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Special Issue Information

Dear Colleagues,

Due to the availability of sophisticated information and communication technology, it has become viable to collect data from a single component or from a whole system, which can be used later to gain valuable insights for smart decision making. The applications of data-driven decision-making can be found in numerous fields, including medicine, finance, transportation, industrial setup, etc. Data-driven intelligent decision making is also applicable in the energy sector. With the availability of a wide range of sensors and advanced algorithms of artificial intelligence, it is possible to develop strategies that can improve monitoring, efficiency, reliability, and control of energy systems. The devised strategies might just consist of simple statistical analysis of the data to perform a cost–benefit analysis and forecasting or to address a technical issue, for instance, fault detection diagnosis, and prognosis of energy systems.

This Special Issue aims to present original research papers with high quality and novelty and also review papers on “Data Analytics in Energy Systems”.

Topics of interest include but are not limited to:

Data analytics for energy system operation and control;
Multimodal data analytics and fusion;
Distributed data mining;
Artificial intelligence, machine learning, and deep learning for energy systems;
Cloud computing for data analytics in energy systems;
Data analytics for energy demand forecasting;
Data collection, visualization, statistical analysis, storage, and information management in energy systems;
Fault detection, diagnosis, and prognosis methodologies;
Model and fuzzy-based control design for smart power systems.

Dr. Farzin Piltan
Dr. M. M.Manjurul Islam
Dr. Belem Saldivar
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
Automation
Big data
Deep learning
Data analytics
Data processing
Fault detection and diagnosis
Energy Systems
Machine learning
System operation and control
Smart grid

Published Papers (2 papers)

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Research

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Open AccessArticle

A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power

Tanvir Alam Shifat

Rubiya Yasmin

and

Jang-Wook Hur

Energies 2021, 14(11), 3156; https://doi.org/10.3390/en14113156 - 28 May 2021

Cited by 9 | Viewed by 2263

Abstract

An effective remaining useful life (RUL) estimation method is of great concern in industrial machinery to ensure system reliability and reduce the risk of unexpected failures. Anticipation of an electric motor’s future state can improve the yield of a system and warrant the reuse of the industrial asset. In this paper, we present an effective RUL estimation framework of brushless DC (BLDC) motor using third harmonic analysis and output apparent power monitoring. In this work, the mechanical output of the BLDC motor is monitored through a coupled generator. To emphasize the total power generation, we have analyzed the trend of apparent power, which preserves the characteristics of real power and reactive power in an AC power system. A normalized modal current (NMC) is used to extract the current features from the BLDC motor. Fault characteristics of motor current and generator power are fused using a Kalman filter to estimate the RUL. Degradation patterns for the BLDC motor have been monitored for three different scenarios and for future predictions, an attention layer optimized bidirectional long short-term memory (ABLSTM) neural network model is trained. ABLSTM model’s performance is evaluated based on several metrics and compared with other state-of-the-art deep learning models. Full article

(This article belongs to the Special Issue Data-Protection Combined with Machine Learning for AI-Integrated Smart Power Systems)

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Graphical abstract

Review

Jump to: Research

Open AccessReview

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Shiza Mushtaq

M. M. Manjurul Islam

and

Muhammad Sohaib

Energies 2021, 14(16), 5150; https://doi.org/10.3390/en14165150 - 20 Aug 2021

Cited by 29 | Viewed by 3562

Abstract

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented. Full article

(This article belongs to the Special Issue Data-Protection Combined with Machine Learning for AI-Integrated Smart Power Systems)

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