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21 pages, 1320 KB

Open AccessArticle

Enhanced Short-Term Load Forecasting Based on Adaptive Residual Fusion of Autoformer and Transformer

by Lukun Zeng, Kaihong Zheng, Guoying Lin, Jingxu Yang, Mingqi Wu, Guanyu Chen and Haoxia Jiang

Energies 2025, 18(24), 6496; https://doi.org/10.3390/en18246496 - 11 Dec 2025

Abstract

Accurate short-term electricity load forecasting (STELF) is essential for grid scheduling and low-carbon smart grids. However, load exhibits multi-timescale periodicity and non-stationary fluctuations, making STELF highly challenging for existing models. To address this challenge, an Autoformer–Transformer residual fusion network (ATRFN) is proposed in [...] Read more.

Accurate short-term electricity load forecasting (STELF) is essential for grid scheduling and low-carbon smart grids. However, load exhibits multi-timescale periodicity and non-stationary fluctuations, making STELF highly challenging for existing models. To address this challenge, an Autoformer–Transformer residual fusion network (ATRFN) is proposed in this paper. A dynamic weighting mechanism is applied to combine the outputs of Autoformer and Transformer through residual connections. In this way, lightweight result-level fusion is enabled without modifications to either architecture. In experimental validations on real-world load datasets, the proposed ATRFN model achieves notable performance gains over single STELF models. For univariate STELF, the ATRFN model reduces forecasting errors by 11.94% in mean squared error (MSE), 10.51% in mean absolute error (MAE), and 7.99% in mean absolute percentage error (MAPE) compared with the best single model. In multivariate experiments, it further decreases errors by at least 5.22% in MSE, 2.77% in MAE, and 2.85% in MAPE, demonstrating consistent improvements in predictive accuracy. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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18 pages, 1537 KB

Open AccessArticle

Adaptive Visual Servo Control for GIS Partial Discharge Detection Robots: A Model Predictive Control Approach

by Yongchao Luo, Zifan Zhang and Yingxi Xie

Energies 2025, 18(23), 6365; https://doi.org/10.3390/en18236365 - 4 Dec 2025

Viewed by 116

Abstract

Gas-insulated switchgear (GIS) serves as the core equipment in substations. Its partial discharge detection requires ultrasonic sensors to be precisely aligned with millimeter-level measurement points. However, existing technologies face three major bottlenecks: the lack of surface texture on GIS makes visual feature extraction [...] Read more.

Gas-insulated switchgear (GIS) serves as the core equipment in substations. Its partial discharge detection requires ultrasonic sensors to be precisely aligned with millimeter-level measurement points. However, existing technologies face three major bottlenecks: the lack of surface texture on GIS makes visual feature extraction difficult; strong electromagnetic interference in substations causes image noise and loss of feature point tracking; and fixed gain control easily leads to end-effector jitter, reducing positioning accuracy. To address these challenges, this paper first employs AprilTag visual markers to define GIS measurement point features, establishing an image-based visual servo model that integrates GIS surface curvature constraints. Second, it proposes an adaptive gain algorithm based on model predictive control, dynamically adjusting gain in real-time according to visual error, electromagnetic interference intensity, and contact force feedback, balancing convergence speed and motion stability. Finally, experiments conducted on a GIS inspection platform built using a Franka Panda robotic arm demonstrate that the proposed algorithm reduces positioning errors, increases positioning speed, and improves positioning accuracy compared to fixed-gain algorithms, providing technical support for the engineering application of GIS partial discharge detection robots. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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24 pages, 6731 KB

Open AccessArticle

Joint Dispatch Model for Power Grid and Wind Farms Considering Frequency Modulation Delay

by Jiaxing Huo, Yongjun Zhang, Yufei Liu, Wenguang Lin and Yanping Sun

Energies 2025, 18(23), 6263; https://doi.org/10.3390/en18236263 - 28 Nov 2025

Viewed by 148

Abstract

The high proportion of wind power access makes the system frequency regulation face serious challenges, and the time delay of wind turbine FM response exacerbates the frequency security problem. For this reason, this paper proposes a joint dispatch model for power grid and [...] Read more.

The high proportion of wind power access makes the system frequency regulation face serious challenges, and the time delay of wind turbine FM response exacerbates the frequency security problem. For this reason, this paper proposes a joint dispatch model for power grid and wind farms considering frequency modulation delay. First, the wind turbine response characteristics and frequency safety constraints are derived by equivalently modeling the wind turbine FM delay. Second, power grid-wind farm joint dispatch model is constructed on this basis, where the system level optimizes the operation cost under the premise of satisfying the frequency safety constraints, and the wind farm level tracks the wind power output target issued by the system to meet the FM demand. Finally, by the case study, Scenario 1 reduces average frequency nadir deviation from 0.205 Hz to 0.098 Hz and RoCoF from 0.216 Hz/s to 0.168 Hz/s in the IEEE-39 system. The stability of the system is enhanced, which verifies the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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19 pages, 2104 KB

Open AccessArticle

DecPD: A Deconstructed Deep Learning Approach for Partial Discharge Pattern Recognition

by Yucheng Wu, Hao Yang, Shengwei Li and Fanghong Guo

Energies 2025, 18(23), 6245; https://doi.org/10.3390/en18236245 - 28 Nov 2025

Viewed by 311

Abstract

Recently, partial discharge pattern recognition (PDPR) for transmission cables has garnered increasing attention due to the severe power outages, equipment damage, and even major safety incidents resulting from the failure of partial discharge (PD) detection. However, existing PD data samples usually suffer from [...] Read more.

Recently, partial discharge pattern recognition (PDPR) for transmission cables has garnered increasing attention due to the severe power outages, equipment damage, and even major safety incidents resulting from the failure of partial discharge (PD) detection. However, existing PD data samples usually suffer from highly similar features and unbalanced distribution. Determining how to precisely realize the PDPR has become a challenge. In this study, an effective PDPR approach is proposed based on a newly designed deconstructed PD (DecPD) model and a customized loss function for PDPR. Notably, the refined deep learning network captures the discriminative features in both temporal and spatial dimensions through a dual-channel learning architecture. Additionally, an adaptive focal loss function is designed, which introduces a peak factor to establish focusing parameters for PDPR, thereby addressing the class imbalance issues. A comprehensive experimental evaluation using real datasets generated on a physical platform is conducted to verify our proposed method. Compared to other existing methods, our DecPD approach demonstrates superior performance, achieving an overall accuracy of 96.65% in the presence of environment noise. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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24 pages, 1366 KB

Open AccessArticle

Short-Term Residential Load Forecasting Based on Generative Diffusion Models and Attention Mechanisms

by Yitao Zhao, Jiahao Li, Chuanxu Chen and Quansheng Guan

Energies 2025, 18(23), 6208; https://doi.org/10.3390/en18236208 - 27 Nov 2025

Viewed by 301

Abstract

Accurate short-term prediction of residential power consumption is imperative for efficient energy system management. However, the complexity of high-resolution load data, nonlinear dynamics of load fluctuation, and external factor interactions pose challenges to traditional load forecasting methods. This work introduces a diffusion model-based [...] Read more.

Accurate short-term prediction of residential power consumption is imperative for efficient energy system management. However, the complexity of high-resolution load data, nonlinear dynamics of load fluctuation, and external factor interactions pose challenges to traditional load forecasting methods. This work introduces a diffusion model-based and attention mechanism-enhanced temporal forecasting framework to address the volatility and uncertainty in load patterns. The proposed model enhances the noise robustness via diffusion processes, captures multi-scale temporal features through temporal convolutional networks, and adaptively focuses on critical time steps using attention mechanisms. Further, a dynamically weighted loss function is designed to improve both the prediction accuracy and latent representation quality. Experiments on multiple real-world residential load datasets show that the proposed model always outperforms benchmarks, reducing on average the mean absolute error (MAE) by 47.4%, symmetric mean absolute percentage error (SMAPE) by 39.7%, and mean absolute percentage error (MAPE) by 57.6%. It also achieves the superior root mean square error (RMSE) and Pearson correlation coefficient (PCC) performance, validating its effectiveness for high-resolution and multi-modal load forecasting. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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24 pages, 1698 KB

Open AccessArticle

Deep Learning-Based Classification of Transformer Inrush and Fault Currents Using a Hybrid Self-Organizing Map and CNN Model

by Heungseok Lee, Sang-Hee Kang and Soon-Ryul Nam

Energies 2025, 18(20), 5351; https://doi.org/10.3390/en18205351 - 11 Oct 2025

Viewed by 484

Abstract

Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a [...] Read more.

Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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16 pages, 2181 KB

Open AccessArticle

A Hybrid Deep Learning and PINN Approach for Fault Detection and Classification in HVAC Transmission Systems

by Mohammed Almutairi and Wonsuk Ko

Energies 2025, 18(18), 4796; https://doi.org/10.3390/en18184796 - 9 Sep 2025

Viewed by 1460

Abstract

High-Voltage Alternating Current (HVAC) transmission systems form the backbone of modern power grids, enabling efficient long-distance and high-capacity power delivery. In Saudi Arabia, ongoing initiatives to modernize and strengthen grid infrastructure demand advanced solutions to ensure system reliability, operational stability, and the minimization [...] Read more.

High-Voltage Alternating Current (HVAC) transmission systems form the backbone of modern power grids, enabling efficient long-distance and high-capacity power delivery. In Saudi Arabia, ongoing initiatives to modernize and strengthen grid infrastructure demand advanced solutions to ensure system reliability, operational stability, and the minimization of economic losses caused by faults. Traditional fault detection and classification methods often depend on the manual interpretation of voltage and current signals, which is both labor-intensive and prone to human error. Although data-driven approaches such as Artificial Neural Networks (ANNs) and Deep Learning have been applied to automate fault analysis, their performance is often constrained by the quality and size of available training datasets, leading to poor generalization and physically inconsistent outcomes. This study proposes a novel hybrid fault detection and classification framework for the 380 kV Marjan–Safaniyah HVAC transmission line by integrating Deep Learning with Physics-Informed Neural Networks (PINNs). The PINN model embeds fundamental electrical laws, such as Kirchhoff’s Current Law (KCL), directly into the learning process, thereby constraining predictions to physically plausible behaviors and enhancing robustness and accuracy. Developed in MATLAB/Simulink using the Deep Learning Toolbox, the proposed framework performs fault detection and fault type classification within a unified architecture. A comparative analysis demonstrates that the hybrid PINN approach significantly outperforms conventional Deep Learning models, particularly by reducing false negatives and improving class discrimination. Furthermore, this study highlights the crucial role of balanced and representative datasets in achieving a reliable performance. Validation through confusion matrices and KCL residual histograms confirms the enhanced physical consistency and predictive reliability of the model. Overall, the proposed framework provides a powerful and scalable solution for real-time monitoring, fault diagnosis, and intelligent decision-making in high-voltage power transmission systems. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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18 pages, 1797 KB

Open AccessArticle

Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations

by Dong Liu, Guodong Guo, Zhidong Wang, Fan Li, Kaiyuan Jia, Chenzhenghan Zhu, Haotian Wang and Yingyun Sun

Energies 2025, 18(14), 3838; https://doi.org/10.3390/en18143838 - 18 Jul 2025

Viewed by 609

Abstract

In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency [...] Read more.

In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)

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