

According to the self-memory method of atmospheric motion prediction,the self-memory numerical prediction formula is derived for predicting the variable in power system dynamic differential equation by using the observed data of multi-variable. The integrated power system angle prediction scheme is proposed based on the wide-area measurement system,in which,the unbalanced power of the critical machine is predicted based on sine trigonometric function at iterative sampling instant,considering it similar to sinusoid,then the rotor angle and speed of the critical machine are calculated by the multi-variable self-memory numerical predictive formula. The numerical results of IEEE 145-bus system and a provincial real power system show that,the proposed method has high precision and good stability. It can predict the system trajectory 0.4 s ahead.