Proactive Failure Management in Smart Grids for Improved Resilience: A Methodology for Failure Prediction and Mitigation

Kaitovic, Igor; Luković, Slobodan; Malek, Miroslaw

doi:10.1109/glocomw.2015.7414155

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…λ i is a parameter. Thus, Equation (5) represents the error values between four matrices and prediction matrices. We use the gradient descent to obtain the optimal solutions of Ω i and Θ i through iteration until convergence with random initialization [29].…”

Section: Matrix Decompositionmentioning

confidence: 99%

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Online Fault Prediction Based on Collaborative Filtering in Smart Grid

Wang

Liang

Xing

et al. 2023

Mathematical Problems in Engineering

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Smart grid, responsible for upgrading traditional power networks by integrating with cutting-edge information and communication networks, forms coupled networks but also pose potential hazards in the face of fault cascade. In coupled networks, fault prediction is of significance because tight interaction between power nodes and communication nodes makes the smart grid more vulnerable. Unfortunately, most existing works of fault prediction are specific to a single network and do not consider the correlation of coupled elements. To address these limitations, in this paper, we highlight the interdependence of networks and define fault correlation. Further, we propose a probabilistic prediction model using collaborative filtering in machine learning. We finally present an online prediction algorithm. We conduct experiments to illustrate the effectiveness of our prediction algorithm with different parameters and give some observations that may give more insight into interdependent networks.

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Section: Matrix Decompositionmentioning

confidence: 99%

“…Therefore, fault prediction of networks is of great importance between two networks in the smart grid. In comparison with a single network, fault prediction becomes challenging work in coupled networks for the academic community [5].…”

Section: Introductionmentioning

confidence: 99%

Online Fault Prediction Based on Collaborative Filtering in Smart Grid

Wang

Liang

Xing

et al. 2023

Mathematical Problems in Engineering

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“…Elevating substations, considering floods, is another possible construction approach to avoid damage in electrical grids. Also, the risk management of an EPG will help to understand what can be changed or improved in order to decrease the faults and susceptibilities of the system [73,90,91].…”

Section: Prevention and Managementmentioning

confidence: 99%

A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience

2019

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One of the most critical infrastructures in the world is electrical power grids (EPGs). New threats affecting EPGs, and their different consequences, are analyzed in this survey along with different approaches that can be taken to prevent or minimize those consequences, thus improving EPG resilience. The necessity for electrical power systems to become resilient to such events is becoming compelling; indeed, it is important to understand the origins and consequences of faults. This survey provides an analysis of different types of faults and their respective causes, showing which ones are more reported in the literature. As a result of the analysis performed, it was possible to identify four clusters concerning mitigation approaches, as well as to correlate them with the four different states of the electrical power system resilience curve.

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“…This may involve running the optimization in the control centers with frequent historic scenarios of failures. Devices such as phase measurement units (PMUs) can make data accessible to control centers in real-time [21], [22] so that the mitigation actions are immediately triggered. PMUs may provide data with the rate of up to 60 [23] or even 120 samples 8 per seconds in some industrial solutions.…”

Section: E Computational Aspectsmentioning

confidence: 99%

Self-Repairable Smart Grids Via Online Coordination of Smart Transformers

Pournaras

Espejo-Uribe

2017

IEEE Trans. Ind. Inf.

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The introduction of active devices in Smart Grids, such as smart transformers, powered by intelligent software and networking capabilities, brings paramount opportunities for online automated control and regulation. However, online mitigation of disruptive events such as cascading failures, is challenging. Local intelligence by itself cannot tackle such complex collective phenomena with domino effects. Collective intelligence coordinating rapid mitigation actions is required. This paper introduces analytical results from which two optimization strategies for self-repairable Smart Grids are derived. These strategies build a coordination mechanism for smart transformers that runs in three healing modes and performs collective decisionmaking of the phase angles in the lines of a transmission system to improve reliability under disruptive events, i.e. line failures causing cascading failures. Experimental evaluation using selfrepairability envelopes in different case networks, AC power flows and varying number of smart transformers confirms that the higher the number of smart transformers participating in the coordination, the higher the reliability and the capability of a network to self-repair.

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Proactive Failure Management in Smart Grids for Improved Resilience: A Methodology for Failure Prediction and Mitigation

Cited by 11 publications

References 29 publications

Online Fault Prediction Based on Collaborative Filtering in Smart Grid

Online Fault Prediction Based on Collaborative Filtering in Smart Grid

A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience

Self-Repairable Smart Grids Via Online Coordination of Smart Transformers

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