基于卡尔曼滤波的电力系统运行状态实时监测

Real Time Monitoring Method of Power System Operation State Based on Kalman Filter

在监测电力系统运行状态时,监测数据与电力系统的实时数据存在异步问题,导致监测结果的精度偏低,因此提出基于卡尔曼滤波的电力系统运行状态实时监测方法。在对电力系统数据进行同步处理时,引入了卡尔曼滤波器,从噪声污染的电力系统运行状态数据中分离出目标电力系统运行状态数据,并构建了对应的目标电力系统运行状态数据状态方程和观测方程。在电力系统运行状态监测阶段,引入神经网络对电力系统的同步误差分析结果进行深度学习,以此实现对监测结果的有效补偿,实现精准监测。在测试结果中,设计方法对于电力系统最大输入电压监测结果与设置值的误差始终稳定在1.0 V以内,具有较高的稳定性和准确性。

When monitoring the operation status of power system,there is a certain degree of asynchronous problem between the monitoring data and the real-time data of power system,which leads to the low accuracy of the monitoring results.Therefore,a real-time monitoring method for the operation status of power system based on Kalman filter is proposed.When the power system data is processed synchronously,the Kalman filter is introduced to effectively separate the target power system operation state data from the noise polluted power system operation state data,and the corresponding target power system operation state data equation of state and observation equation are constructed.In the stage of monitoring the operation status of the power system,a neural network is introduced to deeply learn the synchronization error analysis results of the power system,in order to achieve effective compensation for the monitoring results and achieve precise monitoring.In the test results,the design method consistently maintains an error of within 1.0 V between the maximum input voltage monitoring results of the power system and the set value,demonstrating high stability and accuracy.