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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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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

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.

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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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

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

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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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Investigation of cavitation bubble collapse near rigid boundary by lattice Boltzmann method

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Journal of Hydrodynamics 2016年第3期28卷 442-450页

作者：单鸣雷朱昌平周曦殷澄韩庆邦 College of Internet of Things Engineering and Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai University

The dynamics of the bubble collapse near a rigid boundary is a fundamental issue for the bubble collapse application and prevention. In this paper, the bubble collapse is modeled by adopting the lattice Boltzmann method （LBM） and is verified, and then the dynamic characteristics of the collapsing bubble with the second collapse is investigated. The widely used Shan-Chen model in the LBM multiphase community is modified by coupling with the Carnahan-Starling equation of state （C-S EOS） and the exact difference method （EDM） for the forcing term treatment. The simulation results of the bubble profile evolution by the LBM are in excellent agreements with the theoretical and experimental results. From the two-dimensional pressure field evolution, the dynamic characteristics of the different parts during the bubble collapse stage are studied. The role of the second collapse in the rigid boundary damage is discussed, and the impeding effect between two collapses is demonstrated.

关键词： cavitation mechanics lattice Boltzmarm method bubble collapse rigid boundary

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Pseudopotential multi-relaxation-time lattice Boltzmann model for cavitation bubble collapse with high density ratio

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Chinese Physics B 2016年第10期25卷 189-196页

作者：单鸣雷朱昌平姚澄殷澄蒋小燕 College of Internet of Things Engineering Hohai University Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai University

The dynamics of the cavitation bubble collapse is a fundamental issue for the bubble collapse application and prevention. In the present work, the modified forcing scheme for the pseudopotential multi-relaxation-time lattice Boltzmann model developed by Li Q et al. [ Li Q, Luo K H and Li X J 2013 Phys. Rev. E 87 053301] is adopted to develop a cavitation bubble collapse model. In the respects of coexistence curves and Laplace law verification, the improved pseudopotential multi-relaxation-time lattice Boltzmann model is investigated. It is found that the thermodynamic consistency and surface tension are independent of kinematic viscosity. By homogeneous and heterogeneous cavitation simulation, the ability of the present model to describe the cavitation bubble development as well as the cavitation inception is verified. The bubble collapse between two parallel walls is simulated. The dynamic process of a collapsing bubble is consistent with the results from experiments and simulations by other numerical methods. It is demonstrated that the present pseudopotential multirelaxation-time lattice Boltzmann model is applicable and efficient, and the lattice Boltzmann method is an alternative tool for collapsing bubble modeling.

关键词： lattice Boltzmann method pseudopotential model bubble collapse improved forcing scheme

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transmission time in the reflectionless complex potential

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Chinese Physics B 2014年第10期23卷 7-11页

作者：殷澄王贤平单鸣雷韩庆邦朱昌平 Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai University School of Physics and Communication Electronics Jiangxi Normal University

A phase time definition directly obtained from the Schr6dinger equation is used to investigate the time delay of a particle scattered by complex reflectionless potential. The artifacts introduced by truncating in the numerical simulation are clarified. The time delay of the transmitted wave packet is found to be equal to the reflection time of the truncated potential. Both time delays are the same as the traversal time in the free space, but shorter than the time taken by a classical particle to pass the same potential.

关键词： complex reflectionless potentials transmission time supersymmetric quantum mechanics

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Upconversion Emission Properties of Erbium-and Ytterbium-Doped Potassium Lithium Tantalate Niobate Ceramics

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Chinese Physics Letters 2013年第12期30卷 116-119页

作者： LI Lei ZHOU Zhong-Xiang YANG Wen-Long LI Huan WU Ye Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai UniversityChangzhou 213022 Department of Physics Harbin Institute of TechnologyHarbin 150001

We investigate the luminescence properties of erbium-doped and erbium-ytterbium-codoped potassium lithium tantalate niobate ceramics prepared by the solid phasesyntheses *** crystalline structure and surface morphology of these ceramics are tested by x-ray diffraction and scanning electron *** green,red,and near-infrared upconversion photoluminescence properties are analyzed by the steady-state spectra under 975 nm and 800 nm *** ceramics arewell sintered and tetragonal tungsten type crystalline structure with the 4 mm point group and the P4bm space *** Er3+upconversion emission integrated intensities increase with theincrease in Er3+ion concentration,while the green emissiondecreases with the increase in Yb^(3+)ion concentration under the 800 nm *** effective energy back transfer process from the Er^(3+)^(4)S_(3/2)state to Yb^(3+)ground state plays an important role in enhancing the red emission and weakening the green emission.

关键词： ceramics crystalline Potassium

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