Situational Awareness Using Edge-Computing Enabled Internet of Things for Smart Grids

Hasnat, Abul; Hossain, Jakir; Adeniran, Adetola; Rahnamay-Naeini, Mahshid; Khamfroush, Hana

doi:10.1109/gcwkshps45667.2019.9024632

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Examples of techniques that propose state estimation over regions in power systems include [3,[26][27][28][29][30]. In these examples, regions have been defined according to a number of criteria, including geographic distance, operational similarity, and communication resources [26].The work in [9], for instance, addresses an early stress detection and locating method based on a linear predictive filter that may be used over an edge computing platform for regions determined based on geographical distances. The researchers in [29] offer a multi-area distributed state estimation solution that incorporates edge computing and uses local estimates that are calculated using the belief propagation algorithm over spatially defined areas.…”

Section: Related Workmentioning

confidence: 99%

“…It is demonstrated that multi-area distributed state estimations can speed up the system's reaction time, especially in response to urgent situations such as failures or cyber stressors [3]. With the help of local and distributed data processing in power grids, new technologies, such as edge or fog computing, can enable these functions [9]. Specifically, in this paper, interpretable and generalizable data-driven approaches for state estimation based on Kalman filters in multi-area settings are studied.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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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“…Moreover, the transmission cost can be reduced if a data reduction mechanism is deployed on the device itself. The literature shows that contextual information learning could be paramount for improving the performance of IoT systems [124], [125]. In IIoT, an approach proposed in [126] is an approach that learns context based on energy, backlog and conflict of participating nodes.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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“…AI techniques including machine learning and reinforcement learning at the edge promise to maximize system resiliency [84]. Training data can be used to predict and detect islanding, cyber-attacks, faults and general instability in the MG power system.…”

Section: B Microgrid System Resiliencementioning

confidence: 99%

Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for Microgrids

et al. 2021

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Future generation power grids will require the introduction and deployment of distributed energy resources to meet modern day load requirements. Consequently, we expect to see a rise in microgrids (MGs) existing as part of the main grid (grid-connected) or independent (islanded). Contained in these microgrids are a combination of energy resources such as solar, wind and fossil-fuels coupled with storage devices, electric vehicles and smart devices supporting prosumer operation. However, the addition of renewable energy resources would mean fluctuations in energy supply which would cause power system instability if not managed effectively. Hence, to maximize the management flexibility of MGs, the concept of microgrid software-definition is introduced. A concept that can be looked at as giving the microgrid an operating system to improve operation response and event detection by maintaining a global view of the network. This paper therefore critically analyses what this entails by presenting an architecture for Software-Defined Microgrids and discussing the management opportunities that softwarization of the MG introduces. We also highlight the design requirements and associated challenges in implementing and deploying SDMGs.INDEX TERMS Energy balancing, grid resiliency, microgrids, power grid management, software-defined networking, smart grids.

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Situational Awareness Using Edge-Computing Enabled Internet of Things for Smart Grids

Cited by 10 publications

References 22 publications

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

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

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for Microgrids

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