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Uddin Ahmed, K. M., Bollen, M. H. J. & Alvarez, M. (2023). A Stochastic Approach to Determine the Optimal Number of Servers for Reliable and Energy Efficient Operation of Data Centers. IEEE Transactions on Sustainable Computing, 8(2), 153-164
Open this publication in new window or tab >>A Stochastic Approach to Determine the Optimal Number of Servers for Reliable and Energy Efficient Operation of Data Centers
2023 (English)In: IEEE Transactions on Sustainable Computing, E-ISSN 2377-3782, Vol. 8, no 2, p. 153-164Article in journal (Refereed) Published
Abstract [en]

The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers' failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
data center operation, Monte Carlo simulation, risk assessment, stochastic modeling, server failure
National Category
Other Civil Engineering Energy Systems
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-94241 (URN)10.1109/tsusc.2022.3216350 (DOI)001005680900001 ()2-s2.0-85163183639 (Scopus ID)
Funder
Swedish Energy Agency, 43090-2Norrbotten County Council
Note

Validerad;2023;Nivå 2;2023-07-12 (sofila);

Funder: Cloudberry Datacenters project

Available from: 2022-11-23 Created: 2022-11-23 Last updated: 2023-09-05Bibliographically approved
Oliveira, R. A. & Bollen, M. H. .. (2023). Deep learning for power quality. Electric power systems research, 214, Article ID 108887.
Open this publication in new window or tab >>Deep learning for power quality
2023 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 214, article id 108887Article, review/survey (Refereed) Published
Abstract [en]

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in terms of application, type of data, and learning technique. This work shows that the majority of the deep learning applications to power quality are based on unrealistic synthetic data and supervised learning without proper labelling. Some applications with deep learning have already been solved by previous machine learning methods or expert systems. The main barriers to implementing deep learning to power quality are related to lack of novelty, low transparency of the deep learning methods, and lack of benchmark databases. This work also discusses that even with automatic feature extraction by deep learning methods, power quality expert knowledge is still needed to implement and analyse the results. The main research gaps identified in this work are related to the applications of semi-supervised learning, explainable deep learning and hybrid approaches combining deep learning with expert systems. Suggestions for overcoming the present limitations are: providing a stronger collaboration among the grid stakeholders and academy to keep track of power quality events; proper labelling and enlarging of datasets in deep learning methods; explaining the end-to-end decision making of deep learning methods; providing open-access databases for comparison purposes.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Artificial intelligence, Data analysis, Deep learning, Machine learning, Power quality
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93726 (URN)10.1016/j.epsr.2022.108887 (DOI)2-s2.0-85139840780 (Scopus ID)
Funder
Swedish Energy Agency
Note

Validerad;2022;Nivå 2;2022-10-26 (hanlid)

Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2023-08-24Bibliographically approved
Lennerhag, O., Lundquist, J. & Bollen, M. (2023). Efficient Calculation of Switching Overvoltages Considering Corona Attenuation. IEEE Transactions on Power Delivery, 38(4), 2735-2741
Open this publication in new window or tab >>Efficient Calculation of Switching Overvoltages Considering Corona Attenuation
2023 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 38, no 4, p. 2735-2741Article in journal (Refereed) Published
Abstract [en]

This paper utilizes the Unscented Transform together with Cornish-Fisher expansion to estimate the 2%-value of switching overvoltages when considering the impact of trapped charge and corona attenuation. Simulations were performed using a piecewise linear corona model for twin- and triple-conductor lines. The proposed method was compared to Monte Carlo methods through simulations in PSCAD. The method is shown to be able to estimate the 2%-value with comparable accuracy to methods used in industry today, but with only one fifth of the number of calculations.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Insulation coordination, Corona, Power system transients, Stochastic processes, Switching overvoltages
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-91959 (URN)10.1109/TPWRD.2023.3255782 (DOI)2-s2.0-85149856354 (Scopus ID)
Funder
Swedish Energy Agency, 44360-1Swedish National GridEnergy Research
Note

Validerad;2023;Nivå 2;2023-08-16 (joosat);

This article has previously appeared as a manuscript in a thesis.

Available from: 2022-06-28 Created: 2022-06-28 Last updated: 2023-08-16Bibliographically approved
Nakhodchi, N. & Bollen, M. H. .. (2023). Impact of modelling of MV network and remote loads on estimated harmonic hosting capacity for an EV fast charging station. International Journal of Electrical Power & Energy Systems, 147, Article ID 108847.
Open this publication in new window or tab >>Impact of modelling of MV network and remote loads on estimated harmonic hosting capacity for an EV fast charging station
2023 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 147, article id 108847Article in journal (Refereed) Published
Abstract [en]

The ability of the distribution network to host electric vehicle (EV) charging might be limited by the harmonic voltages due to their harmonic emission. Network harmonic impedance seen from the point of connection of the charging station plays an important role in harmonic voltage calculation. In this paper, the hosting capacity is estimated for a fast charging station close to a distribution transformer considering different scenarios in terms of network modelling. Data from a typical Swedish distribution network is used, together with a one-month measurement of the emission from state-of-the-art EV charging. The hosting capacity using harmonic voltage limits is compared with the hosting capacity using the transformer rating. A stochastic approach is used for both. This paper shows that harmonic hosting capacity studies are needed; it shows that details of the distribution network must be included to get an accurate estimation of the harmonic hosting capacity; it also shows that a stochastic approach is needed for estimating the harmonic hosting capacity.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Electric vehicle charging, Harmonic analysis, Harmonic distortion, Hosting capacity, Power system harmonics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-96350 (URN)10.1016/j.ijepes.2022.108847 (DOI)2-s2.0-85143799248 (Scopus ID)
Funder
Swedish Energy Agency
Note

Validerad;2023;Nivå 2;2023-04-11 (hanlid);

Funder: Umeå Energi; Göteborg Energi research foundation

Available from: 2023-04-10 Created: 2023-04-10 Last updated: 2023-04-11Bibliographically approved
Nakhodchi, N., Bakhtiari, H., Bollen, M. H. J. & Rönnberg, S. K. (2023). Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation. Electric power systems research, 214, Article ID 108933.
Open this publication in new window or tab >>Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation
2023 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 214, article id 108933Article in journal (Refereed) Published
Abstract [en]

The harmonic emission from an electric vehicle fast charger depends on factors like charger topology, EV type, initial state of charge of EV battery, as well as supply voltage and background distortion. This paper presents the results from harmonic current measurement of a fast charger for a period of one month in Sweden that has charged a variety of EVs from different brands under different state of charge and background distortion. Besides the common harmonic emission pattern, a high level of variation in emission is observed that can affect the aggregation of the emission from multiple chargers. To include such uncertainties, the harmonic hosting capacity is obtained for a fast EV charging station in a stochastic way. A new method, based on Bayesian statistics and the correlation between harmonic magnitude and fundamental magnitude, is proposed for the generation of stochastic samples. It is shown that the proposed method, to a high extent, can model the stochastic behavior of harmonic emission from a fast charger. Furthermore, the results show that neglecting the correlation between harmonic magnitude and fundamental magnitude can underestimate the harmonic hosting capacity.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Electric vehicle charging, harmonic analysis, harmonic distortion, hosting capacity, power system harmonics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93358 (URN)10.1016/j.epsr.2022.108933 (DOI)000886826000009 ()2-s2.0-85140914689 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-08 (hanlid);

Funder: Göteborg Energi Research Foundation; Umeå Energi;

This article has previously appeared as a manuscript in a thesis

Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2023-09-05Bibliographically approved
Sudha Letha, S., Bollen, M. H. J., Busatto, T., Espin Delgado, A., Mulenga, E., Bakhtiari, H., . . . Ravindran, V. (2023). Power Quality Issues of Electro-Mobility on Distribution Network—An Overview. Energies, 16(13), Article ID 4850.
Open this publication in new window or tab >>Power Quality Issues of Electro-Mobility on Distribution Network—An Overview
Show others...
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 13, article id 4850Article, review/survey (Refereed) Published
Abstract [en]

The journey towards sustainable transportation has significantly increased the grid penetration of electric vehicles (EV) around the world. The connection of EVs to the power grid poses a series of new challenges for network operators, such as network loading, voltage profile perturbation, voltage unbalance, and other power quality issues. This paper presents a coalescence of knowledge on the impact that electro-mobility can impose on the grid, and identifies gaps for further research. Further, the study investigates the impact of electric vehicle charging on the medium-voltage network and low-voltage distribution network, keeping in mind the role of network operators, utilities, and customers. From this, the impacts, challenges, and recommendations are summarized. This paper will be a valuable resource to research entities, industry professionals, and network operators, as a ready reference of all possible power quality challenges posed by electro-mobility on the distribution network.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
electric vehicle, harmonics, light flicker, power quality, voltage unbalance
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-99488 (URN)10.3390/en16134850 (DOI)001028620400001 ()2-s2.0-85164930081 (Scopus ID)
Funder
Swedish Energy Agency, 47904-1
Note

Validerad;2023;Nivå 2;2023-08-11 (hanlid)

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-09-05Bibliographically approved
Bagheri, A., de Oliveira, R. A., Bollen, M. H. J. & Gu, I. Y. H. (2022). A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances. Energies, 15(4), Article ID 1283.
Open this publication in new window or tab >>A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
2022 (English)In: Energies, E-ISSN 1996-1073, Vol. 15, no 4, article id 1283Article in journal (Refereed) Published
Abstract [en]

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
anomaly detection, machine learning, power quality, principal component analysis, space phasor model
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-89920 (URN)10.3390/en15041283 (DOI)000778148300001 ()2-s2.0-85126538910 (Scopus ID)
Funder
Swedish Energy Agency, P39437-1Swedish Energy Agency, P42979-1Swedish Transport Administration, 36267
Note

Validerad;2022;Nivå 2;2022-03-28 (hanlid);

Funder: Energiforsk

Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2023-08-28Bibliographically approved
Mohammadi, Y., Miraftabzadeh, S. M., Bollen, M. & Longo, M. (2022). An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale. Sustainable Energy, Grids and Networks, 31, Article ID 100773.
Open this publication in new window or tab >>An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale
2022 (English)In: Sustainable Energy, Grids and Networks, ISSN 2352-4677, Vol. 31, article id 100773Article in journal (Refereed) Published
Abstract [en]

This paper proposes an unsupervised learning schema for seeking the patterns in rms voltage variations at the time scale between 1 s and 10 min, a rarely considered time scale in studies but could be relevant for incorrect operation of end-user equipment. The proposed framework employs a Kernel Principal Component Analysis (KPCA) followed by a k-means clustering. The schema is applied on 10-min time series with a 1-s time resolution obtained from 44 different periods of a location south of Sweden. Then, ten patterns are obtained by reconstructing the 10-min time series from each cluster center. The results of the proposed schema show a good separation of cluster centers. Moreover, some statistical power-quality indices are applied to the whole dataset, showing voltage variation between (0.5–3) V over a 10-min window. Obtaining the most suitable indices and applying them to the ten obtained cluster centers and their belonging time series shows that the existing statistical indices may not be enough to show a complete picture of the sub-10 min actual variations. This outcome shows the necessity of extracting 10-min patterns through our proposed schema besides the existing statistics to quantify the voltage variations, levels, and patterns together. Findings of this paper are: Not forgetting the sub-10-min time scale; The necessity of employing both statistics and the proposed schema; Extraction of ten typical patterns; The need for the statistics and patterns that are justified as changes in equipment connected to the grid; and compressing a huge amount of data from power-quality monitoring. The proposed schema is applied to a much less understood phenomena/disturbance type so that this work will result in general knowledge beyond the specific case study.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Power-quality monitoring, Voltage variations, Seeking patterns, Time series clustering, Kernel PCA (KPCA)
National Category
Computer Systems Infrastructure Engineering Fluid Mechanics and Acoustics
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-90846 (URN)10.1016/j.segan.2022.100773 (DOI)000807419100010 ()2-s2.0-85131068283 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-06-12 (marisr);

For correction, see: Mohammadi, Y., Miraftabzadeh, S. M., Bollen, M.H.J. et al. Corrigendum to: An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale. Sustainable Energy, Grids and Networks 32, 100918 (2022). https://doi.org/10.1016/j.segan.2022.100918

Available from: 2022-06-01 Created: 2022-06-01 Last updated: 2023-06-12Bibliographically approved
de Oliveira, R. A., De Souza Salles, R., Bollen, M. H. .. & de Carlí, M. P. (2022). Analysing Waveform Distortion in Wind Power Plants by a Deep Learning-Based Graphical Tool. In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”. Paper presented at 20th International Conference on Harmonics & Quality of Power (ICHQP 2022), Naples, Italy, May 29 - June 1, 2022. IEEE
Open this publication in new window or tab >>Analysing Waveform Distortion in Wind Power Plants by a Deep Learning-Based Graphical Tool
2022 (English)In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

This work shows an application of a deep learning-based graphical tool for analyzing waveform distortion in wind power plants. The tool consists of a deep autoencoder followed by a clustering algorithm. Previous applications of such a tool have covered harmonic emission which follows daily patterns. The challenge of measurements in wind power plants is the intermittence of the power production, which can vary in a time frame of minutes and hours. To this point, this work proposes a modification of a DL method presented in the literature to address measurements from wind power plants. The method can automatically obtain the number of clusters. The method is applied to harmonic measurements from H2 to H50 and active power in a Brazilian wind power plant. The graphical results allowed obtaining the correlation between patterns of odd and even current harmonic with the active power generated by a wind power plant.

Place, publisher, year, edition, pages
IEEE, 2022
Series
International Conference on Harmonics and Quality of Power, ISSN 1540-6008, E-ISSN 2164-0610
Keywords
wind power generation, waveform distortion, deep learning, machine learning, harmonic analysis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-92107 (URN)10.1109/ICHQP53011.2022.9808731 (DOI)000844604500089 ()2-s2.0-85133765019 (Scopus ID)
Conference
20th International Conference on Harmonics & Quality of Power (ICHQP 2022), Naples, Italy, May 29 - June 1, 2022
Funder
Swedish Energy AgencySwedish Transport Administration
Note

ISBN för värdpublikation: 978-1-6654-1639-9

Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2022-09-19Bibliographically approved
Sudha Letha, S., Bollen, M. H. .. & Rönnberg, S. (2022). Analysis and Recommendations for LED Catastrophic Failure Due to Voltage Stress. Energies, 15(2), Article ID 540.
Open this publication in new window or tab >>Analysis and Recommendations for LED Catastrophic Failure Due to Voltage Stress
2022 (English)In: Energies, E-ISSN 1996-1073, Vol. 15, no 2, article id 540Article in journal (Refereed) Published
Abstract [en]

Light-emitting diode (LED) lighting has, compared to other types of lighting, a significantly lower energy consumption. However, the perceived service life is also important for customer satisfaction and here there is a discrepancy between customers’ experience and manufacturers’ statements. Many customers experience a significantly shorter service life than claimed by the manufacturers. An experiment was carried out in the Pehr Högström Laboratory at Luleå University of Technology in Skellefteå, Sweden to investigate whether voltage disturbances could explain this discrepancy. Over 1000 LED lamps were exposed to high levels of voltage disturbances for more than 6000 h; the failure rate from this experiment was similar to the one from previous experiments in which lamps were exposed to normal voltage. The discrepancy thus remains, even though some possible explanations have emerged from the project’s results. The lamps were exposed to five different types of voltage disturbances: short interruptions; transients; overvoltage; undervoltage; and harmonics. Only overvoltage resulted in failure of the lamps, and only for a single topology of lamp. A detailed analysis has been made of the topology of lamps that failed. This lamp type contains a different internal electronics circuit than the other lamp types. Failures of the lamps when exposed to overvoltage are due to the heat development in the control circuit increasing sharply when the lamps are exposed to a higher voltage. Hence, it is concluded that there are lamps that are significantly more sensitive to voltage disturbances than other lamp types. Manufactures need to consider the voltage quality that can be expected at the terminal of the lamp to prevent failure of lamps due to voltage disturbances. This paper therefore contains recommendations for manufacturers of lighting; the recommendations describe which voltage disturbances lamps should cope with.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
Catastrophic failure, Filament lamp, Heat sink type lamp, LED lamps, Power quality, Voltage disturbances
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-89035 (URN)10.3390/en15020540 (DOI)000748048700001 ()2-s2.0-85122874808 (Scopus ID)
Funder
Swedish Energy Agency, 46678-1
Note

Validerad;2022;Nivå 2;2022-02-03 (johcin)

Available from: 2022-02-03 Created: 2022-02-03 Last updated: 2023-09-05Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4074-9529

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