5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids

Cosovic, Mirsad; Tsitsimelis, Achilleas; Vukobratović, Dejan; Matamoros, Javier; Antón-Haro, Carles

doi:10.1109/mcom.2017.1700155

Cited by 109 publications

(67 citation statements)

References 13 publications

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“…GN-BP can be highly parallelized and flexibly distributed in the context of multi-area SE. In our ongoing work, we are investigating GN-BP in asynchronous, dynamic and real-time SE with online bad data detection, supported by future 5G communication infrastructure [32]. The matrix C = diag C 1 , .…”

Section: Discussionmentioning

confidence: 99%

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Distributed Gauss–Newton Method for State Estimation Using Belief Propagation

Cosovic¹,

Vukobratović

2019

IEEE Trans. Power Syst.

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We present a novel distributed Gauss-Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP). The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss-Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation.Contributions: In this paper, we present a novel distributed BP-based Gauss-Newton algorithm, where the BP is applied sequentially over the non-linear model, akin to what is done by the Gauss-Newton method. The resulting Gauss-Newton BP (GN-BP) algorithm represents a BP counterpart of the Gauss-Newton method and introduces a number of advantages over the current state-of-the-art in non-linear SE:

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Section: Discussionmentioning

confidence: 99%

“…The output of the inner iteration loop is the estimate of the state variable increments. Finally, the outer loop updates the set of state variables (lines [30][31][32]. The outer loop iterations are repeated until the stopping criteria is met.…”

mentioning

confidence: 99%

Distributed Gauss–Newton Method for State Estimation Using Belief Propagation

Cosovic¹,

Vukobratović

2019

IEEE Trans. Power Syst.

Self Cite

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“…The suitable candidates include incumbent mobile communication networks (MCN) and low power wide area networks (LP-WANs) [7]. Particularly, MCN covers most densely crowd areas including business regions, and urban residential regions [27], [28]. Therefore, IoE nodes can access the backbone network via the diverse MCN communication technolgoies from 2G (GPRS), 3G, 4G (LTE), 5G, and even 5G-Beyond [29].…”

Section: B Three Expectations Of Ioementioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

172

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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“…A state estimation scheme was suggested that tried to take advantages of both cloud and fog computing paradigm [17]. A distributed state estimation method was presented that is placed with 5G mobile cellular networks [6]. The three approaches addressed a particular WAMC application at the edge of the network and reduced the communication burden compared with centralized application.…”

Section: B Fog Computingmentioning

confidence: 99%

“…5,6,7,8 provide examples of anomaly detection for each type of faults respectively, where we can see the detail of detection result. Both the SSA and the KNN successfully identified the fault.…”

mentioning

confidence: 99%

A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

Yang

Chen

et al. 2018

2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC)

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Based on phasor measurement units (PMUs), a synchronphasor system is widely recognized as a promising smart grid measurement system. It is able to provide high-frequency, high-accuracy phasor measurements sampling for Wide Area Monitoring and Control (WAMC) applications. However, the high sampling frequency of measurement data under strict latency constraints introduces new challenges for real time communication. It would be very helpful if the collected data can be prioritized according to its importance such that the existing quality of service (QoS) mechanisms in the communication networks can be leveraged. To achieve this goal, certain anomaly detection functions should be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud computing hierarchical architecture, which allows computing tasks to be conducted at the network edge, a novel PMU fog is proposed in this paper. Two anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus system. The simulation experiments based on Riverbed Modeler demonstrate that the proposed PMU fog can effectively reduce the data flow end-toend (ETE) delay without sacrificing data completeness.

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5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids

Cited by 109 publications

References 13 publications

Distributed Gauss–Newton Method for State Estimation Using Belief Propagation

Distributed Gauss–Newton Method for State Estimation Using Belief Propagation

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

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