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Processes
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AI-Driven Advanced Process Control for Smart Energy Systems
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AI-Driven Advanced Process Control for Smart Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 30 March 2026 | Viewed by 654

Special Issue Editors

Dr. Jingbo Wang

E-Mail Website
Guest Editor
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
Interests: renewable energy system optimization and control; power sector decarbonization
Prof. Dr. Bo Yang

E-Mail Website
Guest Editor
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: renewable energy power generation systems; optimization and control; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yiyan Sang

E-Mail Website
Guest Editor
College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Interests: control of power-electronics-based power systems; advanced controller design for AC-DC converters

Special Issue Information

Dear Colleagues,

The rapid advancement of industrial automation and digital transformation has driven the need for intelligent algorithms that can address the growing complexity, uncertainty, and dynamic nature of modern process control systems. Applications span chemical and petrochemical processes, power and energy systems, including high-voltage direct current transmission (HVDC), optimal power flow, renewable integration, and the intelligent management of electric mobility charging networks, as well as advanced manufacturing and environmental engineering. Future process control will involve large-scale, interconnected systems with nonlinear dynamics, model-free and data-driven methods, and advanced controller monitoring. Emphasis will be placed on sustainability, energy efficiency, and the integration of artificial intelligence and machine learning for resilient, adaptive, and self-optimizing operations. This Special Issue focuses on novel theories, methods, and applications of intelligent algorithms for advanced process control, bridging computational innovation with practical deployment in industrial, power, and sustainable transportation systems. Topics of interest include, but are not limited to, the following:

  • Intelligent optimization methods for process control;
  • Model-free, data-driven, and predictive control strategies;
  • Control of nonlinear and large-scale industrial, power, and HVDC systems;
  • Optimal power flow modeling, analysis, and control in modern grids;
  • Controller monitoring, performance assessment, and fault-tolerant control;
  • System identification, modeling, and real-time decision-making;
  • Sustainability and energy-efficient control solutions;
  • Intelligent coordination of renewable integration and electric vehicle networks;
  • Fault detection, diagnosis, and adaptive control;
  • Applications in chemical, energy, manufacturing, and environmental processes.

Dr. Jingbo Wang
Prof. Dr. Bo Yang
Dr. Yiyan Sang
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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • intelligent process control
  • artificial intelligence
  • model-free control
  • data-driven control
  • nonlinear systems
  • HVDC and optimal power flow
  • renewable and electric vehicles
  • controller monitoring
  • sustainable control solutions
  • real-time decision-making

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

Published Papers (2 papers)

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Research

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24 pages, 10879 KB  
Article
Exploring Kolmogorov–Arnold Networks for Unsupervised Anomaly Detection in Industrial Processes
by Enrique Luna-Villagómez and Vladimir Mahalec
Processes 2025, 13(11), 3672; https://doi.org/10.3390/pr13113672 - 13 Nov 2025
Viewed by 94
Abstract
Designing reliable fault detection and diagnosis (FDD) systems remains difficult when only limited fault-free data are available. Kolmogorov–Arnold Networks (KANs) have recently been proposed as parameter-efficient alternatives to multilayer perceptrons, yet their effectiveness for unsupervised FDD has not been systematically established. This study [...] Read more.
Designing reliable fault detection and diagnosis (FDD) systems remains difficult when only limited fault-free data are available. Kolmogorov–Arnold Networks (KANs) have recently been proposed as parameter-efficient alternatives to multilayer perceptrons, yet their effectiveness for unsupervised FDD has not been systematically established. This study presents a statistically grounded comparison of Kolmogorov–Arnold Autoencoders (KAN-AEs) against an orthogonal autoencoder and a PCA baseline using the Tennessee Eastman Process benchmark. Four KAN-AE variants (EfficientKAN-AE, FastKAN-AE, FourierKAN-AE, and WavKAN-AE) were trained on fault-free data subsets ranging from 625 to 250,000 samples and evaluated over 30 independent runs. Detection performance was assessed using Bayesian signed-rank tests to estimate posterior probabilities of model superiority across fault scenarios. The results show that WavKAN-AE and EfficientKAN-AE achieve approximately 90–92% fault detection rate with only 2500 samples. In contrast, the orthogonal autoencoder requires over 30,000 samples to reach comparable performance, while PCA remains markedly below this level regardless of data size. Under data-rich conditions, Bayesian tests show that the orthogonal autoencoder matches or slightly outperforms the KAN-AEs on the more challenging fault scenarios, while remaining computationally more efficient. These findings position KAN-AEs as compact, data-efficient tools for industrial fault detection when historical fault-free data are scarce. Full article
(This article belongs to the Special Issue AI-Driven Advanced Process Control for Smart Energy Systems)
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Review

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26 pages, 2244 KB  
Review
Analysis and Mitigation of Wideband Oscillations in PV-Dominated Weak Grids: A Comprehensive Review
by Runzhi Mu, Yuming Zhang, Xiongbiao Wan, Deng Wang, Tianshu Wen, Zichao Zhou, Liming Sun and Bo Yang
Processes 2025, 13(11), 3450; https://doi.org/10.3390/pr13113450 - 27 Oct 2025
Viewed by 443
Abstract
The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning [...] Read more.
The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning sub-Hz to kHz ranges. This review provides a PV-focused and mitigation-oriented analysis that addresses this gap. First, it clarifies the mechanisms of WBOs by mapping oscillatory drivers such as phase-locked loop dynamics, constant power control, converter–grid impedance resonance, and high-frequency switching effects to their corresponding frequency bands, alongside their engineering implications. Second, analysis methods are systematically evaluated, including eigenvalue and impedance-based models, electromagnetic transient simulations, and measurement- and data-driven techniques, with a comparative assessment of their strengths, limitations, and practical applications. Third, mitigation strategies are classified across converter-, plant-, and system-levels, ranging from adaptive control and virtual impedance to coordinated PV-battery energy storage systems (BESS) operation and grid-forming inverters. The review concludes by identifying future directions in grid-forming operation, artificial intelligence (AI)-driven adaptive stability, hybrid PV-BESS-hydrogen integration, and the establishment of standardized compliance frameworks. By integrating mechanisms, methods, and mitigation strategies, this work provides a comprehensive roadmap for addressing oscillatory stability in PV-dominated weak grids. Full article
(This article belongs to the Special Issue AI-Driven Advanced Process Control for Smart Energy Systems)
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