Abstract
To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper. Firstly, the dynamic state estimation model is built. Since the gas pipeline transient flow equations are less than the states, the boundary conditions are used as supplementary constraints to predict the transient states. To increase the measurement redundancy, the zero mass flow rate constraints at the sink nodes are taken as virtual measurements. Secondly, to ensure the stability under bad data condition, the robust Kalman filter algorithm is proposed by introducing a time-varying scalar matrix to regulate the measurement error variances correctly according to the innovation vector at every time step. At last, the proposed method is applied to a 30-node gas pipeline network in several kinds of measurement conditions. The simulation shows that the proposed robust dynamic state estimation can decrease the effects of bad data and achieve better estimating results.
Highlights
In comparison with the traditional coal-fired power units, gas-fired electric generators can respond to the power load fluctuation rapidly, enhancing the operating flexibility of electrical energy systems [1,2,3]. is may help to improve security of power system with large-scale renewable energy.e random change of the natural gas consumptions due to the uncertainties of renewable energies makes it essential for obtaining the accurate dynamic states just like the pressures and mass flow rates of the natural gas pipeline networks to ensure the security and optimal operation of the integrated energy system containing electric powers and natural gases [4,5,6,7]
Some research works about state estimations for natural gas pipeline networks have appeared [11,12,13,14,15]. ese works are based on the nonlinear partial differential equation (PDE) describing the characteristics of transient gas flow [16, 17]
In [12], the extended Kalman filter (EKF) is Mathematical Problems in Engineering chosen to design an efficient observer for natural gas transmission system, and an algorithm is proposed to handle the discontinuities that appear in the dynamic model of a gas transmission networks
Summary
In comparison with the traditional coal-fired power units, gas-fired electric generators can respond to the power load fluctuation rapidly, enhancing the operating flexibility of electrical energy systems [1,2,3]. is may help to improve security of power system with large-scale renewable energy. E existing state estimation methods based on Kalman filter can reduce the random errors to some extent but are vulnerable to bad data To solve this problem, a variety of improved Kalman filter algorithms are proposed. E existing gas pipeline network state estimation methods based on Kalman filter solve the transient flow equations by using a numerical approximation technique called finite element methods. To cope with the above problems, and obtain accurate transient states of large-scale gas pipeline networks under practical operating conditions, this paper focuses on the robust dynamic state estimation method along with the following contributions:. (3) e proposed dynamic state estimation method is tested on a 30-node natural gas pipeline network under the normal measurement condition, bad data condition, and non-zero mean noises condition.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
