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Machine Learning and Big Data Analytics in Energy Infrastructure, including Economic Implications

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15052

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Special Issue Editors

Dr. Dipankar Deb

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Guest Editor

Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India
Interests: active flow control; aircraft flight control; machine learning; wind farm controls
Special Issues, Collections and Topics in MDPI journals

Dr. Moinak Maiti

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Guest Editor

Department of Finance，St.Petersburg School of Economics and Management, National Research University Higher School of Economics, Saint Petersburg, Russia
Interests: asset pricing; behavioral finance; big data analytics; financial econometrics; risk modeling
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Prof. Dr. Ram Bilas Pachori

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Guest Editor

Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
Interests: signal and image processing; biomedical signal processing; non-stationary signal processing; speech signal processing; brain–computer interfacing; machine learning; AI and IoT in healthcare
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Special Issue Information

Dear Colleagues,

Machine learning and big data analytics are playing a big role in energy infrastructure, profit maximization, and resource allocation, both for renewable and non-renewable sources. There are many different economic considerations in all such decisions. Diagnostics, prognostics, and predictive analytics are important aspects of all energy installations which can benefit from machine learning and big data analytics. The key aim of this Special Issue is to present novel theoretical and applicative research developments covering these topics. Therefore, high-quality, not yet published papers from researchers and professionals working in the field of diagnostics and monitoring of energy infrastructure are expected. The proposed papers can deal with detecting winding, bearing or other mechanical faults of the energy installations, and their power converters by means of online and offline, intrusive and non-intrusive, signal-, model- or data-based methods. The proposed approaches can be based on improvements of the traditional time and frequency domain and discrete wavelet transform analysis or modern artificial intelligence-based methods. Papers covering the advanced diagnosis and monitoring methods are strongly welcomed. Further, those dealing with methods to be directly applied in the industrial environment or with comprehensive industrial experiences in the field will be highly appreciated.

Dr. Dipankar Deb
Dr. Moinak Maiti
Prof. Ram Bilas Pachori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence-based methods
big data feature learning
data-based techniques
deep learning
digital image processing
digital signal processing
empirical mode decomposition
entropy-based methods
feature extraction methods
fuzzy logic-based techniques
industrial Internet of Things
machine current signature analysis
machine learning
model-based techniques
neural network-based methods
partial discharge monitoring
signal-based techniques
statistical diagnosis methods
support vector machine
predictive analytics

Published Papers (3 papers)

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Research

14 pages, 706 KiB

Open AccessArticle

Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms

by Alan Turnbull and James Carroll

Energies 2021, 14(16), 4922; https://doi.org/10.3390/en14164922 - 11 Aug 2021

Cited by 10 | Viewed by 2566

Abstract

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site. Full article

(This article belongs to the Special Issue Machine Learning and Big Data Analytics in Energy Infrastructure, including Economic Implications)

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25 pages, 3347 KiB

Open AccessFeature PaperArticle

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

by Tianze Lan, Kittisak Jermsittiparsert, Sara T. Alrashood, Mostafa Rezaei, Loiy Al-Ghussain and Mohamed A. Mohamed

Energies 2021, 14(3), 569; https://doi.org/10.3390/en14030569 - 22 Jan 2021

Cited by 120 | Viewed by 8397

Abstract

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme. Full article

(This article belongs to the Special Issue Machine Learning and Big Data Analytics in Energy Infrastructure, including Economic Implications)

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20 pages, 3710 KiB

Open AccessArticle

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

by Fahd A. Alturki and Emad Mahrous Awwad

Energies 2021, 14(2), 489; https://doi.org/10.3390/en14020489 - 18 Jan 2021

Cited by 47 | Viewed by 3381

Abstract

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254 $/kWh) which was determined using WOA at the same load demand. Full article

(This article belongs to the Special Issue Machine Learning and Big Data Analytics in Energy Infrastructure, including Economic Implications)

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