Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks

Roshany-Yamchi, Samira; Cychowski, Marcin; Negenborn, Rudy R.; Schutter, Bart De; Delaney, Kieran; Connell, Joe

doi:10.1109/tcst.2011.2172444

Cited by 119 publications

(67 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, equation (6) does not allow for a recursive distributed update, i.e., where only local computations are performed and where communication is required only among neighboring diagnosers. In the following, we define an upper bound B i (k) to the local estimation error covariance Π i (k) that can be computed in a distributed way and that can be used for the computation of the local thresholds.…”

Section: Fault Detection Problem Formulationmentioning

confidence: 99%

“…More specifically, by using a partition-based estimation technique and exchanging information with the diagnosers of neighboring This work has been conducted as part of the research project Stability and Control of Power Networks with Energy Storage (STABLE-NET) which is funded by the RCUK Energy Programme (contract no: EP/L014343/1). Recently, several different partition-based approaches have been proposed: for example, [4], [5], [6] propose Kalmanfilter-based estimation schemes for discrete-time systems affected by stochastic noise, while [7], [8] assume that the system is affected by bounded noise. In this paper, we consider linear discrete-time systems affected by stochastic noises.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scalable monitoring of interconnected stochastic systems

Boem

Carli

Farina

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

Abstract-In this paper, we propose a novel distributed fault detection method to monitor the state of a linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger estimator, based on the local model of the subsystems and that takes into account the dynamic coupling terms between the subsystems. The proposed methodology computes -in a distributed way -a bound on the variance of a properly defined residual signal, considering the uncertainty related to the state estimates performed by the neighboring subsystems. This bound allows the computation of suitable local thresholds with guaranteed maximum false-alarms rate. The implementation of the proposed estimation and fault detection method is scalable, allowing Plug & Play operations and the possibility to disconnect the faulty subsystem after fault detection. Theoretical conditions guaranteeing the convergence of the estimates and of the bounds are provided. Simulation results show the effectiveness of the proposed method.

show abstract

Section: Fault Detection Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable monitoring of interconnected stochastic systems

Boem

Carli

Farina

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

show abstract

“…To extend KF to the nonlinear system with Gaussian noise, modified KFs such as extended KF (EKF) and unscented KF (UKF), have been proposed [7][8][9]. However, for the system with high nonlinearity, the poor state estimation results will be obtained [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are some serious problems encountered in the general PF [8,9]. One of them is particle impoverishment.…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

Havangi

2018

Automatika

View full text Add to dashboard Cite

To cite this article: Ramazan Havangi (2018) An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems, Automatika, 59:1, 94-103, DOI: 10.1080/00051144.2018 ABSTRACTParticle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method. ARTICLE HISTORY

show abstract

“…Correos electrónicos: ramongr@us.es (Ramón A. García), rubio@us.es (Francisco R. Rubio), dorihuela@uloyola.es (Luis Orihuela), pmillan@uloyola.es (Pablo Millán), mortega@us.es (Manuel G. Ortega) trabajos basados en versiones distribuidas del Filtro de Kalman (Roshany-Yamchi et al, 2013), (Feng et al, 2013), (Mahmoud and Khalid, 2013), incluso en ocasiones combinado con otras técnicas, (He et al, 2016), (Wu et al, 2016). Otros enfoques posibles son los trabajos basados en adaptaciones del observador de Luenberger donde se usan técnicas de consenso (Millán et al, 2012b), teoría H ∞ (Orihuela et al, 2013), (Zhang et al, 2016), o con horizonte deslizante (Farina et al, 2010), (Li et al, 2014), entre otros.…”

Section: Introductionunclassified

Observadores Distribuidos Garantistas para Sistemas en Red

García

Rubio

Orihuela

et al. 2017

Revista Iberoamericana de Automática e Informática Industrial R

View full text Add to dashboard Cite

ResumenEn este artículo se propone un observador distribuido garantista para sistemas en red, considerando de forma explícita el problema de los retardos variables en las comunicaciones. Se asume que la información intercambiada entre agentes llega siempre a su destino, si bien las comunicaciones están sujetas a retardos variables, cuyo valor máximo se supone conocido. Cada observador trabaja con información parcial, y necesita comunicarse con observadores vecinos para llevar a cabo una estimación del estado completo del sistema. Para representar a los conjuntos garantistas, cuya función es acotar en tiempo real la región en la que se encuentra el estado del sistema, se ha optado por la utilización de zonotopos. Esto permite integrar de forma sencilla la información recibida por cada agente. Finalmente se presentan resultados de simulación para validar el algoritmo propuesto. Palabras Clave:Estimación Distribuida, Observadores de Estado, Sistemas con Retardos, Sistemas en red, Zonotopos. IntroducciónEn control automático existen muchas técnicas que se basan en la descripción interna del sistema a controlar, es decir, en el conocimiento del estado del sistema. Es bien conocido que en la práctica en la mayoría de los casos no se dispone de dicha información, por lo que es necesario recurrir a estimadores de estados de la planta o proceso. Por otro lado en plantas de gran tamaño, que cuentan con un gran número de variables de estado, la información está típicamente distribuida.En ese sentido, el término estimación distribuida hace referencia a dos conceptos. El primero, la estimación del estado de la planta no se lleva a cabo por unúnico observador, sino que se realiza por un conjunto de observadores denominados agentes. El segundo es que, además, dichos agentes colaboran para poder cumplir su objetivo, transmitiendo de forma periódica información a través de una red de comunicación, (Rubio et al., 2014). También hay enfoques que consideran comunicaciones asíncronas, como en (Weimer et al., 2012), (Guinaldo et al., 2016) y (García et al., 2015, donde los agentes determinan en función a ciertos eventos si comunican su información o no.Es posible encontrar en la literatura un gran número de publicaciones sobre este tema. Por ejemplo, existen multitud de * Autor en correspondencia.Correos electrónicos: ramongr@us.es (Ramón A. García), rubio@us.es (Francisco R. Rubio), dorihuela@uloyola.es (Luis Orihuela), pmillan@uloyola.es (Pablo Millán), mortega@us.es (Manuel G. Ortega) trabajos basados en versiones distribuidas del Filtro de Kalman (Roshany-Yamchi et al., 2013), (Feng et al., 2013), (Mahmoud and Khalid, 2013), incluso en ocasiones combinado con otras técnicas, (He et al., 2016), (Wu et al., 2016). Otros enfoques posibles son los trabajos basados en adaptaciones del observador de Luenberger donde se usan técnicas de consenso (Millán et al., 2012b), teoría H ∞ (Orihuela et al., 2013), (Zhang et al., 2016), o con horizonte deslizante (Farina et al., 2010), (Li et al., 2014), entre otros.Por su parte el término gar...

show abstract

Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks

Cited by 119 publications

References 30 publications

Scalable monitoring of interconnected stochastic systems

Scalable monitoring of interconnected stochastic systems

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

Observadores Distribuidos Garantistas para Sistemas en Red

Contact Info

Product

Resources

About