Distributed state estimation with lossy measurement compression in smart grid

Ma, Hang; Yang, Yunjuan; Wang, Qi; Chen, Yan; Liu, K. J. Ray

doi:10.1109/globalsip.2013.6736929

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…For an arbitrary number of the system blocks, in [27,25,75,93,71,4,48,45,56,89,91], approximate iterative solutions of this problem are considered. They are mainly based on implementations of the block coordinate descent method (BCDM) and its modifications [84,10].…”

Section: Differences From Known Techniquesmentioning

confidence: 99%

Optimal modeling of nonlinear systems Method of variable injections

Torokhti,

Soto-Quiros

2024

Proyecciones (Antofagasta)

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Our work addresses a development and justification of the new approach to the modeling of nonlinear systems. Let $\f$ be an unknown input-output map of the system with a random input and output $\y$ and $\x$, respectively. It is assumed that $\y$ and $\x$ are available and covariance matrices formed from $\y$ and $\x$ are known. We determine a model of $\f$ so that an associated error is minimized. To this end, the model $\ttt_p$ is constructed as a sum of $p+1$ particular parts, in the form $\ttt_p (\y) = \sum_{j=0}^{p}G_j H_j Q_j(\vv_j)$ where $G_j$ and $ H_j$, for $j=0,\ldots, p$, are matrices to be determined, and $\vv_j$, for $j=1,\ldots,p$, is a special random vector called the injection. We denote $\vv_0=\y$. Injections $\vv_1,\ldots, \vv_p$ are aimed to diminish the error associated with the proposed model $\ttt_p$. Further, $Q_j$ is a special transform aimed to facilitate the numerical realization of model $\ttt_p$. It is determined in the way allowing us to optimally determine $G_j$ and $ H_j$ as a solution of $p+1$ separate error minimization problems which are simpler than the original minimization problem. The empirical determination of injections $\vv_1,\ldots, \vv_p$ is considered. The proposed method has several degrees of freedom to diminish the associated error. They are `degree' $p$ of $\ttt_p$, choice of matrices $G_0, H_0, \ldots,$ $G_p, H_p$, dimensions of matrices $G_0, H_0, \ldots,$ $G_p, H_p$ and injections $\vv_1,\ldots, \vv_p$, respectively. Four numerical examples are provided. At the end, the open problem is formulated.

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Section: Differences From Known Techniquesmentioning

confidence: 99%

Optimal modeling of nonlinear systems Method of variable injections

Torokhti,

Soto-Quiros

2024

Proyecciones (Antofagasta)

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“…Lemma 1: Suppose the eigenvalue decomposition of is , define , then if has the -restricted isometry constant where , for any satisfying , we have (27) Proof: For any satisfying the conditions in Definition 1, we have By applying the restricted isometry property (RIP), it can be further re-written as (28) To evaluate the estimator calculated by the proposed algorithm, we first define the functions (29) and (30) where , and . According to (4), is the MSE achieved by the estimator .…”

Section: Performance Analysis Under Additional Conditionsmentioning

confidence: 99%

“…Moreover, due to the block-wise and multi-rate [24] nature of most wireless sensor networks, reducing the dimension of the data is useful in matching the data from multiple sensors and/or clusters. Besides the wireless sensor network, other researches on compressing the raw data before estimation lie in different fields including image processing [25], power grid [26], [27] and robotic networks [28].…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation With Dimension Reduction Preprocessing

Yang

Chen

et al. 2014

IEEE Trans. Signal Process.

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Abstract-System state estimation relies heavily on the measurements. With the advance of sensing technology, the ability to measure is no longer a bottleneck in many systems, and more and more researchers now focus on the rich-information setting, i.e., big data. However, although information never hurts, it does not help unconditionally. How to make the most of it depends on whether we can process the data efficiently. In some systems, the inherent constraint such as the bandwidth and cost makes it necessary to reduce the dimension of the measurement before further processing. The problem that the raw measurements are first preprocessed to reduce size and then used for estimation is addressed in this paper. It is shown that there is a lower bound on the size of the preprocessed data such that if the size is beyond the bound, there exists a closedform estimator design that the linear minimum mean-square estimation can be obtained. Moreover, we propose an algorithm that is guaranteed to converge to a stationary point to design an estimator in the conditions that the lower bound cannot be reached. Besides convergence, the proposed algorithm guarantees bounded performance loss compared with the global optimal solution under some additional conditions. Finally, simulation results in three different applications are shown to demonstrate the effectiveness of the proposed algorithm.

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“…Distributed and local data processing strategies are promising and can enhance system monitoring capabilities by enabling low-latency requirements and avoiding the enormous overhead of transmitting a large volume of time-sensitive data to central processing units. Examples of distributed state estimation studies include [3,7,8], where the state estimation has been implemented using various machine learning-based frameworks. This paper studies multiple distributed data-driven state estimation techniques under various scenarios of information sharing among distributed areas.…”

Section: Introductionmentioning

confidence: 99%

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Hossain

Naeini

2022

Energies

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Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

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Distributed state estimation with lossy measurement compression in smart grid

Cited by 3 publications

References 11 publications

Optimal modeling of nonlinear systems Method of variable injections

Optimal modeling of nonlinear systems Method of variable injections

Distributed State Estimation With Dimension Reduction Preprocessing

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

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