期刊：
Electric Power Systems Research,2026年250:112102 ISSN：0378-7796
通讯作者：
Yong Li
作者机构：
College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China;Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China;[Yijia Cao] School of Electrical & Information Engineering, Changsha University of Science and Technology, Changsha, 410114, China;[Fang Wu; Rui Li; Jiuqing Cai] Wuhan second ship design and research institute, Wuhan, 44227, China;[Yinglong Zhao; Yong Li; Sijia Hu] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China
通讯机构：
[Yong Li] C;College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China
关键词：
Electrified ship;Electric power system;Transformer design;Vibration reduction;Finite element model;Multiple optimization
摘要：
The optimization of transformer vibration and power density in marine electrical power systems poses a challenging task due to the constraints imposed by ship noise and limited space. This paper introduces a multi-optimization design method for transformers based on multi-objective optimization model and finite element model, with the objective of minimizing vibration and improving power density. The proposed approach leverages the merits of both methodologies by initially utilizing a multi-objective optimization technique to attain an optimal transformer preliminary design, featuring optimized volume, loss, and vibration acceleration. Subsequently, based on this preliminary design, a finite element model is constructed to further refine the transformer’s placement configuration and thermal limits, ultimately yielding an optimal design scheme for a transformer that boasts both low vibration and high power density. Experimental results demonstrate that the proposed method effectively reducing transformer vibrations and volume. Compared to previous-generation transformers not utilizing this method, the proposed approach leads to a 55.81% reduction in vibrational acceleration and 44.93% reduction in volume. Additionally, the calculation values of the transformer from the proposed method exhibit high precision compared to actual measurements.
The optimization of transformer vibration and power density in marine electrical power systems poses a challenging task due to the constraints imposed by ship noise and limited space. This paper introduces a multi-optimization design method for transformers based on multi-objective optimization model and finite element model, with the objective of minimizing vibration and improving power density. The proposed approach leverages the merits of both methodologies by initially utilizing a multi-objective optimization technique to attain an optimal transformer preliminary design, featuring optimized volume, loss, and vibration acceleration. Subsequently, based on this preliminary design, a finite element model is constructed to further refine the transformer’s placement configuration and thermal limits, ultimately yielding an optimal design scheme for a transformer that boasts both low vibration and high power density. Experimental results demonstrate that the proposed method effectively reducing transformer vibrations and volume. Compared to previous-generation transformers not utilizing this method, the proposed approach leads to a 55.81% reduction in vibrational acceleration and 44.93% reduction in volume. Additionally, the calculation values of the transformer from the proposed method exhibit high precision compared to actual measurements.
摘要：
This study aims to investigate SF 6 decomposition gases, specifically H 2 S, SO 2 , SOF 2 , and SO 2 F 2 , as a means of diagnosing faults in GIS. Using first-principles density functional theory (DFT), simulations were conducted on Zr- and In-modified WTe 2 from several perspectives, including adsorption energy, charge transfer, adsorption distance, density of states, differential charge density, and desorption times at different temperatures. The results demonstrate that the modification of WTe 2 with Zr and In atoms is spontaneous. The pristine WTe 2 exhibits weak physisorption towards all four SF 6 decomposition gases. In contrast, the Zr-WTe 2 system shows strong chemisorption for all four decomposition gases, while the In-WTe 2 system exhibits robust chemisorption specifically for SO 2 , SOF 2 , and SO 2 F 2 , with adsorption energies of −1.169 eV, −1.371 eV, and −1.255 eV, respectively. The Zr-WTe 2 system also demonstrates prolonged desorption times for H 2 S, SO 2 , SOF 2 , and SO 2 F 2 , suggesting its potential as a scavenger for SF 6 decomposition gases. Notably, the In-WTe 2 system exhibits a rapid desorption time of only 0.892 s for SOF 2 at room temperature, indicating its potential for detecting H 2 S gas under ambient conditions.
关键词：
IES-WTE;CCS-P2G;carbon trading;ladder-type GCT;ladder-type CET;low-carbon dispatch;synergistic interaction mechanism;multi-energy system optimization
摘要：
Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a "green-carbon" offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the "green-carbon" offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO2 produced by waste incineration is absorbed and reused. Finally, the "green-carbon" offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES.
关键词：
Photovoltaic power generation;Power outlier detection;Sunny day screening;Quantile regression recurrent neural network;Power output correlation
摘要：
Distributed photovoltaic (PV) power generation systems are widely spread. Moreover, due to the randomness of meteorological conditions and the complexity of installation environments, it is difficult to eliminate the interference of factors such as meteorological fluctuations in the monitoring of abnormal states of PV equipment. Based on this, this paper proposes a PV power generation anomaly detection method based on Quantile Regression Recurrent Neural Network (QRRNN). First, the characteristics of solar irradiance on clear days are analyzed, and the clear day masking method is used to eliminate the interference of cloudy and rainy weather. Then, the output correlation of different power stations is analyzed to obtain PV stations with high output correlation as the horizontal reference, which is used to exclude interferences such as permanent faults at the power stations. At the same time, vertical comparison of the output curves of the station under test on different clear days is conducted to eliminate interference factors such as weather and environmental conditions. Subsequently, the metered active power output data, which is free from interference, is input into the QRRNN model to obtain the normal active power output range of the PV. The power threshold of the normal output range is utilized to identify anomalies in PV power generation. Finally, simulation analysis of actual PV system data is conducted, and the results show that the method can effectively identify PV power generation anomalies and has high accuracy in PV fault detection.
Distributed photovoltaic (PV) power generation systems are widely spread. Moreover, due to the randomness of meteorological conditions and the complexity of installation environments, it is difficult to eliminate the interference of factors such as meteorological fluctuations in the monitoring of abnormal states of PV equipment. Based on this, this paper proposes a PV power generation anomaly detection method based on Quantile Regression Recurrent Neural Network (QRRNN). First, the characteristics of solar irradiance on clear days are analyzed, and the clear day masking method is used to eliminate the interference of cloudy and rainy weather. Then, the output correlation of different power stations is analyzed to obtain PV stations with high output correlation as the horizontal reference, which is used to exclude interferences such as permanent faults at the power stations. At the same time, vertical comparison of the output curves of the station under test on different clear days is conducted to eliminate interference factors such as weather and environmental conditions. Subsequently, the metered active power output data, which is free from interference, is input into the QRRNN model to obtain the normal active power output range of the PV. The power threshold of the normal output range is utilized to identify anomalies in PV power generation. Finally, simulation analysis of actual PV system data is conducted, and the results show that the method can effectively identify PV power generation anomalies and has high accuracy in PV fault detection.
关键词：
Computer crime;Location awareness;Hybrid power systems;Prevention and mitigation;Automation;Power distribution;Hardware;Cyber-physical system;cyber security;hybrid attack;bi-level resilient control;feeder automation
摘要：
Distributed feeder automation system (DFAs), as a promising protection technology for power distribution system (PDS) with distributed generation, its vulnerability to cyberattacks and hybrid attacks (contains both physical and cyberattacks) is gradually recognized and haunts utilities, creating potential risks for its large-scale applications. This paper proposes a novel bi-level resilient control solution (BRCS) deployed to DFAs without hardware burden. Two key modules are developed: 1) Lightweight distributed cyberattack detection module (DCDM), deployed into agents of DFAs, based on unsupervised learning to realize the quick detection and reporting of cyberattacks; 2) Robust centralized fault section localization module (CFSLM), installed in DFAs’ host workstation located in control center, achieving the correctly fault section localization and the high dimensionally awareness of attack events in cyberattack and hybrid attack scenarios. By adopting BRCS, outages and load losses caused by cyberattacks can be 100% avoided, and faults caused by physical attacks can be correctly isolated at once. Finally, the effectiveness and performance of the proposal are verified and conducted by the real two-feeder test platform with DFAs. In this process, a digital high-dimensional awareness and control unit is created against cyber and hybrid attacks, contributing to the system-level application of risk management and resilient control.
通讯机构：
[Huang, JC; Peng, ZY ] C;Changsha Univ Sci & Technol, Sch Energy & Power Engn, Key Lab Efficient & Clean Energy Utilizat, Changsha 410111, Peoples R China.
摘要：
Despite the advancements in film fabrication techniques for emerging perovskite solar cells, achieving a high-quality film by solution processing, while maintaining considerable performance remains a significant challenge. To tackle the issue of inferior CsPbI 2 Br perovskite films deposited via solution-based methods, a novel thermal conduction heating approach was devised and implemented, significantly enhancing film uniformity. Crucially, aliphatic amine acetates (3A) were introduced into the precursor solution to regulate the crystallization process and therefore to mitigate defects. Systematic investigation into the impact of 3A molecules featuring varying alkyl chain lengths on defect passivation revealed that the molecular dipole moment of these additives contributed to both defect mitigation and grain size refinement. Notably, the integration of alkyl chains significantly bolstered the hydrophobic properties of the perovskite film. Consequently, an impressive efficiency of 13.50% for HTM-free carbon-based CsPbI 2 Br perovskite solar cells was achieved, and the device exhibited robust stability retaining 92.4% of its initial efficiency at room temperature after being stored in dry air for 5400 h. This research offers profound insights into defect passivation mechanisms and perovskite crystallization dynamics, paving the way for further advancements in the field of perovskite solar cell technology.
摘要：
Water extraction from Synthetic Aperture Radar (SAR) images is crucial for water resource management and maintaining the sustainability of ecosystems. Though great progress has been achieved, there are still some challenges, such as an insufficient ability to extract water edge details, an inability to detect small water bodies, and a weak ability to suppress background noise. To address these problems, we propose the Global Context Attention Feature Fusion Network (GCAFF-Net) in this article. It includes an encoder module for hierarchical feature extraction and a decoder module for merging multi-scale features. The encoder utilizes ResNet-101 as the backbone network to generate four-level features of different resolutions. In the middle-level feature fusion stage, the Attention Feature Fusion module (AFFM) is presented for multi-scale feature learning to improve the performance of fine water segmentation. In the advanced feature encoding stage, the Global Context Atrous Spatial Pyramid Pooling (GCASPP) is constructed to adaptively integrate the water information in SAR images from a global perspective, thereby enhancing the network's ability to express water boundaries. In the decoder module, an attention modulation module (AMM) is introduced to rearrange the distribution of feature importance from the channel-space sequence perspective, so as to better extract the detailed features of water bodies. In the experiment, SAR images from Sentinel-1 system are utilized, and three different water areas with different features and scales are selected for independent testing. The Pixel Accuracy (PA) and Intersection over Union (IoU) values for water extraction are 95.24% and 91.63%, respectively. The results indicate that the network can extract more integral water edges and better detailed features, enhancing the accuracy and generalization of water body extraction. Compared with the several existing classical semantic segmentation models, GCAFF-Net embodies superior performance, which can also be used for typical target segmentation from SAR images.
作者机构：
[Liu, Rui; Zhang, Chuanliang; Chen, Jiaxiang; Wang, Ziyi] Changsha Univ Sci & Technol, Coll Energy & Power Engn, Changsha 410114, Peoples R China.;[Zhao, Bin] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
通讯机构：
[Zhao, B ] C;Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
关键词：
Airfoil fin PCHE;Bezier curves;Pareto front;Multi-objective genetic algorithm;Comprehensive performance
摘要：
The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bézier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bézier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (η1) and the third enhanced ratio (η3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to η1 and η3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the η1 of the PCHE channel gradually increases, while η3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, η3 significantly increases while η1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, η3 improves without markedly affecting η1. Compared to the traditional airfoil channel, the η1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the η3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.
The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bézier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bézier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (η1) and the third enhanced ratio (η3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to η1 and η3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the η1 of the PCHE channel gradually increases, while η3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, η3 significantly increases while η1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, η3 improves without markedly affecting η1. Compared to the traditional airfoil channel, the η1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the η3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.
摘要：
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
摘要：
Hemodialysis, a renal replacement treatment for end-stage renal failure, relies heavily on the proper functioning of the dialysis machine. Timely detection and handling of dialysis machine alarms are important to ensure the safety of dialysis treatment. This study proposes a method for recognizing dialysis machine alarms using a convolutional neural network (CNN). A dataset of dialysis machine alarm light images was created through a multicenter collaboration, which was used to train the YOLOv5 model. The study shows that the average recognition precision, recall, and mAP@0.5 for each warning light category reached 0.892, 0.813, and 0.833, respectively. A well-trained model can quickly and accurately recognize a variety of dialysis machine alarm types. It is feasible to use convolutional neural networks to recognize dialysis machine alarms, and they can be widely used to improve dialysis safety and management.
作者机构：
[Rui Ma] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China;[Kai Liu; Hongwen Yan] School of Computer Science and Technology, Changsha University of Science and Technology, Changsha, China
会议名称：
2025 2nd International Conference on Electronic Engineering and Information Systems (EEISS)
会议时间：
23 May 2025
会议地点：
Nanjing, China
会议论文集名称：
2025 2nd International Conference on Electronic Engineering and Information Systems (EEISS)
摘要：
To address the challenges of strong multiscale spatiotemporal feature coupling and complex long-term dependencies in short-term power load data, this paper proposes a hybrid short-term load forecasting (STLF) method based on an improved Temporal Fusion Transformer (TFT) model. First, a parallel dilated convolutional network (DCNN) is constructed as a feature extraction module, leveraging convolutional kernels with different dilation rates to capture local periodic patterns and multiscale spatiotemporal correlations in load sequences. Second, a Bidirectional Gated Recurrent Unit (BiGRU) is used to replace the Long Short-Term Memory (LSTM) structure in the traditional TFT, reducing model complexity by simplifying the gating mechanism while enhancing the ability to capture dynamic temporal features. Finally, an improved TFT architecture employs a hierarchical attention mechanism for explicit modeling of multi-source covariates. Experimental results demonstrate that the proposed model achieves higher accuracy than benchmark models on a public dataset, both on weekdays and non-working days.
作者机构：
[Wenjuan Liu; Dongqi Liu] Department of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, China
会议名称：
2025 IEEE 8th International Electrical and Energy Conference (CIEEC)
会议时间：
16 May 2025
会议地点：
Changsha, China
会议论文集名称：
2025 IEEE 8th International Electrical and Energy Conference (CIEEC)
关键词：
Resilient control;vertical federal learning;network attack;Microgrid;distributed cooperative control
摘要：
In microgrids, distributed energy resources share information through sensor and communication systems, which are vulnerable to cyber attacks. This article addresses safety and control challenges in microgrid systems, particularly against network attacks like random denial-of-service. It introduces an adaptive gain elastic controller to ensure system stability by disconnecting communication links with excessive delays and restoring connections through optimization. Additionally, the article presents federated learning as a privacy-preserving method that allows microgrids to collaboratively train models without centralizing data, thus reducing system failure risks. A method for optimizing the collaborative operation of multi-agent microgrids using vertical federated learning is described, involving local training, strategy searching in the cloud, and distributed joint training. Testing confirms the method's effectiveness in enhancing privacy protection and network resilience. Future research will focus on improving computing efficiency and assessing each microgrid's contributions to optimize collaborative strategies.
期刊：
IET Renewable Power Generation,2025年19(1):e70119 ISSN：1752-1416
通讯作者：
Zhiyi Li
作者机构：
[Yanfu Jiang] Faculty of Engineering, Monash University, Clayton, Victoria, Australia;[Md Tanjid Hossain; Xutao Han; Zhiyi Li] Department of Electrical Engineering, Zhejiang University, Hangzhou, China;[Xingyu Shi] Department of Electrical Engineering, Changsha University of Science and Technology, Changsha, China
通讯机构：
[Zhiyi Li] D;Department of Electrical Engineering, Zhejiang University, Hangzhou, China
摘要：
Seasonal fluctuations and the intermittent nature of photovoltaic (PV) generation create significant challenges for accurate short-term forecasting. This study presents Next Frame Gramian Angular field U-Net (NFGUN), a hybrid deep learning forecasting framework that stands apart from conventional methods by transforming 1D PV time-series data into 2D Gramian Angular Summation Field (GASF) images. Unlike models that rely on direct regression or sky imagery, NFGUN forecasts the next GASF frame using a deep architecture and reconstructs it back into time-series form, effectively capturing nonlinear temporal dynamics. Its uniqueness lies in several key innovations: (1) the integration of Convolutional Long Short-Term Memory 2D (ConvLSTM2D) into a customised U-Net model for better generalisation spatiotemporal features; (2) the incorporation of residual blocks in the bottleneck to preserve deep features while mitigating vanishing gradients and cyclical encoding of time to enrich seasonal patterns; (3) the use of Lanczos interpolation with CIEDE2000 colour difference for high-precision reconstruction from predicted image frames. We evaluate NFGUN against six well-established forecasting methods and measure performance using six accuracy metrics such as MAE, RMSE, and WAPE across all four seasons; NFGUN demonstrates superior performance. Compared to the best-performing benchmark, it achieved improvements in MAE (61.23% winter, 56% spring, 37.45% summer, 59.67% autumn), RMSE (48.34% winter, 64.63% spring, 31.65% summer, 45.83% autumn), and WAPE (49.9% winter, 43.84% spring, 45.83% summer, 48.72% autumn), underscoring its ability to adapt to seasonal variability. These results demonstrate NFGUN's ability to effectively capture complex, seasonal dynamics, making it a robust solution for ultra-short-term PV power forecasting.
作者机构：
[Yinjie Li] School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha, China
会议名称：
2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA)
会议时间：
25 April 2025
会议地点：
Xi'an, China
会议论文集名称：
2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA)
关键词：
digital image processing;chaotic image encryption;s-box
摘要：
To enhance the speed of image encryption systems and address the efficiency issues of dynamic S-boxes in practical image encryption applications, this paper proposes a fast image encryption method based on a novel one-dimensional chaotic map and dynamic S-boxes. The method introduces a new chaotic map to expand the parameter space of the encryption system, thereby strengthening the system’s resistance to exhaustive attacks. Additionally, dynamic S-boxes are used for nonlinear transformations of different pixel points, replacing the permutation phase in the pixel encryption process. This eliminates the correlation between adjacent pixels, thus avoiding the time consumption caused by the permutation phase during encryption. The paper conducts detailed experiments on the proposed algorithm, and the experimental results demonstrate a significant improvement in efficiency compared to other image encryption methods, with an average increase of 50%. Furthermore, the proposed method demonstrates a clear advantage in decryption quality, effectively restoring plaintext images when processing ciphertext images contaminated by noise. These experimental results validate the effectiveness and innovation of the proposed method.
通讯机构：
[Zhao, YR ] H;Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China.
关键词：
Uncertainty;Games;Programming;Indexes;Stochastic processes;Resistance heating;Load modeling;Energy hub;stochastic distributionally robust chance-constrained optimization;Wasserstein metric;Stackelberg game;scenarios and moment information
摘要：
As the increasing complexity and uncertainty of integrated energy systems, the cooperative interaction under uncertainty between the energy hub (EH) and multi-energy users has become crucial for the efficient operation of energy systems. This paper proposes a unified collaborative and interactive framework to optimize operations between the EH and multi-energy users under uncertainty. Firstly, in the horizontal dimension, the stochastic distributionally robust chance-constrained (S-DRCC) optimization method with the Wasserstein metric is proposed to hedge against the uncertainty of EH considering the multiple scenarios and moment information of uncertain variables. Then in the vertical dimension, a bilevel Stackelberg game model is developed for EH and users to facilitate the collaborative interaction for multi-agents. Furthermore, several efficient methods are leveraged to reformulate the originally intractable framework into a tractable mixed-integer linear programming (MILP) model that can be directly solved using commercial solvers. Finally, the effectiveness of the proposed model and method are demonstrated by numerical case studies and corresponding simulation results.
关键词：
Monte Carlo method;snake-like robot;volume calculation;workspace;α-shape
摘要：
The method is applicable for solving the obstacle avoidance workspace of a snake-like robot working on high-voltage transmission cables, based on an improved Monte Carlo method, to address the issues of uneven distribution of scattered points, difficulty in extracting point cloud boundaries, and insufficient accuracy in traditional Monte Carlo methods. The proposed method first generates a seed workspace for the snake-like robot using traditional Monte Carlo method and then envelops the seed workspace with a cube and divides it into several smaller cubes that contain points in the workspace equally. Next, Gaussian distribution probability density function is used to extend and sample the seed workspace of the robot, generating the workspace of the snake-like robot. Finally, the α - shape algorithm is used to extract the point cloud boundaries of the snake-like robot workspace and calculate its volume, accurately determining the workspace. Simulation experiments comparing the reconstructed surface obtained from the α - shape algorithm with the point cloud of the snake-like robot workspace show high accuracy.
期刊：
IET Renewable Power Generation,2025年19(1):e70079 ISSN：1752-1416
通讯作者：
Wenchuan Meng
作者机构：
[Wenchuan Meng; Zaimin Yang; Zhi Rao; Siyang Sun] Energy Development Research Institute, China Southern Power Grid, Guangzhou, Guangdong, China;[Yixin Zhuo] Controlling Center, Guangxi Power Grid, Nanning, Guangxi, China;[Junjie Zhong; Sheng Su] College of Electrical & Information Engineering, Changsha University of Science and Technology, Changsha, Hunan, China
通讯机构：
[Wenchuan Meng] E;Energy Development Research Institute, China Southern Power Grid, Guangzhou, Guangdong, China
关键词：
building integrated photovoltaics;cascade control;wind power;photovoltaic power systems
摘要：
Wind power prediction plays a significant role in enhancing the effectiveness of power system operation and decision-making. Given the inherent stochastic nature of meteorological events, achieving highly accurate forecasts for wind power poses considerable challenges. To address this challenge, this paper initially leverages the time series learning capability of recurrent neural networks (RNN) to extract sequential information from historical wind power data. Subsequently, the information extracted from the convolutional layer is transferred to the light gradient boosting machine (LGBM), utilizing the feature extraction capability of convolutional neural networks (CNN). Furthermore, an optimal weighted combination is employed for the short-term prediction of wind power. Finally, a multi-step wind power prediction method of integrated CNN–RNN–LGBM is proposed in this paper. Simulation results demonstrate that the proposed CNN–RNN–LGBM framework outperforms other models during global training. Meanwhile, transferring the information from CNN to LGBM can improve its performance, proving the feature extraction ability of CNN.
摘要：
Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face difficulties related to insufficient feature extraction or excessive model complexity. In this study, we propose a self-optimizing polynomial neural network with a temporal feature enhancement, which is referred to as OPNN-T. Existing classifiers based on polynomial neural networks (PNNs) struggle to achieve high-quality performances when dealing with time series data, primarily due to their inability to extract temporal information effectively. The goal of the proposed classifier is to enhance the nonlinear modeling capability for time series data, thereby improving the classification rate in practical applications. The key features of the proposed OPNN-T include the following: (1) A temporal feature module is employed to capture the dependencies in time series data, providing adaptability and flexibility in handling complex temporal patterns. (2) A polynomial neural network (PNN) is constructed using sub-datasets combined with three types of polynomial neurons, which enhances its nonlinear modeling capabilities across diverse scenarios. (3) A self-optimization mechanism is integrated into iteratively optimized sub-datasets, features, and polynomial types, resulting in significant improvements in the classification rate. The experimental results demonstrate that the proposed method achieves superior performances across multiple standard time series datasets, exhibiting higher classification accuracy and greater robustness than the existing classification models. Our research offers an effective solution for time series classification, and highlights the potential of polynomial neural networks in this field.
摘要：
This paper presents a method of rotor position estimation for switched reluctance motors suitable for saturation. The effects of saturation as well as voltage changes are taken into account at the same time. It is based on the inductance in the unsaturated region. When the phase inductance is equal to the threshold, it is defined as a characteristic point. Meanwhile the characteristic pulse signal is triggered. Different inductance intersection thresholds are determined when the phase current and bus voltage change. The rotor position is estimated by interval speed. Compared with the traditional inductance method, the position estimation error is smaller. Finally, the correctness and effectiveness of the proposed method are verified by simulation and experiments.
作者机构：
[Xia, Xiangyang; Xia, Tian; Xia, XY; Yue, Jiahui] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.;[Gong, Yu] State Grid Jibei Elect Power Co Ltd, Beijing 100045, Peoples R China.;[Tan, Jianguo] Zhejiang Narada Power Source Co Ltd, Hangzhou 311300, Peoples R China.;[Wen, Lixing] Was Energy Technol Co Ltd, Xiangtan 411100, Peoples R China.
关键词：
lithium-ion battery;state of charge;Kalman filter algorithm;dung beetle optimizer
摘要：
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO's fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy.
