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Grid-Connected/Islanded Switching Control Strategy for Photovoltaic Storage Hybrid Inverters Based on Modified Chimpanzee Optimization Algorithm

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Energy Engineering 2025年第1期122卷 265-284页

作者： Chao Zhou Narisu Wang Fuyin Ni Wenchao Zhang School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology Jiangsu University of TechnologyChangzhou213001China

Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island *** response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization *** proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop ***,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control *** pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid ***,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee *** operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load *** strategy effectively mitigated transient voltage and current surges during mode ***,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid *** enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.

关键词： Photovoltaic storage hybrid inverters modified chimpanzee optimization algorithm droop control seamless switching

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Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm

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Energy Engineering 2025年第3期122卷 1153-1174页

作者： Zhaoyu Zeng Fuyin Ni School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology Jiangsu University of TechnologyChangzhou213001China

Energy storage power plants are critical in balancing power supply and ***,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy *** tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network *** architecture effectively reduces transmission costs by minimizing data travel *** addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation *** IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal *** results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and *** successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy *** edge-computing framework significantly reduces transmission delays between energy ***,IWOA outperforms traditional algorithms in optimizing the objective function.

关键词： Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm

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Research on Load Forecasting Based on Deep Learning Framework Optimized by Sparrow Search Algorithm

Research on Load Forecasting Based on Deep Learning Framewor...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Zeng, Zhaoyu Ni, Fuyin School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China Jiangsu University of Technology Jiangsu Key Laboratory for Power Transmission & Distribution Equipment Technology Changzhou213001 China

ISBN: (纸本)9798331506797

Precise power load forecasting is crucial for maintaining the stable and efficient operation of power systems. To enhance predicted accuracy, this study introduces a hybrid forecasting approach that integrates Sparrow Search Algorithm (SSA) with deep learning framework. Convolutional neural network (CNN) is employed to capture nonlinear relationships among factors influencing power load, generating high-quality feature representations. Then the Sparrow Search Algorithm (SSA) optimizes the parameters of the Long Short-Term Memory (LSTM) to enhance its fitting ability to handle raw load data. Subsequently, the LSTM models the time-dependence of power loads and accurately captures the short-term fluctuation characteristics. Finally, the Attention mechanism is incorporated to assign weights to the input features, emphasizing critical features and improving the prediction accuracy. The experimental findings indicate that, compared with alternative models, the proposed CNN-SSA-LSTM-Attention model attains an $\mathrm{R}^power$ value of 0.983, with the MAPE significantly decreased to 2.85%. In addition, the RMSE and MAE are reduced to 2.94kW and 2.12kW, respectively, which are better than other models. © 2025 IEEE.

关键词： Attention Mechanism Load Forecasting Neural Network Sparrow Search Algorithm

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A Multi-Granularity Deep Signal Shrinkage Network for Noise-Robust Specific Emitter Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4256-4264页

作者： Han, Guangjie Wang, Weitao Xu, Zhengwei Hohai University Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou213022 China Nanjing Research Institute of Electronics Technology Nanjing210039 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

Wireless network security is a significant issue in wireless communication systems. Specific emitter identification (SEI) technology, as an effective physical layer authentication method, has been extensively studied. Methods based on deep learning (DL) for SEI have emerged as the predominant approach, attributed to their end-to-end recognition framework and enhanced capability for feature extraction. However, the training of DL models relies on high-quality data, and the data collection in real-world scenarios is often in low signal-to-noise ratio (SNR) environments, leading to poor model training performance. This paper presents a novel solution, the Multi-Granularity Deep Signal Shrinkage Network (MGDSSN), for the challenging task of SEI in low SNR environments. To this end, the proposed MGDSSN incorporates soft thresholding processing and employs subnetworks for adaptive thresholding, effectively eliminating noise-related features and achieving robust SEI in low SNR environments. Additionally, MGDSSN incorporates a multi-granularity deep signal network architecture that improves the recognition accuracy and stability of the model. This is achieved by capturing the interrelated attributes of in-phase/quadrature-phase (I/Q) signals and features at multiple levels of granularity. Experiments conducted with real-world dataset reveal that the proposed MGDSSN surpasses the current state-of-the-art SEI methods in low SNR environments, demonstrating robust SEI and verifying the superiority of the proposed method. © 2005-2012 IEEE.

关键词： Signal to noise ratio

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Research on Load Forecasting Based on Deep Learning Framework Optimized by Sparrow Search Algorithm

Research on Load Forecasting Based on Deep Learning Framewor...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Zhaoyu Zeng Fuyin Ni School of Electrical and Information Engineering Jiangsu University of Technology Changzhou China Jiangsu Key Laboratory for Power Transmission & Distribution Equipment Technology Jiangsu University of Technology Changzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

关键词： Attention mechanisms Accuracy Fluctuations Load forecasting Predictive models Prediction algorithms Feature extraction Convolutional neural networks Long short term memory Load modeling

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A Novel Partial Domain Adaptation Method for Cross-Domain Specific Emitter Identification

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Han, Guangjie Zhang, Yu Xu, Zhengwei Wang, Weitao Hohai University Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Changzhou213022 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

In wireless communication networks (WCN), specific emitter identification (SEI) technology is significant for guaranteeing cybersecurity and can effectively defend against network attacks. Compared to traditional SEI methods that depend on specific training data or environmental conditions, cross-domain recognition study has attracted widespread attention for dynamic WCN. However, in a more general scenario, the target domain (TD) sometimes represents a subset of source domain (SD), i.e., in partial domain adaptation (PDA), which has not been well addressed. To solve this concern, we present a novel selective partial domain adaptation method based on complex-valued convolutional block attention module (CSPDA) for SEI, which tackles the domain shift challenge caused by the inconsistency in emitter categories between SD and TD. The proposed method includes a mixed feature extraction module that efficiently extracts representative features from both source and target raw signals at multiple levels, reducing feature information loss. In addition, by using the maximum of cosine (MoC) assessment mechanism and a data information filtering strategy, we can dynamically select the appropriate source signal samples to match each target signal sample. Experiment results on real-world data show that the CSPDA method achieves a recognition accuracy of 73.46% at -5 dB and 89.28% at 20 dB, outperforming other transfer learning and domain adaptation methods in PDA tasks. © 2015 IEEE.

关键词： Information filtering

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Flexibility characterization and analysis of electric vehicle clusters based on real user behavior data

Flexibility characterization and analysis of electric vehicl...

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作者： Dong, Xiaohong Ren, Yanqi Zhou, Yue Si, Qianyu Dong, Xing Wang, Mingshen State Key Laboratory of Intelligent Power Distribution Equipment and System Hebei University of Technology Tianjin300130 China School of Engineering Cardiff University Wales CardiffCF24 3AA United Kingdom State Grid Jiangsu Electric Power Research Institute Nanjing211103 China

With the increase in electric vehicles (EVs), they have begun to be valued as a new flexibility resource. Characterizing the flexibility of EVs is conducive to fully utilizing their flexibility to support the operation of electric power systems. A flexibility characterization method for EVs based on real user behavior data was proposed. First, the raw data are cleaned to obtain a dataset for each EV user. Then, a clustering algorithm is used to mine the plug-in time habit of each EV user, and an EV flexibility potential index system is proposed to quantify the flexibility of EV users with adjustable capacity, on-grid time, and charging power under the plug-in time habit. Furthermore, based on the flexibility indexes, the EV flexibility region under each plug-in time habit was constructed. Finally by setting the generators reasonably, the flexibility characterization from one EV user to the EV user cluster that includes large EVs is realized with the help of the zonotope similar. The flexibility of 6903 EV users in Nanjing, China, was characterized. The results showed that the similarity was improved by 0.566 by resetting the generator. Simultaneously, the results demonstrate the flexibility of the EV cluster in different periods. For example, there is a capacity adjustment range of approximately 75–150 MWh in 20:00–22:00, and approximately 15–20 MWh in 2:00–4:00 for the power grid to carry out day-ahead dispatching of EVs to assist the operation of the power grid. © 2025

关键词： Electric vehicles Flexibility Flexible region User data Zonotope

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Active Adjustable Coil Receiver for Adaptive Output power Based on Wireless power Transfer

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International Journal of Circuit Theory and Applications 2025年

作者： Dai, Zhongyu Wang, Huihui Ma, Yayun Li, Musong Zhou, Yitong Zhang, Xian State Key Laboratory of Intelligent Power Distribution Equipment and System Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Key Laboratory of Flexible DC Transmission Equipment and Technology Demonstration of Hebei Province Electrical Engineering Department Hebei University of Technology Tianjin China State Grid Tangshan Power Supply Company Tangshan China

The wireless power transfer (WPT) system operates under variable load conditions. During different stages of charging, the power required by the load varies. Therefore, the WPT needs to adjust the voltage and power in real-time to meet these differing demands. The power transfer power of WPT can be optimized for variable loads. This paper introduces an active adjustable coil receiver with adaptive output power. The end of the sliding rod is connected to the accessible part of each subcoil. The sliding rod can slide outward from the center of the coil or inward toward the center along the accessible part, determining the number of coil turns to be involved in coupling within the circuit. The proposed design can achieve the stabilized output power of ACR in the range of 10 to 30 W with a transmission distance varying from 5 to 10 cm, 10–38 W with a radial offset distance of 0.6–10 cm and 3–21 W with load of 20–40 (Formula presented.). The receiver can meet the different power requirements of the load and is not sensitive to factors such as transmission distance and offset. The system can reduce the complexity of charging and power supply system design and effectively promote the application of wireless power transmission in the field of multiload equipment. © 2025 John Wiley & Sons Ltd.

关键词： Inductive power transmission

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Effects of Structural Changes on Mechanical Properties of XLPE with Different Cross-linking Modes During Thermal Oxidative Aging: A Theoretical Analysis

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Ye, Wenyu Gao, Chenyu Hao, Jian Che, Haolun Zhang, Jianwen Liao, Ruijin China University of Mining and Technology School of Electrical Engineering Xuzhou Jiangsu221116 China State Grid Xuzhou Power Supply Company Xuzhou Jiangsu221000 China Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China

The thermal oxidative aging of cross-linked polyethylene (XLPE) during cable operation is a primary factor leading to changes in its mechanical properties. Notably, these properties exhibit an initial increase followed by a decrease as aging progresses. In this study, molecular models of silane-cross-linked polyethylene (Si-XLPE) and peroxide-cross-linked polyethylene (p-XLPE) were constructed to analyze their thermal oxidative aging processes using ReaxFF simulations. The research focused on understanding how structural changes in XLPE during aging impact its mechanical properties from the perspective of intermolecular forces. The simulation results reveal that Si-XLPE initially degrades from the silane cross-linking bonds, eventually forming small hydrocarbon molecules. In contrast, p-XLPE experiences the formation of C=O and C=C bonds during its structural degradation. As the degree of aging increases, both the density and cohesive energy density of XLPE initially rise and then decline, while the free volume fraction exhibits a decrease followed by an increase. This indicates that the early stages of aging enhance the intermolecular forces within XLPE, which explains the observed pattern in mechanical property changes. Furthermore, the study finds that Si-XLPE consistently exhibits stronger intermolecular forces than p-XLPE across all stages of aging, contributing to its superior mechanical properties. These simulation results effectively explain the mechanical behavior observed in experimental studies of XLPE. This paper is expected to provide some new perspectives and ideas on constructing the structure-activity relationship between the microstructure and macroscopic properties of XLPE. © 2025 IEEE.

关键词： Crosslinking

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Molecular Insights into the Effects of Thermal Oxidative Aging on the Insulation Properties of Cross-Linked Polyethylene

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Ye, Wenyu Gao, Chenyu Qiao, Xinhan Che, Haolun Zhang, Jianwen Hao, Jian China University of Mining and Technology School of Electrical Engineering Jiangsu Xuzhou221116 China State Grid Xuzhou Power Supply Company Jiangsu Xuzhou221000 China Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China

The degradation of the insulation performance of cross-linked polyethylene (XLPE) during thermal oxidative aging is a crucial factor affecting the safe operation of cables. Investigating the influence of the structural degradation process of XLPE on its insulation properties from a molecular structure and microscopic parameter perspective can provide a better understanding of the molecular mechanisms behind insulation performance degradation during thermal oxidative aging. In this study, the reaction kinetics of XLPE were simulated using the Ab initio Molecular Dynamics (AIMD), resulting in the extraction of five XLPE structures with varying degrees of aging. Density functional theory simulations were employed to analyze the variation patterns and differences in discharge-related microscopic parameters of these five different XLPE structures under varying electric field intensities. The results show that there are significant differences in the structure and dipole moment of XLPE with different aging degrees, resulting in large changes in its microscopic parameters. In particular, the formation of carbonyl groups in XLPE has a significant impact on its structure and microscopic parameters. As the aging degree of XLPE increases, the ionization of XLPE molecules and the activity of electron affinity molecules intensify. The structural evolution of XLPE during the aging process markedly influences the excitation process and molecular orbitals of its molecules, facilitating the release of numerous photons, creating conditions for secondary electron collapse, and enhancing molecular conductivity. The simulation results of the molecular surface electrostatic potential reveal that deeper aging of XLPE increases the likelihood of electrophilic and nucleophilic reactions, as well as electron accumulation and collision. Overall, the electric field exerts a minimal effect on the molecular structure and microscopic parameters of different XLPE molecules. © 1994-2012 IEEE.

关键词： Electron affinity

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