Survey on knowledge based methods to assist fault restoration in power  distribution networks

Oualmakran, Youssef; Meléndez, Joaquím; Herraiz, S.; Lopez-Perea, Mercedes; González, Eloy

doi:10.24084/repqj09.617

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Increased levels of automation, distributed intelligence, and on-line data mining and management are required to deliver the network control functions, reducing reconfiguration and the restoration times. Reconfiguration of smart grids addresses new challenges during normal operation and also for restoration and management of crisis situations [10, 19]. The connection of end-users (prosumers) to the energy market will facilitate the installation and connection of devices that offer grid services that will help mitigate capital and operational costs of the grid modernization required for energy transition and minimize environmental impact, thus ensuring lower electricity prices for everything involved.…”

Section: The Smart Grid As An Iotmentioning

confidence: 99%

High Performance Web of Things Architecture for the Smart Grid Domain

Vernet

Zaballos

Pozuelo

et al. 2015

International Journal of Distributed Sensor Networks

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The increasing complexity in the management of Smart Grids is an essential factor in the creation of new technological infrastructures capable of managing the different devices involved in the network. This network has been converted into an example of the Internet of Things. In this regard, the Web of Things enables an improvement in the processing of these data. Besides, the large amount of data in the Smart Grid domain means that a high performance architectural design is able to manage concurrently the entire information processing ability. This paper presents an initial approach for a new architecture and the first results after the system implementation.

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Section: The Smart Grid As An Iotmentioning

confidence: 99%

High Performance Web of Things Architecture for the Smart Grid Domain

Vernet

Zaballos

Pozuelo

et al. 2015

International Journal of Distributed Sensor Networks

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“…Because of the complexity of the problem, some authors have developed methods based on meta-heuristic optimization techniques such as genetic algorithms [22][23][24]. Others have used techniques based on expert systems [25][26][27][28], graph-theoretic approaches [29,30], or other artificial intelligence schemes [31][32][33][34][35]. Most of these solutions view the system from a centralized aspect and try to find a restoration procedure accordingly.…”

Section: Power Grid and Natural Disastersmentioning

confidence: 99%

Optimal energy dispatch of the power distribution network during the course of a progressing wildfire

Ansari

Mohagheghi

2014

Int. Trans. Electr. Energ. Syst.

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Summary Wildfires are natural phenomena that play a crucial role in many forest and grassland ecosystems. Under favorable conditions, that is, extreme heat, availability of fuel, and high winds, they may spread beyond control and approach city limits. Power lines are vulnerable to wildfires in their vicinity, mainly because of increased conductor temperatures as a result of ambient temperature rising. This may lead to conductor annealing and/or increased sag. A violation of safety clearances of the lines could in turn lead to possible flashovers and onset of cascading failures. To address the situation, the operator may dynamically alter the ratings of the lines in the affected areas in order to reduce the flow of current through them, thereby assisting with conductor cooling. However, with reduced line capacities, the utility may not be able to supply all the loads from the main substation anymore, which may increase the risk of unserved load and load shedding. A solution is put forth in this paper that enables the system operator to dispatch distributed generators, demand responsive loads, and microgrids in order to supply loads under such emergency conditions. Because of the uncertain spread/severity of wildfires, the problem is stochastic in nature and is therefore modeled as two‐stage stochastic optimization. First‐stage variables determine the generation capacity to be purchased before the onset of the event, while second stage variables represent generation dispatch commands. The efficiency of the proposed solution is verified on a modified version of the Institute of Electrical and Electronics Engineers (IEEE) 123‐bus test system. Copyright © 2014 John Wiley & Sons, Ltd.

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“…• User Interface: Allows different clients to get to approvals in view of the engineering. In any case, the developed multifaceted nature in smart grid is a fundamental factor in the game plan of new innovative frameworks, which are well equipped for dealing with various gadgets used as a piece of the framework diminishing the execution of smart grid [14]. Other than the tremendous volume of data in the smart grid connected network, effectively handling the storage, processing and analysis of the data in question is a difficult job [15].…”

Section: Introductionmentioning

confidence: 99%

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

et al. 2020

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Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

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Survey on knowledge based methods to assist fault restoration in power distribution networks

Cited by 5 publications

References 23 publications

High Performance Web of Things Architecture for the Smart Grid Domain

High Performance Web of Things Architecture for the Smart Grid Domain

Optimal energy dispatch of the power distribution network during the course of a progressing wildfire

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

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