Faults in smart grid systems: Monitoring, detection and classification

Rivas, Angel Esteban Labrador; Abrão, Taufik

doi:10.1016/j.epsr.2020.106602

Cited by 111 publications

(38 citation statements)

References 133 publications

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“…Because of its low latency and great dependability, 5G is a viable alternative to fixed connections. Special performance requirements, especially very low latency (less than 1ms), are common in SG communication [94]. A clearly superior DR infrastructure will emerge with further development of 5G solutions, which will raise the bar in terms of transmission, trustworthiness, safety, and interconnectivity [7].…”

Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

“…Improved sensing, communication, and control techniques are necessary with the introduction of smart grid technologies that accommodate DERs and EVs [96]. Cybersecurity breaches, cascade failures, blackouts, and other attacks on the electric grid can result in infrastructural breakdowns [94]. For example, information regarding the availability of residents may be gleaned from the use of heater or air-conditioning data in a residential home management system throughout the summer or winter seasons.…”

Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

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Overview of Demand-Response Services: A Review

2022

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It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance of fostering behavioral change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The internet of things (IoT) has been applied to ensure adaptive monitoring of energy consumption and cost-effective and adequate demand-side management (DSM). The article is based on the research of the most recent sources of DR implementation methods applied at the power distribution level. It explains the main concepts, classifications, and entities implementing DSM programs, and suggests new visions and prospects for DSM and DR. Moreover, it discusses the application of blockchain technology potential for the internet of energy.

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Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

Overview of Demand-Response Services: A Review

2022

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“…[60] This paper reviews machine learning and big data techniques for efficiently processing the massive data volume generated by Internet of Things (IoT) devices for reliable decision-making and ensuring the safety of both data and infrastructure against cyber attacks in a smart grid. 2020 [61] This paper presents failures in smart grid components (conventional energy systems, renewable resources, cables and transmission lines, power electronics and power transformers) and describes sensors, communication tools, including 5G, and monitoring infrastructure. Moreover, it discusses procedures associated with smart grids fault detection and location and provides an insight into lessons learned and future trends.…”

Section: Ref Contributionsmentioning

confidence: 99%

“…Some issues are still open and worth further investigations, such as the high-performance data processing technologies and analysis techniques for intelligent decision-making in large-scale complex multi-energy systems, lightweight machine learning-based solutions for fast classification, and so forth. It has been recently suggested that quality of service (QoS) application requirements could be met considering emerging new sensing technologies and embedded computing [61]. Moreover, in a smart grid context, where hybrid energy systems interact with other system architectures at different application levels, the use of advanced computing and communication technologies, e.g., edge computing, ubiquitous Internet of Things and 5G wireless networks, will obviously improve the monitoring of smart grid conditions.…”

Section: Critical Analysis and Future Research Topics And Challengesmentioning

confidence: 99%

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

et al. 2021

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Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.

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“…With the advancement in 5G development, meeting the low latency, high bandwidth and ultra-reliability demands of smart grid networks can be realized through key 5G technologies. Transitioning the core networks of the power grid from the current protocols such as Multi-Protocol Label Switching (MPLS) and 4G to 5G cellular technology will offer the performance requirements of smart grid control commands, alarms and teleprotection services of ≤ 1 ms, and user plane latency corresponding to 32 bytes of data [4]. The future standard releases of 5G will potentially achieve reliability of 10 Block Error Rate (BLER) and latency in the sub-ms range and satisfying a high capacity transport network at lower Operational Cost (OpEx) becomes realistic.…”

Section: Introductionmentioning

confidence: 99%

Challenges and Promises of 5G for Smart Grid Teleprotection Applications

Ghanem

Ugwuanyi

Asif

et al. 2021

2021 International Symposium on Networks, Computers and Communications (ISNCC)

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This work is based on some ongoing technical projects, research activities, and discussions for applying 5G technology in smart grid critical applications, specifically in teleprotection. The functional and performance requirements of 5G capabilities needed to meet the overall teleprotection essentials in the smart grid will be discussed. Recommendations that cover the main challenges and promises/prospects for 5G in teleprotection will also be presented.

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Faults in smart grid systems: Monitoring, detection and classification

Cited by 111 publications

References 133 publications

Overview of Demand-Response Services: A Review

Overview of Demand-Response Services: A Review

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Challenges and Promises of 5G for Smart Grid Teleprotection Applications

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