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学者姓名：林琼斌
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Abstract ：
The optimal capacity of energy storage facilities is a cornerstone for the investment and low-carbon operation of integrated energy systems (IESs). However, the intermittence of renewable energy and the different operating characteristics of facilities present challenges to IES configuration. Therefore, a two-stage decision-making framework is developed to optimize the capacity of facilities for six schemes comprised of battery energy storage systems and hydrogen energy storage systems. The objectives considered are to minimize the levelized cost of electricity (LCOE), power abandonment rate (PAR) and maximize self-sufficiency rate (SSR) simultaneously. In the first stage, each scheme is solved using NSGA-II. In the second stage, the weights of objective function are determined by entropy weight method, while the optimal individual is selected from the Pareto solutions by the technique for order preference by similarity to ideal solution approach. Life models of battery, fuel cell, and electrolyzer are introduced to quantify device replacement costs. Meanwhile, carbon trading mechanisms and time-of-use tariffs are considered to assess environmental and economic benefits. The results show that the hydrogen-electric coupling scheme demonstrated superior performance, with LCOE, SSR, and PAR of 0.6416 ¥/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR. © 2024 Elsevier Ltd
Keyword ：
Battery storage Battery storage Carbon Carbon Decision making Decision making Entropy Entropy Fuel cells Fuel cells Genetic algorithms Genetic algorithms Hydrogen storage Hydrogen storage Investments Investments
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| GB/T 7714 | Lin, Liangguang , Ou, Kai , Lin, Qiongbin et al. Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system [J]. | Journal of Energy Storage , 2024 , 97 . |
| MLA | Lin, Liangguang et al. "Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system" . | Journal of Energy Storage 97 (2024) . |
| APA | Lin, Liangguang , Ou, Kai , Lin, Qiongbin , Xing, Jianwu , Wang, Ya-Xiong . Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system . | Journal of Energy Storage , 2024 , 97 . |
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Abstract ：
The optimal capacity of energy storage facilities is a cornerstone for the investment and low-carbon operation of integrated energy systems (IESs). However, the intermittence of renewable energy and the different operating characteristics of facilities present challenges to IES configuration. Therefore, a two-stage decision-making framework is developed to optimize the capacity of facilities for six schemes comprised of battery energy storage systems and hydrogen energy storage systems. The objectives considered are to minimize the levelized cost of electricity (LCOE), power abandonment rate (PAR) and maximize self-sufficiency rate (SSR) simultaneously. In the first stage, each scheme is solved using NSGA-II. In the second stage, the weights of objective function are determined by entropy weight method, while the optimal individual is selected from the Pareto solutions by the technique for order preference by similarity to ideal solution approach. Life models of battery, fuel cell, and electrolyzer are introduced to quantify device replacement costs. Meanwhile, carbon trading mechanisms and time-of-use tariffs are considered to assess environmental and economic benefits. The results show that the hydrogen-electric coupling scheme demonstrated superior performance, with LCOE, SSR, and PAR of 0.6416 & YEN;/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR.
Keyword ：
Battery energy storage system Battery energy storage system Configuration optimization Configuration optimization Entropy weight method Entropy weight method Hydrogen energy storage system Hydrogen energy storage system ideal solution ideal solution Non-dominated sorting genetic algorithm-II Non-dominated sorting genetic algorithm-II Technique for order preference by similarity to Technique for order preference by similarity to
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| GB/T 7714 | Lin, Liangguang , Ou, Kai , Lin, Qiongbin et al. Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system [J]. | JOURNAL OF ENERGY STORAGE , 2024 , 97 . |
| MLA | Lin, Liangguang et al. "Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system" . | JOURNAL OF ENERGY STORAGE 97 (2024) . |
| APA | Lin, Liangguang , Ou, Kai , Lin, Qiongbin , Xing, Jianwu , Wang, Ya-Xiong . Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system . | JOURNAL OF ENERGY STORAGE , 2024 , 97 . |
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Battery state-of-health (SOH) estimation is an effective approach to evaluate battery reliability and reduce maintenance costs for battery-based backup power supply systems. This paper proposes a novel SOH estimation method for batteries, which only uses the response characteristics of load surges and is, therefore, non-destructive to the estimated battery and its system. The discrete wavelet transform (DWT) method based on multi-resolution analysis (MRA) is used for wavelet energy features extraction, and the fuzzy cerebellar model neural network (FCMNN) is introduced to design the battery SOH estimator. The response voltage signals to load surges are used in the training and detection process of the FCMNN. Compared to conventional methods, the proposed method only exploits characteristics of online response signals to the inrush currents rather than injecting interference signals into the battery. The effectiveness of the proposed method is validated by detailed simulation analysis and experiments.
Keyword ：
dynamical battery state-of-health estimation dynamical battery state-of-health estimation fuzzy cerebellar model neural network fuzzy cerebellar model neural network non-invasive detection non-invasive detection response characteristic of load surges response characteristic of load surges wavelet transform wavelet transform
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| GB/T 7714 | Fan, Yuhang , Lin, Qiongbin , Huang, Ruochen . Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges [J]. | ENERGIES , 2024 , 17 (3) . |
| MLA | Fan, Yuhang et al. "Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges" . | ENERGIES 17 . 3 (2024) . |
| APA | Fan, Yuhang , Lin, Qiongbin , Huang, Ruochen . Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges . | ENERGIES , 2024 , 17 (3) . |
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针对传统三相电压源逆变器开路故障诊断方法存在准确率低和鲁棒性差的问题，提出一种用于故障诊断的改进二维卷积神经网络优化方法.该方法首先引入一种新的数据预处理方式，通过马尔可夫变迁场(MTF)将原始时域电压信号数据转换成二维灰度图像，有效保留特征的时空关系；其次，提出采用并行注意力机制对卷积神经网络ResNet18特征提取层提取的特征分别进行通道和空间特征筛选，并完成有效特征融合；最后，融合的特征经ResNet18全连接层和输出层得到故障分类结果.实验结果表明，所提出的改进故障诊断方法能将诊断精度提升至99.80%;在不同噪声条件下均能保持90%以上的分类准确性，验证该方法可有效提高逆变器开路故障诊断性能和鲁棒性.
Keyword ：
ResNet18网络 ResNet18网络 开路故障 开路故障 注意力机制 注意力机制 逆变器 逆变器 马尔可夫变迁场 马尔可夫变迁场
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| GB/T 7714 | 谢泽文 , 陈裕成 , 柴琴琴 et al. 改进残差网络的逆变器开路电路故障诊断 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (01) : 45-52 . |
| MLA | 谢泽文 et al. "改进残差网络的逆变器开路电路故障诊断" . | 福州大学学报(自然科学版) 52 . 01 (2024) : 45-52 . |
| APA | 谢泽文 , 陈裕成 , 柴琴琴 , 林琼斌 , 王武 . 改进残差网络的逆变器开路电路故障诊断 . | 福州大学学报(自然科学版) , 2024 , 52 (01) , 45-52 . |
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Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid waveform control. In this paper, a modeling method for bi-directional DC-AC grid-connected converters based on type-II T-S fuzzy models is proposed, and the corresponding type-II T-S fuzzy control strategy is designed to address the parameter uncertainty and non-linearity issues. Simulation results show that type-II T-S fuzzy control offers superior control performance and better current waveform quality compared to type-I T-S fuzzy control under uncertainty parameter conditions. The effectiveness of the proposed strategy is further validated through a 1 kW prototype of a bi-directional DC-AC converter.
Keyword ：
DC-AC inverters DC-AC inverters dual-buck bi-directional inverter dual-buck bi-directional inverter model building model building nonlinear inductance nonlinear inductance type-II T-S fuzzy model type-II T-S fuzzy model
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| GB/T 7714 | Chen, Zhihua , Huang, Ruochen , Lin, Qiongbin et al. Nonlinear Modeling and Control Strategy Based on Type-II T-S Fuzzy in Bi-Directional DC-AC Converter [J]. | ELECTRONICS , 2024 , 13 (9) . |
| MLA | Chen, Zhihua et al. "Nonlinear Modeling and Control Strategy Based on Type-II T-S Fuzzy in Bi-Directional DC-AC Converter" . | ELECTRONICS 13 . 9 (2024) . |
| APA | Chen, Zhihua , Huang, Ruochen , Lin, Qiongbin , Yu, Xinhong , Dan, Zhimin . Nonlinear Modeling and Control Strategy Based on Type-II T-S Fuzzy in Bi-Directional DC-AC Converter . | ELECTRONICS , 2024 , 13 (9) . |
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Advanced wind power prediction technique plays an essential role in the stable operation of the grid with largescale grid integration of wind power. Most research focuses on distance-based static classification where the subjective nature of initial center selection increases the uncertainty of the prediction. And the data classification on a daily basis neglects the potentially significant climate changes at smaller time scales. To address these issues, the improved snake optimization-long short-term memory (ISO-LSTM) model with Gaussian mixture model (GMM) clustering is proposed to forecast wind power from an adaptive perspective. By exploiting the merits of the probabilistic classification, the K-means optimized GMM clustering enables an appropriate feature modelling for substantial climate changes at smaller time scales. Then the ISO algorithm exhibits higher search accuracy and is better suited for finding hyperparameter combinations for LSTM neural networks. The data from the National Aeronautics and Space Administration (NASA) of the US is used to validate the effectiveness of the proposed method. Compared to the traditional K-means clustering, the K-means optimized GMM clustering has increased accuracy by 2.63 %. Simultaneously, with the adoption of the enhanced ISO algorithm, the accuracy further increases by 7.27 %. Different existing models have also been tested; it shows that the proposed model demonstrates higher prediction accuracy.
Keyword ：
Gaussian mixture model Gaussian mixture model Improved snake optimization Improved snake optimization K -means algorithm K -means algorithm Long short-term memory network Long short-term memory network Probabilistic classification Probabilistic classification
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| GB/T 7714 | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin et al. Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| MLA | Zhou, Yu et al. "Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 157 (2024) . |
| APA | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin , Chai, Qinqin , Wang, Wu . Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
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When high power density of electric vehicle (EV) charging load connected to the microgrids in expressway service areas, the stability of the system is affected. Long-distance transmission or transformer expansion can be costly and difficult to maintain, therefore, this research proposed a dynamic capacity expansion method. Combined with the energy storage system, through the most economic operation, aiming for strong microgrid independence and minimum power fluctuation, a multi-objective model is established to pursue the optimal dispatch scheme by integration of power assets using the Nondominated Sorting Genetic Algorithm II (NSGA-II). To address insufficient diversity for evolutionary operators and slow speed of the original NSGA-II, improvements are made based on the initial dataset optimization and differential evolution. Verified by the simulating results, the proposed method dynamically expands the capacity of the microgrid, thus mitigates the impact of heavy charging load. © 2024 IEEE.
Keyword ：
Electric load dispatching Electric load dispatching Microgrids Microgrids Multiobjective optimization Multiobjective optimization
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| GB/T 7714 | Gao, Sheng , Wang, Zheng , Shen, Yu-Long et al. Dynamic Capacity Expansion Strategy for Expressway Service Area Based on I-NSGA-II Algorithm [C] . 2024 : 324-329 . |
| MLA | Gao, Sheng et al. "Dynamic Capacity Expansion Strategy for Expressway Service Area Based on I-NSGA-II Algorithm" . (2024) : 324-329 . |
| APA | Gao, Sheng , Wang, Zheng , Shen, Yu-Long , Lin, Qiongbin . Dynamic Capacity Expansion Strategy for Expressway Service Area Based on I-NSGA-II Algorithm . (2024) : 324-329 . |
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随着绿色能源的快速发展,海上风电场的规模也越来越大,同时柔性直流也是海上风电场对外传输电能的主要方式.海上风电柔直输电系统在遇日常或故障检修后,需要由换流站等独立完成系统的黑启动.本研究从系统黑启动电源的选择、风电场和换流站的启动方式展开,总结出一种适用于海上风电柔性直流输电系统的黑启动策略,并归纳分析了系统启动过程中注意事项及难点.最后,提出系统黑启动的未来发展方向,为今后海上风电柔直输电系统黑启动的研究和应用提供参考.
Keyword ：
控制方式 控制方式 柔直输电 柔直输电 海上换流站 海上换流站 海上风电 海上风电 黑启动策略 黑启动策略
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| GB/T 7714 | 陈志华 , 林琼斌 , 詹银 et al. 海上风电柔直输电系统黑启动研究进展 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (4) : 547-557 . |
| MLA | 陈志华 et al. "海上风电柔直输电系统黑启动研究进展" . | 福州大学学报（自然科学版） 51 . 4 (2023) : 547-557 . |
| APA | 陈志华 , 林琼斌 , 詹银 , 代妍妍 , 林传伟 . 海上风电柔直输电系统黑启动研究进展 . | 福州大学学报（自然科学版） , 2023 , 51 (4) , 547-557 . |
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Virtual synchronous generator (VSG) control technology can simulate the output characteristics of a synchronous generator. VSG can effectively solve the problem that the inertia and damping support capacity of the grid decreases after a large-scale distributed energy resource is connected to the grid. However, the selection of its control parameters is more complex. The unsuitable control parameters have a great influence on the grid-connection stability of the inverter. In order to solve this problem, the acceleration factor is linearly changed based on Particle Swarm Optimization (PSO) algorithm, and it is used for the optimization calculation of VSG control parameters. The traditional VSG control method and the optimized VSG control method with different optimization algorithms were compared and analyzed by Simulink. The simulation results show the effectiveness and superiority of the improved particle swarm optimization algorithm. © The Institution of Engineering & Technology 2023.
Keyword ：
Electric inverters Electric inverters Energy resources Energy resources Particle swarm optimization (PSO) Particle swarm optimization (PSO) Synchronous generators Synchronous generators Virtual Power Plants Virtual Power Plants
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| GB/T 7714 | Yang, Linjie , Lin, Qiongbin , Huang, Ruochen et al. VSG Control of Grid-connected Inverter Based on Improved PSO [C] . 2023 : 14-17 . |
| MLA | Yang, Linjie et al. "VSG Control of Grid-connected Inverter Based on Improved PSO" . (2023) : 14-17 . |
| APA | Yang, Linjie , Lin, Qiongbin , Huang, Ruochen , Dan, Zhimin , Wang, Yaxiong . VSG Control of Grid-connected Inverter Based on Improved PSO . (2023) : 14-17 . |
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Accurate estimation of the state of health (SOH) of lithium-ion batteries is an important guarantee to ensure safe and reliable operation of lithium-ion battery systems. However, the complex aging mechanism inside the battery makes it difficult to measure the battery SOH directly. In this paper, a SOH estimation method based on a novel dual-stage attention-based recurrent neural network (DARNN) and health feature (HF) extraction from time varying charging process is proposed. Firstly, the constant current charging time, the maximum temperature time, the isochronous voltage difference, and the isochronous current were extracted as lithium-ion battery HFs, and their correlations with SOH are verified by spearman correlation coefficient. Secondly, the DARNN is proposed to capture the time-dependent and temporal features of the input sequence and to accurately predict SOH. Finally, the proposed estimation method is validated on the NASA battery dataset. The results show that the method can accurately estimate SOH for lithium-ion batteries. The mean square error and the mean absolute percentage error of the method are <0.5 %.
Keyword ：
Dual -stage attention -based recurrent neural Dual -stage attention -based recurrent neural Lithium-ion battery Lithium-ion battery network network State of health State of health
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| GB/T 7714 | Hong, Jiangnan , Chen, Yucheng , Chai, Qinqin et al. State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network [J]. | JOURNAL OF ENERGY STORAGE , 2023 , 72 . |
| MLA | Hong, Jiangnan et al. "State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network" . | JOURNAL OF ENERGY STORAGE 72 (2023) . |
| APA | Hong, Jiangnan , Chen, Yucheng , Chai, Qinqin , Lin, Qiongbin , Wang, Wu . State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network . | JOURNAL OF ENERGY STORAGE , 2023 , 72 . |
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