SCKF-LSTM Based Trajectory Tracking for Electricity-Gas Integrated Energy System

TL;DR

This paper presents a combined Kalman filter and LSTM-based method for accurately tracking the dynamic states of integrated natural gas and power systems, improving over traditional measurement techniques.

Contribution

It introduces a novel combined approach using square-root cubature Kalman filter and LSTM for joint gas and power system trajectory tracking.

Findings

High tracking accuracy demonstrated in simulations.

Outperforms traditional measurement-based methods.

Effective in handling nonlinear system equations.

Abstract

This paper introduces a novel approach for tracking the dynamic trajectories of integrated natural gas and power systems, leveraging a Kalman filter-based structure. To predict the states of the system, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, respectively. The square-root cubature Kalman filter algorithm is utilized to address the numerical challenges posed by the strongly nonlinear system equations. The boundary conditions in the gas system include the flow balances at sink nodes, and the mass flow rates of loads have to be predicted at each computation step. For the prediction of load mass flows, the long short-term memory network is employed, known for its effectiveness in time series prediction. Consequently, a combined method based on the square-root cubature Kalman filter and the long short-term memory network is proposed for tracking integrated gas and power systems. To evaluate the tracking performances of the proposed method, the IEEE-39 bus power system and GasLib-40 node gas system are used to form the testing system. Simulation results demonstrate high precision in tracking the dynamic states of power and gas systems. Two indexes are introduced for a numerical analysis of the tracking results, indicating that the accuracy of this method surpasses that of traditional measurements.