Hybrid method for multi-rate refined oil pumping station system unsteady state estimation with bad data attacks

With the recent advancement of products pipelines digitization, a large number of sensors have been installed in pumping stations for real-time flow parameters measurement. In these asynchronous multi-sensor systems, data missing and false data attacks are likely to occur when performing online operation monitoring of the oil pipeline system. In this paper, a hybrid state estimation method is proposed to process both the missing and fault measurement, considering the dynamic operation process of the whole system. Combing frequency-domain analysis method with model-free adaptive control algorithm, the state estimation model with adaptive deviation compensation is established to characterize the nonlinear transient flow process of the pumping station. And the Kalman Filter method is adopted to overcome the interference of sensor noise. In terms of multi-rate observation data processing, this study innovatively proposes an algorithm based on the first principle and generalized predictive control theory to improve the accuracy of traditional missing data processing methods based on statistical analysis. Moreover, non-obvious abnormal observations are identified by introducing long short-term memory network characterized by deviations between sensor measurements and multi-rate state estimation results. To verify the effectiveness of proposed method, it is adopted to the unsteady state estimation of a refined oil pumping station system under the attack of noise, nonuniform asynchronous sampling and insignificant abnormal data.