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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 2775

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 (6 papers)

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Research

40 pages, 2403 KB  
Article
Mechanism and Simulation Analysis of Resonance De-Icing for 100 m High-Voltage Transmission Line
by Yu Zhang, Yinke Dou, Fujia Liu, Liangliang Zhao, Yangyang Jiao and Huajian Li
Processes 2026, 14(12), 1952; https://doi.org/10.3390/pr14121952 - 15 Jun 2026
Viewed by 115
Abstract
To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line [...] Read more.
To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line as the research model, 49.8 m ice-covered sections are set on both sides of the line, and the 0.4 m range in the middle is designated as the concentrated excitation force area of the vibration motor. By applying intermittent harmonic loads in the excitation stage, the process of mechanical vibration de-icing is accurately reproduced. At the same time, life and death element technology is introduced to remove ice-covered units with stress exceeding the critical failure threshold, accurately realizing the dynamic simulation of the entire process of ice-covering cracking and detachment. This study selects resonance frequency bands that are suitable for the structural characteristics of the transmission line through static analysis, modal analysis, and harmonic response analysis, and preliminarily locks in candidate excitation frequencies. Combined with transient dynamics simulation, the optimal excitation frequency for vibration de-icing of transmission lines is determined by comprehensively considering the efficiency of de-icing and the safety constraints of conductor dancing. A method for determining the optimal de-icing frequency based on multi-step finite element analysis has been developed, which can provide theoretical support and simulation reference for the structural design, frequency matching, and operational parameter optimization of mechanical vibration de-icing devices for high-voltage transmission lines and overhead cables. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
15 pages, 1459 KB  
Article
Adaptive Distance Protection Setting Method Based on Sensitivity Constraints and Disturbance-Domain Model
by Jianbin Ci, You Yu, Tao Li, Zhenting Sun, Jingfu Tian, Ming Dong, Qiang Ma, Shiming Wang and Jingshan Mo
Processes 2026, 14(11), 1752; https://doi.org/10.3390/pr14111752 - 27 May 2026
Viewed by 243
Abstract
With the expansion of transmission networks and the increasing penetration of inverter-based resources (IBRs), fixed offline distance-protection settings face increasing difficulty in balancing selectivity, sensitivity, and operating speed. This problem is particularly evident in Zone III remote-backup protection, where conservative load-avoidance settings may [...] Read more.
With the expansion of transmission networks and the increasing penetration of inverter-based resources (IBRs), fixed offline distance-protection settings face increasing difficulty in balancing selectivity, sensitivity, and operating speed. This problem is particularly evident in Zone III remote-backup protection, where conservative load-avoidance settings may create blind zones. This paper proposes an adaptive three-zone distance-protection setting method based on explicit sensitivity constraints and a disturbance-domain model. The method has two main features. First, online recalculation is restricted to the local disturbance domain affected by topology changes, thereby avoiding network-wide recomputation. Second, Zone II and Zone III settings are determined by a constrained model that incorporates real-time branch coefficients, load impedance, sensitivity requirements, and downstream coordination limits. A fallback mechanism is also included to maintain security under data loss or abnormal measurements. In a 220 kV case study, the proposed method increases the Zone II sensitivity coefficient from 1.92 to 1.95 and the Zone III remote-backup sensitivity coefficient from 0.83 to 1.35. Additional tests under high-resistance faults, measurement errors, volatile load, and inverter-based resource integration show that the method preserves selectivity while reducing backup protection blind zones. The disturbance-domain strategy also reduces the average recalculation time from 820 ms to 18 ms in the tested regional setting-calculation scenario. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
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19 pages, 6417 KB  
Article
Improved Football Team Training Algorithm Based on Modal Decomposition and BiLSTM Method for Short-Term Wind Power Forecasting
by Lingling Xie, Yanjing Luo, Chunhui Li, Long Li and Fengyuan Liu
Processes 2026, 14(6), 951; https://doi.org/10.3390/pr14060951 - 17 Mar 2026
Viewed by 448
Abstract
Reliable wind power forecasting is essential for maintaining the safe and stable operation of power systems with high renewable energy penetration. This study proposes a short-term wind power forecasting model based on decomposition–optimization–prediction, integrating complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), [...] Read more.
Reliable wind power forecasting is essential for maintaining the safe and stable operation of power systems with high renewable energy penetration. This study proposes a short-term wind power forecasting model based on decomposition–optimization–prediction, integrating complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved football team training algorithm (IFTTA), and the bidirectional long short-term memory network model (BiLSTM). CEEMDAN is employed to decompose the non-stationary wind power sequence into relatively stable intrinsic mode functions (IMFs), thereby separating multi-scale fluctuation features. The IFTTA incorporates a dynamic probability allocation strategy and an adaptive parameter adjustment mechanism, which contributes to a better balance between global exploration and local exploitation. After optimizing the hyperparameters of BiLSTM using IFTTA, the prediction performance significantly improved. Validations were conducted on three datasets from Xinjiang, Ningxia, and Inner Mongolia, China, each containing 1440 samples (1152 for training and 288 for testing). Comparisons with the benchmark forecasting model demonstrate that the pro-posed model reduces the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) by at least 25.29%, 29.62%, and 20.66%, respectively. Correspondingly, the coefficient of determination (R2) was improved by at least 0.0069. This model provides an effective solution for short-term wind power prediction in practical engineering. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
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31 pages, 2346 KB  
Article
Research on MPC-Based Power Allocation Strategy and Dynamic Value Evaluation of Wind–Hydrogen Coupled Systems
by Jiyong Li, Chen Ye, Hao Huang, Zhiliang Cheng, Yide Peng and Kaiyue Wang
Processes 2026, 14(6), 924; https://doi.org/10.3390/pr14060924 - 14 Mar 2026
Viewed by 424
Abstract
With rising renewable energy penetration, wind–hydrogen coupling systems are key to large-scale green hydrogen production and wind power integration. This paper proposes a multi-timescale power allocation measure and evaluation framework that executes scheduling planning, rolling updates and real-time control sequentially. First, an intelligent [...] Read more.
With rising renewable energy penetration, wind–hydrogen coupling systems are key to large-scale green hydrogen production and wind power integration. This paper proposes a multi-timescale power allocation measure and evaluation framework that executes scheduling planning, rolling updates and real-time control sequentially. First, an intelligent power allocation strategy based on model predictive control (MPC) and State of Health (SOH) prediction is designed, which pursues short-term operational efficiency while actively avoiding electrolyzer-damaging conditions. Second, a comprehensive evaluation model integrating dynamic hydrogen value and flexibility value is built, overcoming the limitations of traditional fixed-hydrogen-value and single-system-value evaluations to quantify operational strategy viability more accurately. Simulation results show that the proposed strategy boosts the system’s lifecycle Net Present Value (NPV) by ~12.7% versus conventional strategies, verifying the framework’s effectiveness and superiority in improving wind–hydrogen coupling system performance. Full article
(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)
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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 635
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 436
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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