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学者姓名：林琼斌
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Abstract ：
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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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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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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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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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 ：
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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During the planning and construction process of a multi-energy coupled microgrid, various risks may be encountered. Conducting a reasonable risk analysis and evaluation for its planning and construction can help avoid unnecessary losses. This article first provides a detailed analysis of the risks faced in the planning and construction of multi-energy coupled microgrids and establishes a risk assessment index system. Then, it proposes an evaluation method for the planning risks of multi-energy coupled microgrids. This method calculates the weights of risk indicators using the analytic hierarchy process (AHP). Via the risk matrix method combined with Delphi method, the comprehensive risk value can be calculated for the planning and construction of multi-energy coupled microgrids. With the proposed method, comprehensive risk assessments are performed on two case studies to demonstrate the feasibility of the method. By comparing the risk indicator scoring results of the two planned cases, the characteristics of multi-energy coupled microgrids are revealed. © 2023 IEEE.
Keyword ：
Analytic hierarchy process Analytic hierarchy process Decision making Decision making Risk analysis Risk analysis Risk assessment Risk assessment
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| GB/T 7714 | Yang, Yubin , Lin, Qiongbin , Huang, Ruochen et al. Risk Analysis and Assessment for Multi-Energy Coupled Microgrid Planning [C] . 2023 . |
| MLA | Yang, Yubin et al. "Risk Analysis and Assessment for Multi-Energy Coupled Microgrid Planning" . (2023) . |
| APA | Yang, Yubin , Lin, Qiongbin , Huang, Ruochen , Li, Yi , Liu, Jie , Liu, Changsha . Risk Analysis and Assessment for Multi-Energy Coupled Microgrid Planning . (2023) . |
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The wide use of Li-ion batteries in various industrial applications has led to a focus on their health status. This paper proposes a novel fast SOH estimation method by combining the response characteristics and the Genetic Algorithm-Extreme Learning Machine (GA-ELM) model. Based on the theoretical manipulations, the dynamic response due to the inrush current has been derived and shows that the response curve due to the inrush current contains information related to the battery SOH. Since there is no explicit relation, GA-ELM with the wavelet transform method is used to extract effective dynamic response voltage data features to estimate SOH without manually adjusting parameters. Experiment results validate that the fast estimation of the battery SOH can be achieved by using the proposed method with a resolution accuracy of 3% in any state of charge (SOC). Compared with other models by the crossover analysis, the method is efficient for feature extraction while providing better performance. This paper aims to highlight the use of load surges response characteristics-to estimate SOH with no additional sensors. It shows the application has a good prospect in the electric vehicles (EVs). © 2023 IEEE.
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| GB/T 7714 | Fan, Yuhang , Lin, Qiongbin , Huang, Ruochen et al. Fast Battery SOH Estimation Based on Response Characteristics of Load Surges and GA-ELM [C] . 2023 . |
| MLA | Fan, Yuhang et al. "Fast Battery SOH Estimation Based on Response Characteristics of Load Surges and GA-ELM" . (2023) . |
| APA | Fan, Yuhang , Lin, Qiongbin , Huang, Ruochen , Lin, Yufeng , Wang, Jia , Huang, Qingrong . Fast Battery SOH Estimation Based on Response Characteristics of Load Surges and GA-ELM . (2023) . |
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当输入串联输出并联（ISOP）型双有源桥（DAB）DC-DC变换器系统各模块参数不同时，系统存在传输功率不均衡导致的过电压过电流现象，严重时可能损坏器件，此外变换器运行时还需考虑其电流应力与动态性能。针对这些问题，基于双重移相提出一种结合电流应力优化的电压均衡控制方案。分析双重移相下变换器的电流应力与传输功率模型，并通过KKT(Karush-Kuhn-Tucker)条件法求取电流应力最优的移相比公式。在电流应力优化的基础上提出一种电压均衡控制方法，通过传输功率模型推导桥间移相比表达式，引入均压分量和动态传输功率，实现输入电压均衡、输出电压稳定以及提高动态性能。最后，搭建双模块ISOP-DAB实验平台进行对比实验，验证所提控制方法的有效性。
Keyword ：
双有源桥DC-DC变换器 双有源桥DC-DC变换器 双重移相 双重移相 微电网 微电网 电压均衡 电压均衡 电流应力优化 电流应力优化
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| GB/T 7714 | 蔡逢煌 , 林俊腾 , 江加辉 et al. 结合电流应力优化的ISOP-DAB变换器电压均衡控制 [J]. | 太阳能学报 , 2023 , 44 (10) : 90-96 . |
| MLA | 蔡逢煌 et al. "结合电流应力优化的ISOP-DAB变换器电压均衡控制" . | 太阳能学报 44 . 10 (2023) : 90-96 . |
| APA | 蔡逢煌 , 林俊腾 , 江加辉 , 王武 , 林琼斌 . 结合电流应力优化的ISOP-DAB变换器电压均衡控制 . | 太阳能学报 , 2023 , 44 (10) , 90-96 . |
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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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