Forced oscillation detection amid communication uncertainties

This article proposes a novel technique for the detection of forced oscillation (FO) in a power system with the uncertainty in the measured signals. The impacts of communication uncertainties on measured signals are theoretically investigated based on the mathematical models developed in this article. A data recovery method is proposed and applied to reconstruct the signal under the effects of communication losses. The proposed FO detection with communication uncertainties is evaluated in the modified 14-machine Southeast Australian power system. A rigorous comparative analysis is made to validate the effectiveness of the proposed data recovery and FO detection methods

Particle filter approach to dynamic state estimation of generators in power systems

Emami, K ORCiD: 0000-0001-5614-4861This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions. © 1969-2012 IEEE

Forced Oscillation Detection and Damping in Future Power Grids with High Penetration of Renewables

Forced oscillation (FO) has recently been detected in actual power systems, i.e. Nordic and Western America power systems. These major events eventually result in the widespread blackout in the power system. Therefore, intensive research in the FO detection is sought. Numerous techniques have been successfully applied for the FO detection. Nevertheless, previous FO detection methods did not consider the impact of communication channels. To fill this gap, this work proposes a method to detect the FO taken into account impacts communication channels, which cooperates with artificial intelligent (AI) methods of ranking sources of the FO. The signal restoration technique will be applied to restore the quality of data so that the proposed technique can ensure small-signal and transient stabilities in large-scale power system. Previously, a small number of works focused on damping out the FO mode. The system may experience instability without proper FO detection and damping methods. For this reason, this work seeks a new technique for the FO detection and damping incorporating with AI approach in uncertain power systems with high penetrations of renewables, i.e. wind and solar generators. In this regard, impacts of uncertainties from renewables on the FO detection and damping will be analyzed. The power oscillation damper (POD) will be designed to simultaneously improve the damping of the FO mode and the inter-area mode. An adaptive control technique will be applied to enhance the FO mode along with moving window time without the installation of additional PODs. Besides, the event-triggered control strategy will be used to activate the functions of the new POD appropriately. By addressing the fundamental limitations in the FO detection and appropriate control methods, the definite recommendation will be made for the robust operation of the smart power grid with high penetration of renewables and various uncertainties.</p

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

Emami, K ORCiD: 0000-0001-5614-4861In 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

Effects of non-stationary forced oscillation on electromechanical modes

The forced oscillation (FO) containing variable frequencies, known as non-stationary FO, has yet been well understood in power system engineers. This paper analyzes the effects of the non-stationary FO on electromechanical modes (EMs) in the range of 0.2 to 2.0 Hz. The non-stationary forced disturbance (FD) with such variable frequency bands is mathematically modeled. The injection of the non-stationary FD results in the non-stationary FO. A modified subspace-based state-space identification (so-called 4SID) method is applied to estimate the non-stationary frequencies and damping ratios of the EMs along with a moving window. Effects of the non-stationary FO on the EMs are compared to those of the conventional FO. Simulation results are verified in the future 14-machine Southeast Australian power system with converter controlled-based generations under various FO conditions. © 2021 IEE

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

Power system dynamic state estimation using particle filter

A particle filter based power system dynamic state estimation scheme is presented in this paper. The proposed method can be considered as an alternative to the other schemes which are mostly based on the Kaiman Filter. The particle filter approach can be used to estimate the states of nonlinear systems which are subjected to both Gaussian and non-Gaussian noise. Furthermore, the presented scheme has a simple algorithm that can be easily implemented numerically. The case study considered in this paper reveals that the method has considerable accuracy and provides smooth dynamic state estimation even when the noise variance differs from a known initial value. © 2014 IEEE

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