Application of long short-term memory neural networks in dynamic state estimation of generators subjected to ageing in complex power systems

© 2019 IEEE. In this paper, Long short-term memory(LSTM) neural networks based techniques for estimating dynamic states of generators in highly complex power systems is presented. It is proven that time-series prediction techniques can be used for dynamic state estimation. The most benefit that proposed method offers, is its independency from the mathematical model of the generators. The results proves superiority of the proposed technique over particle filter and unscented Kalman filter when parameters of the generators alter. The proposed scheme sustain its accuracy and precision even in the presence of unobservable variances in generator parameters. Parameter alterations in generators usually happen due to ageing of the equipment and environment impacts, and so on

A functional observer based dynamic state estimation technique for grid connected solid oxide fuel cells

IEEE This paper presents a functional observer based technique for estimating gaseous partial pressures in Triple Phase Boundary of a high-order Solid Oxide Fuel Cell. Triple Phase Boundary is a nano-scale region in Solid Oxide Fuel Cells where direct measurement of partial pressure of individual gases is not possible. For a reliable and a safe operation those quantities must be monitored. This paper reports a novel functional observer based dynamic state estimation approach that utilizes a system decomposition algorithm to provide a functional observer with minimum order. Therefore, the proposed technique has a simpler structure than conventional state observer based schemes. Case studies of the proposed technique, implemented on a complex nonlinear power system, show accurate and smooth estimations in comparison to full-order state observer based techniques in terms of tracking of nonlinear partial pressures

A novel neural network approach to dynamic state estimation of generators subjected to ageing in complex power systems

In this paper, a neural network based technique for estimating dynamic states of generators in highly complex power systems is presented. The proposed method is independent to the mathematical model of the generators and uses a nonlinear autoregressive neural network with exogenous inputs to estimate dynamic states of the generators. The proposed technique has been compared to particle filter and unscented Kalman filter based schemes previously reported in the literature. The simulation results show superiority of the proposed technique over the two other schemes when parameters of the generators alter. Parameter alterations in generators are practically occur due to environment impacts, ageing of the equipment and so on. The proposed scheme is capable of keeping its accuracy and precision even in the presence of unobservable variances in generator parameters. © 2019 IEEE

Multiple fault location in three-terminal transmission lines

This paper presents a novel method for locating two faults, instead of a single fault, in three-terminal transmission lines. It is assumed two faults occur in two different sections of a three-terminal transmission line and the faults may occur simultaneously or one after another before the first fault is cleared. It is also assumed types of faults are unknown and no measurement equipment is available at the junction point where three lines are joined and connected to each other. An objective function is developed and the Nelder-Mead method and multi start points techniques are employed to minimise the objective function. The proposed method is independent of prefault current, type of fault and prefault conditions. Numerical results for a large number fault locations and different scenarios are reported