A Two-Level Static State Estimator for Electric Power Systems

Cutsem, Th. Van; Horward, J.L.; Ribbens-Pavella, M.

doi:10.1109/tpas.1981.317015

Cited by 132 publications

(12 citation statements)

References 4 publications

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“…For example, distributed state estimation methods have been considered for decades with the goal of reducing the computational burden at the central control center by distributing the tasks across the system. A simple but effective approach is decomposition-and-merge, which is essentially a two-level hierarchical method [93,94]. At the lower level, each local area independently runs its own estimator on the basis of its local measurements, whereas at the higher level, the central coordinator receives the state estimation results, with boundary measurements if necessary, from the individual areas and then combines them to obtain a system-wide solution.…”

Section: Decentralized and Cooperative Sensingmentioning

confidence: 99%

Smart grid sensor data collection, communication, and networking: a tutorial

Kayastha

Niyato

Hossain

et al. 2012

Wireless Communications

124

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The smart grid is an innovative energy network that will improve the conventional electrical grid network to be more reliable, cooperative, responsive, and economical. Within the context of the new capabilities, advanced data sensing, communication, and networking technology will play a significant role in shaping the future of the smart grid. The smart grid will require a flexible and efficient framework to ensure the collection of timely and accurate information from various locations in power grid to provide continuous and reliable operation. This article presents a tutorial on the sensor data collection, communications, and networking issues for the smart grid. First, the applications of data sensing in the smart grid are reviewed. Then, the requirements for data sensing and collection, the corresponding sensors and actuators, and the communication and networking architecture are discussed. The communication technologies and the data communication network architecture and protocols for the smart grid are described. Next, different emerging techniques for data sensing, communications, and sensor data networking are reviewed. The issues related to security of data sensing and communications in the smart grid are then discussed. To this end, the standardization activities and use cases related to data sensing and communications in the smart grid are summarized. Finally, several open issues and challenges are outlined. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Decentralized and Cooperative Sensingmentioning

confidence: 99%

Smart grid sensor data collection, communication, and networking: a tutorial

Kayastha

Niyato

Hossain

et al. 2012

Wireless Communications

124

View full text Add to dashboard Cite

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“…In 1981 also, Van Cutsem et al [11] developed a two-level estimator based on non-overlapping areas. Only internal measurements are used at the area level, while the coordination level is aimed at estimating tie-line power flows through vector u.…”

Section: Coordination At the State Estimation Levelmentioning

confidence: 99%

A taxonomy of multi-area state estimation methods

Gómez-Expósito

Jaén

Gómez-Quiles

et al. 2011

Electric Power Systems Research

171

103

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a b s t r a c tThis paper presents a critical review of the state of the art in multi-area state estimation (MASE) methods, which are currently gaining renewed interest due to their capability of properly tracking multi-TSO transactions and accommodating highly redundant information systems. Based on several classification criteria, a taxonomy of MASE methods is first proposed. Two main categories, namely two-step or hierarchical versus decentralized, are identified. Then, for each class of methods, the resulting model structure and area interactions are discussed and a brief presentation is made of a selected subset of references.

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“…One approach is to use the hierarchical method [2], [3] where the local measurements are first processed by the local estimators and then transmitted to a central coordinator for further estimation. Another approach is fully distributed where the information is exchanged between the substations without the central coordinator [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed state estimation with lossy measurement compression in smart grid

Yang

Wang

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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Abstract-State estimation in smart grid highly relies on the availability of measurements. Due to the interconnected nature of the power grid, the measurements at different substations are not totally independent and thus contain some redundancy. Among the various environments the power grid work in, there are certain circumstances the system communication capability is limited such that transmitting a lot of measurements within a small time interval is expensive and sometimes even impossible, and thus the measurements need to be compressed before transmitted to remove the redundancy. While it is possible to design lossless compression methods in some cases, in this paper, we focus on the problem of lossy compression of the measurements to adapt to more severe conditions. An algorithm is proposed to jointly design the pre-processors that compressing the measurements subject to the communication constraints and the subsequent estimator that using only the compressed measurements for state estimation. The effectiveness of the proposed algorithm is illustrated by numerical results.

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A Two-Level Static State Estimator for Electric Power Systems

Cited by 132 publications

References 4 publications

Smart grid sensor data collection, communication, and networking: a tutorial

Smart grid sensor data collection, communication, and networking: a tutorial

A taxonomy of multi-area state estimation methods

Distributed state estimation with lossy measurement compression in smart grid

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