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Modeling, Monitoring, and Coordinated Control of Dynamic Processes in Grid-Connected...
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Modeling, Monitoring, and Coordinated Control of Dynamic Processes in Grid-Connected Inverter-Interfaced Energy Systems

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "Energy Systems".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 1081

Editors

Dr. Junlei Chen

E-Mail Website
Guest Editor
School of Intelligent Engineering, Shaoxing University, Shaoxing 312000, China
Interests: machine design and analysis of electric drive systems; grid integration control for new energy sources; servo control
Dr. Qiushuo Chen

E-Mail Website
Guest Editor
School of Intelligent Engineering, Shaoxing University, Shaoxing 312000, China
Interests: designing and analyzing low-speed direct-drive motors for electric traffic
Dr. Xu Wang

E-Mail Website
Guest Editor
School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Interests: machine design and analysis of electric drive systems; grid integration control for new energy sources; variable-speed pumped storage systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Fan

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: electric drives; electric vehicles; wind power generation

Special Issue Information

Dear Colleagues,

With the rapid development of photovoltaic generation, wind power, battery energy storage, electric vehicles, and distributed energy resources, modern power systems are evolving toward high penetration of power electronic and inverter-interfaced energy resources. Grid-connected inverters are no longer simple power conversion interfaces; they are becoming key components in renewable energy integration, grid dynamic support, fault response, energy coordination, and system stability control. Their operational characteristics are affected not only by devices and controllers, but also by renewable energy fluctuations, storage operating conditions, load variations, weak-grid characteristics, multi-inverter coupling, and grid-code requirements.

This Special Issue focuses on dynamic processes in grid-connected inverter-interfaced energy systems, with particular emphasis on recent advances in modeling, monitoring, diagnosis, control, and operational optimization. Contributions are encouraged to investigate the dynamic interactions among inverters, renewable energy generation units, energy storage systems, loads, and power grids from the perspectives of mechanism-based modeling, data-driven analysis, and hybrid mechanism-data approaches. Topics related to stability, reliability, and operational performance under weak grids, high renewable energy penetration, multiple parallel inverters, and complex operating conditions are especially welcome.

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

  • Dynamic process modeling and characteristic analysis of grid-connected inverter-interfaced energy systems;
  • Online monitoring, parameter identification, and state estimation methods for grid-connected inverters;
  • Digital twin and data-driven methods for inverter operation, degradation tracking, and performance optimization;
  • Coordinated control of multiple grid-connected inverters in renewable energy plants, microgrids, and distribution networks;
  • Stability assessment and interaction mechanism analysis of inverter-dominated energy systems under weak-grid conditions;
  • Fault diagnosis, health management, and lifecycle reliability assessment of power electronic interfaces;
  • Grid-code compliance, fault ride-through, and resilient operation of inverter-interfaced energy systems.

Dr. Junlei Chen
Dr. Qiushuo Chen
Dr. Xu Wang
Prof. Dr. Ying Fan
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 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-anonymized 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 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 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

  • grid-connected inverter
  • inverter-interfaced energy system
  • dynamic process modeling
  • online monitoring
  • parameter identification
  • state estimation
  • digital twin
  • data-driven method
  • coordinated control
  • weak grid
  • stability assessment
  • interaction mechanism
  • fault diagnosis
  • health management
  • lifecycle reliability
  • grid-code compliance
  • fault ride-through
  • resilient operation
  • renewable energy integration
  • energy storage system

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Published Papers (3 papers)

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Research

19 pages, 3295 KB  
Article
Dual-Terminal Third-Order Super-Twisting Sliding Mode Control of M3C for Low-Frequency Transmission
by Ziming Li, Yi Lu, Yanxia Yao, Jiachuan You, Chao Ding, Xiaojun Ni and Jiaxing Lei
Processes 2026, 14(19), 3078; https://doi.org/10.3390/pr14193078 - 25 Sep 2026
Viewed by 152
Abstract
Conventional controllers and standard super-twisting sliding mode controllers (STSMCs) are commonly applied to Modular Multilevel Matrix Converters (M3Cs) in flexible low-frequency power transmission. However, under conditions of multi-variable strong coupling and time-varying disturbances, they often exhibit limited suppression capabilities against complex, high-order internal [...] Read more.
Conventional controllers and standard super-twisting sliding mode controllers (STSMCs) are commonly applied to Modular Multilevel Matrix Converters (M3Cs) in flexible low-frequency power transmission. However, under conditions of multi-variable strong coupling and time-varying disturbances, they often exhibit limited suppression capabilities against complex, high-order internal cross-coupling dynamics, leading to residual fluctuations. To address these drawbacks, this paper proposes a nonlinear control strategy based on a third-order super-twisting sliding mode controller (TOSMC). Dual outer control loops are constructed under dual d-q rotating reference frames for capacitor voltage regulation at the power-frequency side and power control at the low-frequency side, respectively. A feedforward of the reference command based on cross-port power conservation is introduced to maintain fundamental power flow and the instantaneous macroscopic energy balance of the system. Meanwhile, a multi-integral robust compensation structure is adopted to realize real-time estimation and compensation of internal parameter perturbations and complex higher-order lumped disturbances, utilizing a continuous boundary layer function to eliminate inherent chattering. Simulation results demonstrate that in transient scenarios with step active power variation, compared with the STSMC, both methods exhibit robust and nearly identical macroscopic active power tracking with zero overshoot. However, the proposed TOSMC reduces the transient fluctuation amplitude of the average sub-module capacitor voltage by approximately 15%. Without sacrificing macroscopic dynamic response speed, the proposed control strategy ensures a smoother internal energy balancing process and superior high-order anti-disturbance performance, providing a feasible engineering solution for the reliable operation of M3Cs under complicated operating conditions. Full article
(This article belongs to the Special Issue Modeling, Monitoring, and Coordinated Control of Dynamic Processes in Grid-Connected Inverter-Interfaced Energy Systems)
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23 pages, 11558 KB  
Article
Dynamic Condition-Matching Network for End-to-End Machinery Health Monitoring Under Time-Varying Operating Conditions
by Jiangling Wang, Juntao Wang, Caiming Zhong and Yu Tian
Processes 2026, 14(19), 3073; https://doi.org/10.3390/pr14193073 - 24 Sep 2026
Viewed by 60
Abstract
Dynamic monitoring of critical machinery under time-varying operating conditions remains a challenging problem. Existing methods often fail to efficiently and conveniently generate reliable health indicators. To address this issue, a Dynamic Condition-Matching Network (DCMN) is proposed to end-to-end generate machinery health indicators under [...] Read more.
Dynamic monitoring of critical machinery under time-varying operating conditions remains a challenging problem. Existing methods often fail to efficiently and conveniently generate reliable health indicators. To address this issue, a Dynamic Condition-Matching Network (DCMN) is proposed to end-to-end generate machinery health indicators under time-varying operating conditions. First, a self-supervised matching-pair-based sample generation method is developed. Real and virtual vibration–speed pairs are constructed to produce pseudo-regression labels for exploring latent cross-modal relationships. Second, cross-attention is introduced into DCMN to model the vibration–speed cross-modal representation association. Fused features that reflect deviation from the reference baseline are thereby generated. Finally, a cross-modal matching-rate prediction head is established in DCMN. The health indicators under given vibration and speed inputs can be computed in an end-to-end manner. The effectiveness of DCMN is validated through a case study under time-varying operating conditions. Experimental results demonstrate that DCMN can effectively generate condition-invariant health indicators. It outperforms the compared methods and provides an elegant approach for machinery health monitoring under time-varying conditions. Full article
(This article belongs to the Special Issue Modeling, Monitoring, and Coordinated Control of Dynamic Processes in Grid-Connected Inverter-Interfaced Energy Systems)
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21 pages, 3671 KB  
Article
Multimodal Prompt-Tuning Large Language Model for Machinery Fault Diagnosis with Sound-Vibration Signals
by Yu Tian, Juntao Wang, Ying Zheng, Jiangling Wang and Caiming Zhong
Processes 2026, 14(17), 2802; https://doi.org/10.3390/pr14172802 - 31 Aug 2026
Viewed by 526
Abstract
Vibration and sound-sensing technology has become the mainstream perception method in the field of mechanical fault diagnosis. However, most current diagnostic models still focus on single-modality data analysis, which to some extent restricts further improvement of diagnostic performance. Multimodal fault-diagnosis models are still [...] Read more.
Vibration and sound-sensing technology has become the mainstream perception method in the field of mechanical fault diagnosis. However, most current diagnostic models still focus on single-modality data analysis, which to some extent restricts further improvement of diagnostic performance. Multimodal fault-diagnosis models are still relatively lacking in existing research. In addition, most methods usually simply concatenate or early fuse vibration and sound signals and directly input them into the model, failing to deeply explore and effectively fuse the discriminative features of each modality. Moreover, the strong generalization ability of large language models (LLMs) demonstrates their potential for application in other fields. Therefore, it is interesting to perform machinery fault diagnosis based on LLMs with sound and vibration signals. This study proposes a new multimodal DNN, namely a sound-vibration large language model (SVLLM), for multimodal feature fusion and machinery fault diagnosis. Firstly, a multimodal prompt-tuning module is proposed to fine-tune LLMs to learn domain knowledge of fault diagnosis based on vibration signals and sound signals. It can also adaptively fuse sound signals and vibration signals. Secondly, a freeze–thaw module is proposed to freeze the pre-trained key layers and thaw the layers that need to be learned to adapt the model to fault diagnosis tasks. Thirdly, the multihead cross-attention module is proposed to fully extract the characteristics of vibration signals and sound signals through modal expansion interaction. The experimental results on the motor dataset and Ottawa dataset show that SVLLM has good performance in feature extraction and machinery fault diagnosis with sound and vibration signals. Full article
(This article belongs to the Special Issue Modeling, Monitoring, and Coordinated Control of Dynamic Processes in Grid-Connected Inverter-Interfaced Energy Systems)
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