Distributed Widely Linear Kalman Filtering for Frequency Estimation in Power Networks

Kanna, Sithan; Dini, Dahir H.; Xia, Yili; Hui, S. Y. Ron; Mandic, Danilo P.

doi:10.1109/tsipn.2015.2442834

Cited by 46 publications

(14 citation statements)

References 48 publications

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“…The power grid is usually designed to operate optimally under balanced conditions; however, faults in the power system can cause imbalanced operating conditions that propagate through the network, threatening its stability. Therefore, it is important in fault detection and mitigation applications to identify the incidences when the power grid is operating in an unbalanced fashion [56].…”

Section: Qaut For Imbalance Detection In Three-phase Power Systemsmentioning

confidence: 99%

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

et al. 2018

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Section: Qaut For Imbalance Detection In Three-phase Power Systemsmentioning

confidence: 99%

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

et al. 2018

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“…The typical approach for DSE is to separate magnitude and phasor angle in the state vector, whereas complex-valued estimators consider the state as a vector of complex values. ACKF has been proposed in other applications such as frequency estimation in power networks [14].…”

Section: Introductionmentioning

confidence: 99%

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Louis

Ledwich

Walker

et al. 2020

Journal of Modern Power Systems and Clean Energy

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Distribution state estimation (DSE) is an essential part of an active distribution network with high level of distributed energy resources. The challenges of accurate DSE with limited measurement data is a well-known problem. In practice, the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance. This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter (ACKF). The Kalman filter method inherently provides state error covariance prediction. It can be utilized to accurately infer the error covariance of other parameters and provide a method to determine optimal measurement locations based on the sensitivity of error covariance to measurement noise covariance. This paper also proposes a generalized formulation of ACKF to allow scalar measurements to be incorporated into the complex-valued estimator. The proposed method is simulated by using modified IEEE 34-bus and IEEE 123-bus test feeders, and randomly generates the load data of complex-valued Wiener process. The ACKF method is compared with an equivalent formulation using the traditional weighted least squares (WLS) method and iterated extended Kalman filter (IEKF) method, which shows improved accuracy and computation performance.

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“…It only relies on the local data exchange between interconnected nodes, and therefore removes the requirement of a powerful central processor and, as such, reduces communications bandwidth of the traditional centralized estimation whilst retaining similar estimation performance [2], [3]. Distributed estimation has been applied to target localization [4], clustering [5], frequency estimation [6] and spectrum estimation in Cognitive radio (CR) [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

Zhao

Lamare

et al. 2019

IEEE Trans. Signal Process.

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This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

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Distributed Widely Linear Kalman Filtering for Frequency Estimation in Power Networks

Cited by 46 publications

References 48 publications

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

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