Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

Abstract

Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this challenging issue, two techniques such as the open-loop Kalman filter and the compensating closed-loop Kalman filter have emerged. The closed-loop technique compensates for the missing data using the autoregressive model. However, the autoregressive model used only past measurements for data loss compensation. Considering only one parameter, i.e., the past measurements, is insufficient and leads to inaccurate state estimation. Thus, in this work, autoregressive moving average with exogenous inputs model considers three parameters, i.e., the past measurements, the input signal, and the sensor noise, simultaneously to compensate data loss in state estimation. To endorse the effectiveness and applicability of the proposed model, a standard mass-spring-damper is employed in the case study. Simulation results show that the proposed model outperforms the existing autoregressive models in terms of performance parameters.

An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid

“…In recent decades, load forecasting techniques are developed by numerous authors like the time series methods: exponential smoothing [6,7], Kalman filters [8], regression methods [9,10], the grey forecasting model (GM) [11], and the autoregressive integrated moving average (ARIMA), as well as ARMAX methods [12][13][14]. In [15], the authors developed the ARIMA-MPSO model for load forecasting.…”