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Processes
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Adaptive Control and Optimization in Power Grids
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Adaptive Control and Optimization in Power Grids

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 712

Special Issue Editors

Dr. Xiaoping Xiong

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: optimal power system operation; cyber–physics system; distribution network communication systems; electricity market
Dr. Fanrong Wei

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: power system security; power system operation and planning; power system analysis and computation
Dr. Lingling Xie

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: DC-DC converter; DC-AC inverter; nonlinear characteristics analysis and control

Special Issue Information

Dear Colleagues,

The increasing penetration of renewable energy sources and the growing complexity of modern power grids have created unprecedented challenges for ensuring stability, reliability, and efficiency. Fluctuating demand, variable renewable generation, and the integration of distributed energy resources necessitate advanced control and optimization strategies that can adapt in real-time. Adaptive control methods, combined with optimization frameworks, are becoming essential tools to guarantee secure, efficient, and sustainable grid operation under uncertainty.

This Special Issue, titled “Adaptive Control and Optimization in Power Grids,” aims to highlight novel advances in methodologies, algorithms, and applications that enhance the resilience, sustainability, and intelligence of power systems. We welcome contributions that address theoretical developments, computational techniques, simulation studies, and real-world implementations.

Topics of interest include, but are not limited to:

  • Adaptive and predictive control (e.g., Model Predictive Control) for frequency and voltage regulation;
  • Robust and stochastic optimization methods for power system operations;
  • Data-driven and machine learning approaches for adaptive grid control;
  • Short-term and probabilistic forecasting of load and renewable generation for control applications;
  • Coordination and optimization of demand response, energy storage, and distributed energy resources (DERs);
  • Digital twin frameworks, hardware-in-the-loop (HIL), and cyber-physical testbeds for validation;
  • Cybersecurity and resilience strategies for adaptive control and optimization in smart grids.

Dr. Xiaoping Xiong
Dr. Fanrong Wei
Dr. Lingling Xie
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-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 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

  • adaptive control
  • power system optimization
  • robust control
  • forecasting of load and renewable generation
  • renewable energy integration
  • grid resilience

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Published Papers (2 papers)

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Research

22 pages, 6763 KB  
Article
Smart Protection Relay for Power Transformers Using Time-Domain Feature Recognition
by Hengchu Shi, Hao You, Xiaofan Chen, Ruisi Li, Shoudong Xu, Jianqiao Zhang and Ruiwen He
Processes 2026, 14(3), 449; https://doi.org/10.3390/pr14030449 - 27 Jan 2026
Viewed by 254
Abstract
Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is [...] Read more.
Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
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17 pages, 1888 KB  
Article
Wind Power Prediction for Extreme Meteorological Conditions Based on SSA-TCN-GCNN and Inverse Adaptive Transfer Learning
by Jiale Liu, Weisi Deng, Weidong Gao, Haohuai Wang, Chonghao Li and Yan Chen
Processes 2026, 14(2), 353; https://doi.org/10.3390/pr14020353 - 19 Jan 2026
Viewed by 221
Abstract
Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, [...] Read more.
Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R2) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
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