scholarly journals Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 305
Author(s):  
Odin Foldvik Eikeland ◽  
Filippo Maria Bianchi ◽  
Inga Setså Holmstrand ◽  
Sigurd Bakkejord ◽  
Sergio Santos ◽  
...  

Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploited statistical and machine-learning techniques to predict the occurrence of failures. The classification models achieve good performance, meaning that there is a significant relationship between the collected variables and fault occurrence. Thus, we interpreted the variables that mostly explain the classification results to be the main driving factors of power interruption. Wind speed of gust and local industry activity are found to be the main controlling parameters in explaining the power failure occurrences. The result could provide important information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures.

2020 ◽  
Vol 10 (24) ◽  
pp. 8897
Author(s):  
Sergio Bustamante ◽  
Mario Manana ◽  
Alberto Arroyo ◽  
Alberto Laso ◽  
Raquel Martinez

Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.


Author(s):  
Georgy Ayzel ◽  
Georgy Ayzel

Arctic coastal systems are very sensitive to the freshwater budget mainly formed by river runoff. Great biases in estimation of total river runoff load to the Arctic Ocean proposed by the number of various scientific groups and insufficiency of physically-based, short-term, spatially diverse runoff predictions lead to strong necessity of state-of-art hydrological techniques implementation. At the moment the most powerful tools for the land hydrological cycle modeling are physically-based, conceptual or data-driven models. Better model – wider sources of hydrometeorological and landscape-related information we need to use to perform robust calculations. Severe climatic conditions of Arctic coastal region have led to weak river runoff monitoring net and a high level of uncertainties related to difficulties of direct measurements. There is the reason we need to develop modern techniques that allow providing effective runoff predictions by state-of-art models in the case of strong research data scarcity (for ungauged basins). Early stage of research aimed to coupling of conceptual hydrological model, cutting edge machine learning techniques and various sources of geographical data will be proposed with the call for intensification of cross-disciplinary research activities for the Arctic region sustainable development and safety.


Author(s):  
Georgy Ayzel ◽  
Georgy Ayzel

Arctic coastal systems are very sensitive to the freshwater budget mainly formed by river runoff. Great biases in estimation of total river runoff load to the Arctic Ocean proposed by the number of various scientific groups and insufficiency of physically-based, short-term, spatially diverse runoff predictions lead to strong necessity of state-of-art hydrological techniques implementation. At the moment the most powerful tools for the land hydrological cycle modeling are physically-based, conceptual or data-driven models. Better model – wider sources of hydrometeorological and landscape-related information we need to use to perform robust calculations. Severe climatic conditions of Arctic coastal region have led to weak river runoff monitoring net and a high level of uncertainties related to difficulties of direct measurements. There is the reason we need to develop modern techniques that allow providing effective runoff predictions by state-of-art models in the case of strong research data scarcity (for ungauged basins). Early stage of research aimed to coupling of conceptual hydrological model, cutting edge machine learning techniques and various sources of geographical data will be proposed with the call for intensification of cross-disciplinary research activities for the Arctic region sustainable development and safety.


Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.


2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.


2018 ◽  
Vol 35 (4) ◽  
pp. 110-113
Author(s):  
V. A. Tupchienko ◽  
H. G. Imanova

The article deals with the problem of the development of the domestic nuclear icebreaker fleet in the context of the implementation of nuclear logistics in the Arctic. The paper analyzes the key achievements of the Russian nuclear industry, highlights the key areas of development of the nuclear sector in the Far North, and identifies aspects of the development of mechanisms to ensure access to energy on the basis of floating nuclear power units. It is found that Russia is currently a leader in the implementation of the nuclear aspect of foreign policy and in providing energy to the Arctic region.


2020 ◽  
Vol 33 (5) ◽  
pp. 480-489
Author(s):  
L. P. Golobokova ◽  
T. V. Khodzher ◽  
O. N. Izosimova ◽  
P. N. Zenkova ◽  
A. O. Pochyufarov ◽  
...  

2011 ◽  
Author(s):  
Chimerebere Onyekwere Nkwocha ◽  
Evgeny Glebov ◽  
Alexey Zhludov ◽  
Sergey Galantsev ◽  
David Kay

2021 ◽  
Vol 13 (10) ◽  
pp. 1884
Author(s):  
Jingjing Hu ◽  
Yansong Bao ◽  
Jian Liu ◽  
Hui Liu ◽  
George P. Petropoulos ◽  
...  

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.


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