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学者姓名：江灏
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Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry. Owing to economic and security concerns, a common method is to train data generated by simulators. However, achieving a satisfactory performance in practical applications is difficult because simulators imperfectly emulate reality. To bridge this gap, we propose a novel framework called simulation-to-reality domain adaptation (SRDA) for forecasting the operating parameters of nuclear reactors. The SRDA model employs a transformer-based feature extractor to capture dynamic characteristics and temporal dependencies. A parameter predictor with an improved logarithmic loss function is specifically designed to adapt to varying reactor powers. To fuse prior reactor knowledge from simulations with reality, the domain discriminator utilizes an adversarial strategy to ensure the learning of deep domain-invariant features, and the multiple kernel maximum mean discrepancy minimizes their discrepancies. Experiments on neutron fluxes and temperatures from a pressurized water reactor illustrate that the SRDA model surpasses various advanced methods in terms of predictive performance. This study is the first to use domain adaptation for real-world reactor prediction and presents a feasible solution for enhancing the transferability and generalizability of simulated data.
Keyword ：
Domain adaptation Domain adaptation Forecasting Forecasting Knowledge transfer Knowledge transfer Nuclear power plant (NPP) Nuclear power plant (NPP) Pressurized water reactor (PWR) Pressurized water reactor (PWR) Transformer Transformer
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| GB/T 7714 | Lin, Wei-Qing , Miao, Xi-Ren , Chen, Jing et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation [J]. | NUCLEAR SCIENCE AND TECHNIQUES , 2025 , 36 (5) . |
| MLA | Lin, Wei-Qing et al. "Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation" . | NUCLEAR SCIENCE AND TECHNIQUES 36 . 5 (2025) . |
| APA | Lin, Wei-Qing , Miao, Xi-Ren , Chen, Jing , Ye, Ming-Xin , Xu, Yong , Jiang, Hao et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation . | NUCLEAR SCIENCE AND TECHNIQUES , 2025 , 36 (5) . |
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The dampers absorb transmission line vibration energy, reducing the vibration amplitudes of conductors. However, dampers may develop internal structural anomalies (e.g., damage to damper heads) or external positional anomalies (e.g., slippage along the conductor), both of which compromise vibration suppression efficacy. Existing anomaly detection methods focus on single anomaly type and struggle with local feature extraction. To address these limitations, this paper introduces SKAD, a unified framework guided by structural knowledge, to concurrently detect internal and external damper anomalies. SKAD encodes structural properties of dampers through four key structural points, enabling sub-pixel-level localization via a hybrid network (HRNet + GAU + SimCC). By analyzing spatial relationships and vector features of these structural points, SKAD can simultaneously detect anomalies like damage (via confidence thresholds and vector dot products) and slippage (via depth-parallelism-distance constraints) at the structural level. Experiments on a real-world dataset demonstrate SKAD outperforms object-based methods in accuracy and robustness, providing novel transmission line inspection perspectives, ensuring early anomaly detection to prevent conductor fatigue and power outages. © 2025 IEEE.
Keyword ：
Fracture mechanics Fracture mechanics Health risks Health risks
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| GB/T 7714 | Shi, Jiahao , Chen, Jing , Jiang, Hao et al. SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines [J]. | IEEE Transactions on Power Delivery , 2025 , 40 (3) : 1743-1753 . |
| MLA | Shi, Jiahao et al. "SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines" . | IEEE Transactions on Power Delivery 40 . 3 (2025) : 1743-1753 . |
| APA | Shi, Jiahao , Chen, Jing , Jiang, Hao , Miao, Xiren , Yang, Lin . SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines . | IEEE Transactions on Power Delivery , 2025 , 40 (3) , 1743-1753 . |
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The substation plays an essential role in the security and reliability of the power supply of the grid as it serves as the energy conversion hub of the entire power system. To address the challenges posed by complex on-site environments, low detection accuracy, and excessive resource consumption in existing deep learning models, we propose a lightweight substation safety inspection system designed for front-end devices. The system consists of software and hardware modules. The software module utilizes weighted bidirectional feature pyramid network, attention mechanism, and pruning-quantization-distillation operations to improve and lightweight the YOLOXs (you only look once version-xs) model, effectively compressing the model size while maintaining accuracy. The hardware module mainly achieves quantization compilation and hardware acceleration of the lightweight YOLOXs detection model on the FPGA frontend device, enabling low-latency, high-precision real-time detection for on-site operations at substations. In Experiment, the improved YOLOXs model shows an average detection accuracy increase of 2.71% compared to the original model, with a reduction in model size of 86.9% after light weighting. The FPGA front-end device achieves a single-image detection time of 87.33 ms, which satisfies the practical engineering requirements for substation safety inspection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Distillation equipment Distillation equipment Electric power system security Electric power system security Electric power transmission networks Electric power transmission networks Electric substations Electric substations Error correction Error correction Inspection equipment Inspection equipment Requirements engineering Requirements engineering Safety testing Safety testing
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| GB/T 7714 | He, Hao , Jiang, Hao , Liu, Jiawei et al. Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices [C] . 2025 : 374-385 . |
| MLA | He, Hao et al. "Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices" . (2025) : 374-385 . |
| APA | He, Hao , Jiang, Hao , Liu, Jiawei , Chen, Jing , Miao, Xiren , Liu, Xinyu et al. Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices . (2025) : 374-385 . |
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Aiming at the problem that the current deep learning network model for substation meter detection has too many parameters and is difficult to be deployed in mobile devices and embedded devices with limited computing resources, we propose a lightweight substation meter detection algorithm with improved YOLOv5. Based on the YOLOv5 network, the improved algorithm introduces the SE fusion attention mechanism module, and adaptively learns the relationship between feature channels to improve the model’s ability to extract important features from the instrument. Meanwhile, TensorRT technology is used to reconstruct and optimize the improved model, which can reduce the number of model parameters, improve the detection speed and ensure the accuracy of the model detection. Experimental results demonstrate that compared with YOLOv5 on the embedded device Jetson Nano, the improved algorithm proposed in this paper presents significant advantages, which increase by 1.5% and 2.3% respectively on mAP@.5 and mAP@.5:.95, and the detection frame per second increases by 130%, reaching 23FPS. It can realize real-time instrument detection in substation scene, and has practical application significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Electric substations Electric substations Instrument testing Instrument testing
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| GB/T 7714 | Liu, Xian , Jiang, Hao , Zhang, Minggui et al. Research on Lightweight Substation Instrument Detection Model for Front-End Equipment [C] . 2025 : 364-373 . |
| MLA | Liu, Xian et al. "Research on Lightweight Substation Instrument Detection Model for Front-End Equipment" . (2025) : 364-373 . |
| APA | Liu, Xian , Jiang, Hao , Zhang, Minggui , Miao, Xiren , Liu, Xinyu , Chen, Jing . Research on Lightweight Substation Instrument Detection Model for Front-End Equipment . (2025) : 364-373 . |
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PurposeMulti-unmanned aerial vehicle (UAV) missions aim to optimize the execution of multiple missions using limited resources, making it possible to balance the objectives of each mission while minimizing the time to completion.Design/methodology/approachAn algorithm combining cluster analysis and differential evolution particle swarm optimization (DE-PSO) is proposed to solve this problem.FindingsThe investigative study is based on the homogenization of multi-UAV missions in multi-objective task distribution to reduce the total elapsed time.Practical implicationsThis method effectively reduces task time and provides a solution for multi-UAV operations in transmission line cooperation.Originality/valueA novel heuristic algorithm is proposed, and the algorithm fully considers the clustering characteristics under multi-region and the positional relationship characteristics of scene target distribution. It also fully considers the physical characteristics of airport location and UAV power to uniformly optimize the time.
Keyword ：
Collaborative work Collaborative work DE-PSO algorithm DE-PSO algorithm Difference and variation Difference and variation Multi-UAV Multi-UAV
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| GB/T 7714 | Jiang, Hao , Lin, Sicheng , Chen, Jing et al. Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line [J]. | ENGINEERING COMPUTATIONS , 2025 , 42 (4) : 1447-1470 . |
| MLA | Jiang, Hao et al. "Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line" . | ENGINEERING COMPUTATIONS 42 . 4 (2025) : 1447-1470 . |
| APA | Jiang, Hao , Lin, Sicheng , Chen, Jing , Miao, Xiren . Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line . | ENGINEERING COMPUTATIONS , 2025 , 42 (4) , 1447-1470 . |
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To meet the application requirements of flexible power flow control in local power systems across voltage levels, the PFCT circuit topology and its power flow control strategy are studied. A PFCT circuit topology and its equivalent model are proposed. The mathematical modeling for the series and parallel components of the PFCT are established, along with corresponding control strategies. Flexible control of power flow transmission along the line is accomplished by cross-decoupling power flow control and feedforward decoupling control. A simulation model was built by using the MATLAB/Simulink platform to analyze and validate the PFCT's control of power flow transmission across voltage levels in a typical local power system. A prototype was constructed to validate further the correctness and feasibility of the proposed mathematical models and control strategies. © 2025 Institute of Physics Publishing. All rights reserved.
Keyword ：
Electric power system control Electric power system control Electric power transmission Electric power transmission Mathematical morphology Mathematical morphology Topology Topology
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| GB/T 7714 | Jiang, Hao , Yi, Yang , Zhu, Lin et al. Power flow control of local power system with power flow controllable transformer [C] . 2025 . |
| MLA | Jiang, Hao et al. "Power flow control of local power system with power flow controllable transformer" . (2025) . |
| APA | Jiang, Hao , Yi, Yang , Zhu, Lin , Yao, Zhiwei . Power flow control of local power system with power flow controllable transformer . (2025) . |
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The axial power deviation of a reactor can reflect the axial power distribution of the core and the operation of the reactor. Aiming at the difficulties in predicting the axial power deviation under variable operating conditions, this paper proposes a prediction method of reactor axial power deviation based on the combined feature selection and temporal convolutional network (TCN). Taking the basic principle of axial power deviation control as the starting point, this paper analyzes the factors affecting the change of axial power deviation, comprehensively analyzes the redundancy and correlation among multi-dimensional features, uses the combined feature selection strategy to form the optimal feature subset for axial power deviation prediction, constructs the key correlation feature data for axial power deviation prediction, and inputs it into TCN to capture dynamic causality, so as to achieve the prediction of reactor axial power deviation. Experimental studies show that the proposed method can deeply explore the temporal causal change characteristics of the parameters related to the axial power deviation of the reactor, accurately predict the development trend of the axial power deviation, solve the problem that the traditional prediction model does not predict and track in time under complex operating conditions, and provide an auxiliary reference basis for the reactor status monitoring and safe operation of nuclear power plants. © 2025 Atomic Energy Press. All rights reserved.
Keyword ：
Nuclear energy Nuclear energy Nuclear power plants Nuclear power plants Prediction models Prediction models Reactor operation Reactor operation
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| GB/T 7714 | Chen, Jing , Chen, Yan , Jiang, Hao et al. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network [J]. | Nuclear Power Engineering , 2025 , 46 (2) : 239-247 . |
| MLA | Chen, Jing et al. "Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network" . | Nuclear Power Engineering 46 . 2 (2025) : 239-247 . |
| APA | Chen, Jing , Chen, Yan , Jiang, Hao , Duan, Pengbin , Lin, Weiqing , Qiu, Xinghua et al. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network . | Nuclear Power Engineering , 2025 , 46 (2) , 239-247 . |
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热状态参量预测是特高压变压器绝缘老化评估及故障预警的重要技术方法.然而,现有预测方法侧重高维时间序列分析以构建数据驱动模型,未计及设备内部温度潜在的空间变化规律,为此,提出一种基于时空特征挖掘的特高压变压器热状态参量预测方法.首先,综合考虑多源数据间的相关度与冗余度,提出组合特征筛选策略寻找最优特征子集;其次,结合热状态参量的最优特征子集及相关系数,构建面向热状态参量预测的时空图数据;最后,建立双重自适应图卷积门控循环单元(dual adaptive graph convolution gate recurrent unit,DA-GCGRU)模型,采用节点自适应模块强化油箱内不同部位温度变化趋势的拟合,以适应特定温升趋势;采用图自适应模块自主学习热状态参量的空间温度分布关联性,以推断空间映射关系.实验结果表明,该方法可深度挖掘特高压变压器内部温度的时空变化特性,准确预测绕组温度和顶层油温的变化趋势,具有较好的鲁棒性和泛化性.
Keyword ：
图卷积网络 图卷积网络 特高压变压器 特高压变压器 绕组温度 绕组温度 自适应 自适应 门控循环单元 门控循环单元 顶层油温 顶层油温
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| GB/T 7714 | 林蔚青 , 缪希仁 , 肖洒 et al. 基于时空特征挖掘的特高压变压器热状态参量预测方法 [J]. | 中国电机工程学报 , 2024 , 44 (4) : 1649-1661,中插33 . |
| MLA | 林蔚青 et al. "基于时空特征挖掘的特高压变压器热状态参量预测方法" . | 中国电机工程学报 44 . 4 (2024) : 1649-1661,中插33 . |
| APA | 林蔚青 , 缪希仁 , 肖洒 , 江灏 , 卢燕臻 , 邱星华 et al. 基于时空特征挖掘的特高压变压器热状态参量预测方法 . | 中国电机工程学报 , 2024 , 44 (4) , 1649-1661,中插33 . |
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For intelligent inspection of power lines, a core task is to detect components in aerial images. Currently, deep supervised learning, a data-hungry paradigm, has attracted great attention. However, considering real-world scenarios, labeled data are usually limited, and the utilization of abundant unlabeled data is rarely investigated in this field. This study deploys a pretrained model for power line component detection based on a self-supervised pretraining approach, which exploits useful information from unannotated data. Concretely, we design a new masking strategy based on the structural characteristic of power lines to guide the pretraining process with meaningful semantic content. Meanwhile, a Siamese architecture is proposed to extract complete global features by using dual reconstruction with semantic targets provided by the proposed masking strategy. Then, the knowledge distillation is utilized to enable the pretrained model to learn both domain-specific and general representations. Moreover, a feature pyramid mechanism is adopted to capture multiscale features, which can benefit the detection task. Experimental results show that the proposed approach can successfully improve the performance of a variety of detection frameworks for power line components, and outperforms other self-supervised pretraining methods.
Keyword ：
Component detection Component detection deep learning deep learning machine vision machine vision power line inspection power line inspection self-supervised pretraining self-supervised pretraining
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| GB/T 7714 | Liu, Xinyu , Miao, Xiren , Jiang, Hao et al. Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 513-523 . |
| MLA | Liu, Xinyu et al. "Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 513-523 . |
| APA | Liu, Xinyu , Miao, Xiren , Jiang, Hao , Chen, Jing , Wu, Min , Chen, Zhenghua . Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 513-523 . |
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Fault detection of electrical poles is part of the daily operation of power utilities to ensure the sustainability of power transmission. This paper develops a method for intelligent detection of fallen poles based on the improved YOLOX. The hyper-parameters in this method are optimized automatically by Particle Swarm Optimization (PSO) including batch size and input resolution. During parameter optimization, a specific comprehensive evaluation metric is presented as the fitness function to obtain optimal solutions with low labor cost and high method performance. In addition, virtual pole images are generated by 3D Studio Max to overcome the imbalance problem of normal and fault data. The results show that the proposed method can achieve 95.7% of recall and 98.9% of precision, which demonstrates the high accuracy of the method in fallen pole detection. In the comparative experiment, the proposed PSO-YOLOX method is superior to the existing methods including original YOLOX and Faster R-CNN, which verifies the effectiveness of automatic optimization and virtual data augmentation.
Keyword ：
Fallen poles detection Fallen poles detection Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) UAV inspection UAV inspection YOLOX YOLOX
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| GB/T 7714 | Jiang, Hao , Wang, Ben , Wu, Li et al. Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2024 . |
| MLA | Jiang, Hao et al. "Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization" . | MULTIMEDIA TOOLS AND APPLICATIONS (2024) . |
| APA | Jiang, Hao , Wang, Ben , Wu, Li , Chen, Jing , Liu, Xinyu , Miao, Xiren . Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization . | MULTIMEDIA TOOLS AND APPLICATIONS , 2024 . |
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