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 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

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

Load forecasting for diurnal management of community battery systems

This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage (LV) distribution network for voltage management, energy arbitrage or peak load reduction. The methods compared include: a neural network (NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network (WNN) model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system (ANFIS) approach. The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge (SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic (PV) field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer

Short-term electric load forecasting in microgrids: Issues and challenges

This paper compares performance of three well-known short-term load forecasting (STLF) methodologies in microgrid applications. The chosen methods include: I) seasonal auto-regressive integrated moving average with exogenous variables, ii) neural networks, and iii) wavelet neural networks. These methods utilise combinations of historical load data and metrological variables to predict the load of individual customers in a microgrid over the next day. This is essential for scheduling, management and control of microgrid resources. So far, the existing STLF methodologies have been successfully used for the aggregated load forecasting in transmission and distribution systems. Nevertheless, their prediction accuracy in microgrid applications, where diversity is low and considerable changes in the load of customers can be observed in a short period of time, is not investigated. The random and chaotic nature of individual customers' loads make STLF challenging; hence, this paper aims to address the issues for the above methodologies in microgrids. © 2018 IEEE

Dynamic state estimation based control strategy for DFIG wind turbine connected to complex power systems

This paper proposes a viable solution to the longlasting issue of using flux-involved control scheme to regulate the behavior of doubly fed induction generator (DFIG) during faults. Instead of trying to design a complicated method to measure flux, which cannot be directly measured with contemporary technology, the solution utilizes UKF-based dynamic state estimation of DFIG connected to a complex power system to estimate the wanted variables. The decentralized estimation scheme takes into consideration the overall power system network and uses only local noisy PMU measurement data. DFIG control schemes are also investigated to a fair extent where three control methods are discussed with comparison results presented. The improved control scheme displays a better fault recovery response and system compatibility. A number of considerations are taken into account in the design of DFIG control schemes, including reactive power supports and DC-link current compensation

Realization of state-estimation-based DFIG wind turbine control design in hybrid power systems using stochastic filtering approaches

© 2016 IEEE.This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable - stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design