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Artificial Neural Network Applications in the Simulation Modeling, Control, and Fault Diagnosis of Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 10 August 2026 | Viewed by 3099

Special Issue Editor

Prof. Dr. Ali Alouani

E-Mail Website1 Website2
Guest Editor
Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Interests: smart grid; EV battery health; autonomous system design; sensor data fusion; home health monitoring; intelligent systems; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Demand for energy is increasing globally. The control and fault diagnosis of energy systems such as smart grids and storage batteries are crucial for ensuring efficiency, maintaining the health of such systems, and avoiding potential blackouts and catastrophic incidents. A strong control system requires a reliable system model to ensure stability and efficiency. However, the complexity of energy systems makes classical modeling using the known laws of physics difficult to achieve. Similarly, the design of analytical control systems and fault diagnosis may not be possible for such systems.

This Special Issue aims to disseminate recent advances in modeling, control, efficiency, and fault diagnosis in energy systems.

Topics of interest for publication include, but are not limited to, the following:

  • Review papers of energy system modeling;
  • Modeling energy systems using AI;
  • Controlling energy systems using AI;
  • Diagnosing faults in energy systems using AI;
  • Data analytics in energy system efficiency.

Prof. Dr. Ali Alouani
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • energy systems
  • modeling
  • control
  • fault diagnosis
  • efficiency
  • artificial intelligence (AI)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 4372 KB  
Article
Physics-Informed Domain Adaptation for Stator Inter-Turn Short Circuit Diagnosis in Synchronous Machines Using Excitation Current Signatures
by Jarosław Kozik
Energies 2026, 19(9), 2231; https://doi.org/10.3390/en19092231 - 5 May 2026
Viewed by 287
Abstract
Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical [...] Read more.
Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in the Simulation Modeling, Control, and Fault Diagnosis of Energy Systems)
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31 pages, 9977 KB  
Article
Novel Deep Learning Framework for Evaporator Tube Leakage Estimation in Supercharged Boiler
by Yulong Xue, Dongliang Li, Yu Song, Shaojun Xia and Jingxing Wu
Energies 2025, 18(15), 3986; https://doi.org/10.3390/en18153986 - 25 Jul 2025
Cited by 1 | Viewed by 978
Abstract
The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from [...] Read more.
The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from noise. Additionally, the large number of monitoring parameters (approximately 140) poses a challenge for spatiotemporal feature extraction, feature decoupling, and establishing a mapping relationship between high-dimensional monitoring parameters and leakage, rendering the precise quantitative estimation of evaporation tube leakage extremely difficult. To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. This framework processes temporal embeddings (LSTM-generated) via a dual-branch ACNN—where the 1D-CNN captures local spatial features and the attention models’ global significance—yielding decoupled representations that prevent cross-modal interference. This architecture is implemented in a simulated supercharged boiler, validated with datasets encompassing three operational conditions and 15 statuses in the supercharged boiler. The framework achieves an average diagnostic accuracy (ADA) of over 99%, an average estimation accuracy (AEA) exceeding 90%, and a maximum relative estimation error (MREE) of less than 20%. Even with a signal-to-noise ratio (SNR) of −4 dB, the ADA remains above 90%, while the AEA stays over 80%. This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in the Simulation Modeling, Control, and Fault Diagnosis of Energy Systems)
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18 pages, 2594 KB  
Article
Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes
by Bartłomiej Kiczek and Michał Batsch
Energies 2025, 18(14), 3630; https://doi.org/10.3390/en18143630 - 9 Jul 2025
Cited by 2 | Viewed by 1306
Abstract
Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with [...] Read more.
Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with computational efficiency that allows for use on edge devices. This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. Using only experimentally measured vibrational signals from a healthy gearbox as a training set, novel neural network architectures, including convolutional and recurrent autoencoders, were employed and compared with a classical dense autoencoder. The study confirmed the effectiveness of these methods in gear transmission diagnostics and demonstrated the potential for achieving high-quality classification metrics using unsupervised learning. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in the Simulation Modeling, Control, and Fault Diagnosis of Energy Systems)
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