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19 pages, 1710 KB

Open AccessArticle

Research on Comprehensive Evaluation Model of Virtual Power Plant Operational Benefits Based on DEMATEL-CRITIC-EDAS

by Ranran Li, Hecheng Yuan, Jianing Zhang, Qiushuang Li, Jiarui Li, Wanying Li and Zhengsen Ji

Processes 2026, 14(10), 1545; https://doi.org/10.3390/pr14101545 - 11 May 2026

Viewed by 397

Abstract

Different types of Virtual Power Plants (VPPs) play distinct roles within power systems. To scientifically evaluate the operational benefits of VPPs, this paper constructs a comprehensive evaluation framework based on combined weighting and the Evaluation based on Distance from Average Solution (EDAS) method. [...] Read more.

Different types of Virtual Power Plants (VPPs) play distinct roles within power systems. To scientifically evaluate the operational benefits of VPPs, this paper constructs a comprehensive evaluation framework based on combined weighting and the Evaluation based on Distance from Average Solution (EDAS) method. First, an evaluation index system is established encompassing four dimensions: economic, environmental, social, and technical. Subsequently, a hybrid model integrating DEMATEL, CRITIC, Game Theory, and EDAS is proposed. Specifically, the DEMATEL method is employed to analyze the causal relationships among indicators and determine subjective weights, while the CRITIC method is used to calculate objective weights. Game Theory is then applied to optimize the combination of weights, and the EDAS method is utilized to rank the alternatives. Empirical analysis of five VPP scenarios indicates that the renewable energy accommodation rate and hardware investment costs are the core driving factors affecting operational benefits. Specifically, the renewable-energy accommodation rate exhibits the highest combined weight of 0.08, and the hardware investment cost reaches 0.07. Among the scenarios, a wind-solar-storage hybrid VPP demonstrates the optimal comprehensive performance. The results are consistent with comparative methods such as TOPSIS, verifying the reliability of the proposed framework and providing a scientific reference for VPP investment decision-making. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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25 pages, 1905 KB

Open AccessArticle

Decision-Making for Secure and Stable Operation of Power Systems: A Multi-Scenario-Based Optimization Model

by Liang Guo, Ziping Peng, Junjie Zhang, Yi Zheng and Shufang Zhou

Processes 2026, 14(9), 1488; https://doi.org/10.3390/pr14091488 - 5 May 2026

Viewed by 449

Abstract

In practical power system operation scenarios, extreme natural weather conditions and fluctuations at both the supply and demand sides pose significant challenges to the stable operation and the formulation of operational decision-making for power systems. Particularly in extreme scenarios involving faults, it may [...] Read more.

In practical power system operation scenarios, extreme natural weather conditions and fluctuations at both the supply and demand sides pose significant challenges to the stable operation and the formulation of operational decision-making for power systems. Particularly in extreme scenarios involving faults, it may lead to power supply–demand imbalances and instability in the power system. To address this issue, this paper proposes a decision-making approach for the secure and stable operation of power systems using a multi-scenario-based optimization model. Initially, a joint scenario set is generated using historical operational data to accurately depict multiple complex scenarios. Building on this, a multi-scenario-based optimization model is constructed, with responses facilitated by flexible adjustment resources within the system. Considering the non-convex and nonlinear characteristics of the model, an improved Harris Hawks Optimization (HHO) algorithm is employed to search for the global optimal solution. Finally, a modified IEEE-33 bus test system is utilized to demonstrate the feasibility and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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25 pages, 4245 KB

Open AccessArticle

Comprehensive Early Alert and Adaptive Local Response Framework for Wildfire Risk in Transmission Line Corridors Using Coupled Global Factors in Power System

by Tianliang Xue, Chengsi Xiang, Xi Chen and Lei Zhang

Processes 2026, 14(5), 752; https://doi.org/10.3390/pr14050752 - 25 Feb 2026

Viewed by 497

Abstract

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay [...] Read more.

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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16 pages, 3345 KB

Open AccessArticle

A Lightweight Model for Insulator Defect Detection Based on Vision–Language Modeling and Prior Knowledge in Power Systems

by Shanfeng Liu, Weijian Zhang, Shaoguang Yuan, Hua Bao, Wandeng Mao and Shengzhe Xi

Processes 2025, 13(11), 3714; https://doi.org/10.3390/pr13113714 - 17 Nov 2025

Cited by 3 | Viewed by 1696

Abstract

Insulators serve as critical insulating components in power transmission lines, and their defects are one of the primary causes of power outages in power grids. Power companies widely utilize unmanned aerial vehicle (UAV) inspections to collect image data of power transmission lines. However, [...] Read more.

Insulators serve as critical insulating components in power transmission lines, and their defects are one of the primary causes of power outages in power grids. Power companies widely utilize unmanned aerial vehicle (UAV) inspections to collect image data of power transmission lines. However, existing methods face two core challenges: at the data level, insulator defect samples are extremely scarce in massive image datasets, leading to severe data imbalance issues. At the algorithm level, deep learning-based defect detection methods rely on data-driven feature extraction, ignoring quantifiable prior knowledge such as insulator installation specifications and mechanical structure. This factor results in low localization efficiency and poor robustness in complex scenarios. To address these issues, this paper proposes an insulator defect detection method based on Vision–Language models and prior knowledge. It extracts prior knowledge about the physical characteristics of insulators, quantifies spatial structure and installation specifications as prior constraints, embeds prior knowledge into the vision–language model’s feature space to generate insulator defect samples, addresses the data imbalance issue, and detects insulator defects using an improved You Only Look Once (YOLO) algorithm. This approach reduces model parameters while maintaining detection accuracy, constructing a lightweight model for insulator defect detection. The experimental results show that, compared with PP-YOLOE-m and RT-DETR-R18 models, the method proposed in this paper can significantly improve the detection accuracy. The mean average precision indicator of the model in this paper has reached 95.7%. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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30 pages, 8022 KB

Open AccessArticle

Intelligent ANN-Based Controller for Decentralized Power Grids’ Load Frequency Control

by Rambaboo Singh, Ramesh Kumar, Ravi Shankar and Rakesh Kumar Singh

Processes 2025, 13(11), 3562; https://doi.org/10.3390/pr13113562 - 5 Nov 2025

Cited by 1 | Viewed by 907

Abstract

In this study, the authors demonstrate the development and evaluation of an optimal frequency control controller for an interlinked two-area power system that incorporates Renewable Energy Sources (RESs). In decentralized power grids, the Load Frequency Control (LFC) system allows scheduled tie-line power as [...] Read more.

In this study, the authors demonstrate the development and evaluation of an optimal frequency control controller for an interlinked two-area power system that incorporates Renewable Energy Sources (RESs). In decentralized power grids, the Load Frequency Control (LFC) system allows scheduled tie-line power as well as system frequency to be reimposed to their nominal values. Designing an advanced controller might enhance the functionality of the LFC mechanism. This article illustrates the possible impacts of converter capacitors using the new High-Voltage Direct Current (HVDC) tie-line model as well as the Inertia Emulation Technique (IET). This paper suggests a new adaptive control procedure for the expected LFC mechanism: an ANN-based (PI^λ + PI^λf) controller. The authors evaluate which control parameters are most effective using a modified version of the Quasi-Opposition-learning-based Reptile Search Algorithm (QORSA) method. Software called MATLAB/Simulink-2015 is used to create this arrangement. The use of established techniques for handling step as well as random load disturbances has enabled an evaluation of the suggested LFC architecture’s efficacy. An IET-based HVDC tie-line reduces overshoot by 100% in Areas 1 and 2 (Area 1 frequency deviation, i.e., ∆f₁, as well as Area 2 frequency deviation, i.e., ∆f₂). When considering SLD, the suggested controller outperforms the most widely used alternative settings. The IEEE-39 bus system has been changed by the addition of RESs. The IEEE-39 bus system is composed of three control areas. It is confirmed how the IEEE-39 bus system reacts to changes in frequency in Areas 1, 2, and 3. It is illustrated how to use the suggested controller in the modified IEEE-39 bus system, accompanied by real-time load variations. Recent research indicates that the suggested control method is better and more efficient due to its 100% decrease in overshoot in Areas 1 and 2 and quick response time. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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14 pages, 1462 KB

Open AccessArticle

Estimation of Primary Current of DC Bias Current Transformer in Power System Based on LSTM

by Zhanyi Ren, Kanyuan Yu, Guangbo Chen, Yunxiao Yang, Yizhao Cheng and Li Zhang

Processes 2025, 13(11), 3391; https://doi.org/10.3390/pr13113391 - 23 Oct 2025

Viewed by 937

Abstract

In high-voltage transmission systems, current transformers (CTs) are susceptible to core saturation when subjected to DC bias, which leads to severe distortion of the secondary output current, affecting protection and metering accuracy. In order to solve this problem, this paper proposes a CT [...] Read more.

In high-voltage transmission systems, current transformers (CTs) are susceptible to core saturation when subjected to DC bias, which leads to severe distortion of the secondary output current, affecting protection and metering accuracy. In order to solve this problem, this paper proposes a CT distortion current inverse derivation method based on long short-term memory (LSTM) neural network. The method takes the secondary current (referred to the primary side) as input, models its temporal structure with an LSTM, and outputs the corresponding estimate of the true primary current. First, the simulation model considering DC bias effect is built in PSCAD to generate training samples; then, the LSTM network structure is designed, and finally, the training samples are trained and the inverse derivation results are generated. Simulation results demonstrate accurate recovery of the primary current across saturation levels and DC-bias magnitudes, reducing RMSE by 66.29% and 79.54%, and MAE by 44.12% and 72.06%, relative to an SVM and a shallow ANN, respectively, indicating strong generalization and robustness. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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21 pages, 4746 KB

Open AccessArticle

Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer

by Xinyu Luo, Zhaofan Zhou, Bin Li, Yumeng Zhang, Chenle Yi, Kun Shi and Songsong Chen

Processes 2025, 13(10), 3265; https://doi.org/10.3390/pr13103265 - 13 Oct 2025

Cited by 1 | Viewed by 924

Abstract

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible [...] Read more.

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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13 pages, 381 KB

Open AccessArticle

A Novel Electric Load Prediction Method Based on Minimum-Variance Self-Tuning Approach

by Sijia Liu, Ziyi Yuan, Qi An and Bo Zhao

Processes 2025, 13(8), 2599; https://doi.org/10.3390/pr13082599 - 17 Aug 2025

Viewed by 933

Abstract

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a [...] Read more.

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a novel minimum-variance self-tuning (MVST) method is proposed, grounded in adaptive control theory, to overcome these challenges. The method utilizes recursive least squares with self-tuning parameter updates, delivering high prediction accuracy, rapid computation, and robust multi-step forecasting without pre-training requirements. Testing is performed on CO₂ emissions (annual), transformer load (15 min), and building electric load (hourly) datasets, comparing MVST against LSTM, ARDL, fixed-PID, XGBoost, and Prophet across varied scales and contexts. Significant improvements are observed, with prediction errors reduced by 3–8 times and computational time decreased by up to 2000 times compared to these methods. Finally, these advancements facilitate real-time power system dispatch, enhance energy planning, and support carbon emission management, demonstrating substantial research and practical value. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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12 pages, 610 KB

Open AccessArticle

High-Accuracy Harmonic Source Localization in Transmission Networks Using Voltage Difference Features and Random Forest

by Sijia Liu, Pengchao Lei and Bo Zhao

Processes 2025, 13(8), 2579; https://doi.org/10.3390/pr13082579 - 15 Aug 2025

Viewed by 1013

Abstract

This paper proposes a harmonic source localization method for power systems, combining voltage difference features with a random forest classifier. The method captures harmonic propagation patterns and optimizes network topology handling to ensure accurate and efficient identification across various configurations. Validated on IEEE [...] Read more.

This paper proposes a harmonic source localization method for power systems, combining voltage difference features with a random forest classifier. The method captures harmonic propagation patterns and optimizes network topology handling to ensure accurate and efficient identification across various configurations. Validated on IEEE standard transmission networks, it achieves high accuracy and scalability. While effective in transmission systems, distribution networks pose challenges due to complex topologies and high impedance. Future enhancements will focus on advanced feature engineering, data augmentation, and real-time processing to improve adaptability in diverse power system environments. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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21 pages, 964 KB

Open AccessArticle

A Data-Driven Strategy Assisted by Effective Parameter Optimization for Cable Fault Diagnosis in the Secondary Circuit of a Substation

by Dongbin Yu, Yanjing Zhang, Sijin Luo, Wei Zou, Junting Liu, Zhiyong Ran and Wei Liu

Processes 2025, 13(8), 2407; https://doi.org/10.3390/pr13082407 - 29 Jul 2025

Cited by 2 | Viewed by 953

Abstract

As power systems evolve rapidly, cables, essential for electric power transmission, demand accurate and timely fault diagnosis to ensure grid safety and stability. However, current cable fault diagnosis technologies often struggle with incomplete feature extraction from complex fault signals and inefficient parameter tuning [...] Read more.

As power systems evolve rapidly, cables, essential for electric power transmission, demand accurate and timely fault diagnosis to ensure grid safety and stability. However, current cable fault diagnosis technologies often struggle with incomplete feature extraction from complex fault signals and inefficient parameter tuning in diagnostic models, hindering efficient and precise fault detection in modern power systems. To address these, this paper proposes a data-driven strategy for cable fault diagnosis in substation secondary circuits, enhanced by effective parameter optimization. Initially, wavelet packet decomposition is employed to finely divide collected cable fault current signals into multiple levels and bands, effectively extracting fault feature vectors. To tackle the challenge of selecting penalty and kernel parameters in Support Vector Machine (SVM) models, an improved Golden Jackal Optimization (GJO) algorithm is introduced. This algorithm simulates the predatory behavior of golden jackals in nature, enabling efficient global optimization of SVM parameters and significantly improving the classification accuracy and generalization capability of the fault diagnosis model. Simulation verification using real cable fault cases confirms that the proposed method outperforms traditional techniques in fault recognition accuracy, diagnostic speed, and robustness, proving its effectiveness and feasibility. This study offers a novel and efficient solution for cable fault diagnosis. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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17 pages, 2878 KB

Open AccessArticle

Research on Mechanical Fault Diagnosis Method of Isolation Switch Based on Variational Autoencoder

by Shun He, Fangrong Zhou, Xiangyu Tan, Guangfu Hu, Jiangjun Ruan and Song He

Processes 2025, 13(8), 2388; https://doi.org/10.3390/pr13082388 - 27 Jul 2025

Cited by 2 | Viewed by 1336

Abstract

This study presents a Variational Autoencoder (VAE)-based framework for the unsupervised mechanical fault diagnosis of high-voltage isolation switches. By analyzing voltage and current signals to compute instantaneous power sequences, the method detects anomalies through reconstruction errors. Experimental results from both laboratory and real-world [...] Read more.

This study presents a Variational Autoencoder (VAE)-based framework for the unsupervised mechanical fault diagnosis of high-voltage isolation switches. By analyzing voltage and current signals to compute instantaneous power sequences, the method detects anomalies through reconstruction errors. Experimental results from both laboratory and real-world datasets demonstrate that the VAE model outperforms traditional methods, offering high diagnostic accuracy and robustness without the need for labeled data or manual feature extraction. This approach provides an effective solution for the real-time monitoring and predictive maintenance of high-voltage switchgear. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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25 pages, 3447 KB

Open AccessArticle

Research on Transformer Fault Diagnosis and Maintenance Strategy Generation Based on TransQwen Model

by Zichun Xue, Bo Wang, Hengrui Ma, Jiaxin Zhang, Hanqi Zhang and Jinhui Zhou

Processes 2025, 13(7), 1977; https://doi.org/10.3390/pr13071977 - 23 Jun 2025

Cited by 3 | Viewed by 1854

Abstract

Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate [...] Read more.

Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate status evaluation and proactive risk management. This paper proposes TransQwen, a domain-adapted LLM tailored for transformer fault diagnosis and maintenance strategy generation. Built upon the Qwen-7B-Chat architecture, TransQwen is fine-tuned on a domain-specific corpus encompassing transformer fault cases aligned with technical standards and operational procedures. It integrates DoRA for efficient parameter adaptation and RoPE to enhance positional encoding during training. The model is evaluated in three core tasks: fault type classification, fault severity grading, and strategy generation. The results show significant improvements—over 10 percentage point gains in standard conditions and up to 30 percentage points in F1 score under extreme low-sample settings (e.g., 100 samples), demonstrating robust generalization. In the maintenance strategy generation experiment, all the evaluation results of the TransQwen model reached the optimal. Through a knowledge-driven approach, the model can perform question-and-answer tasks involving professional knowledge in the power vertical field, and customize and generate accurate maintenance strategies for specific fault scenarios. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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19 pages, 4706 KB

Open AccessArticle

Load Restoration Based on Improved Girvan–Newman and QTRAN-Alt in Distribution Networks

by Chao Zhang, Qiao Sun, Jiakai Huang, Shiqian Ma, Yan Wang, Hao Chen, Hanning Mi, Jiuxiang Chen and Tianlu Gao

Processes 2025, 13(5), 1473; https://doi.org/10.3390/pr13051473 - 12 May 2025

Cited by 1 | Viewed by 1287

Abstract

With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent [...] Read more.

With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent reinforcement learning (MARL) framework enhanced by distribution network partitioning to address this challenge. Firstly, an improved Girvan–Newman algorithm is employed to achieve balanced partitioning of the network, defining the state space of each agent and action boundaries within the multi-agent system (MAS). Subsequently, a counterfactual reasoning framework solved by the QTRAN-alt algorithm is incorporated to refine action selection during training, thereby accelerating convergence and enhancing decision-making efficiency during execution. Experimental validation using a 27-bus system and a 70-bus system demonstrates that the proposed QTRAN-alt with the Girvan–Newman method achieves fast convergence and high returns compared to typical MARL approaches. Furthermore, the proposed methodology significantly improves the success rate of full system restoration without violating constraints. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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21 pages, 2951 KB

Open AccessArticle

Research on Power Quality Control Methods for Active Distribution Networks with Large-Scale Renewable Energy Integration

by Yongsheng Wang, Yaxuan Guo, Haibo Ning, Peng Li, Baoyi Cen, Hongwei Zhao and Hongbo Zou

Processes 2025, 13(5), 1469; https://doi.org/10.3390/pr13051469 - 12 May 2025

Cited by 3 | Viewed by 1504

Abstract

With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues [...] Read more.

With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues such as excessive or insufficient voltage amplitudes. To effectively address this problem, this paper proposes a multi-resource coordinated dynamic reactive power–voltage coordination optimization method. Firstly, an improved Generative Convolutional Adversarial Network (GCAN) is used to generate typical wind and solar power output scenarios. Based on these generated typical scenarios, a voltage control model for ADNs is established with the objective of minimizing voltage fluctuations, fully exploiting the dynamic reactive power regulation resources within the ADN. In view of the non-convex and nonlinear characteristics of the model, an improved Gray Wolf Optimizer (GWO) algorithm is employed for model optimization and solution seeking. Finally, the effectiveness and feasibility of the proposed method are demonstrated through simulations using modified IEEE-33-bus and IEEE-69-bus test systems. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

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