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学者姓名：林培杰
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Abstract ：
光伏电站功率数据存在随机性和波动性的特征，研究精准的光伏电站功率预测模型成为未来电力发展中灵活的电力调度和管理的必要条件。对此提出一种基于混合DBSCAN聚类、PCA主成分分析和改进自注意力机制的光伏功率预测模型。首先采用DBSCAN聚类将分布较为分散和密集的历史数据进行分类，得到波动数据集和平稳数据集；其次利用PCA提取波动数据的主要成分序列，得到便于模型获得关键信息的时间序列；最后提取关键气象参数与具有感知上下文信息的改进自注意力机制模型进行互助式的动态建模。实验运用RMSE和MAE两个指标说明本文所提模型在每个季节下的平稳数据和波动数据都有较高的预测精度。
Keyword ：
DBSCAN聚类 DBSCAN聚类 PCA分析法 PCA分析法 光伏功率预测 光伏功率预测 自注意力机制 自注意力机制
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| GB/T 7714 | 李祺彬 , 卢箫扬 , 林培杰 et al. 基于DBSCAN-PCA和改进自注意力机制的光伏功率预测 [J]. | 电气开关 , 2024 , 62 (01) : 6-12 . |
| MLA | 李祺彬 et al. "基于DBSCAN-PCA和改进自注意力机制的光伏功率预测" . | 电气开关 62 . 01 (2024) : 6-12 . |
| APA | 李祺彬 , 卢箫扬 , 林培杰 , 程树英 , 陈志聪 . 基于DBSCAN-PCA和改进自注意力机制的光伏功率预测 . | 电气开关 , 2024 , 62 (01) , 6-12 . |
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为了克服光伏发电固有的间断性和波动性对电网稳定性的负面影响，提出一种二维灰度关联分析-双向长短期记忆神经网络(two-dimensional grey relational analysis and bidirectional long short-term memory network, 2DGRA-BiLSTM)模型，用于实现日前光伏功率曲线预测，以更好指导电网调度.不同于以往的点预测，本研究将日功率曲线作为整体进行预测.首先用2DGRA实现最佳历史相似日数据的获取；其次，根据日功率曲线的波动性将总数据分为3类；最后，根据3种分类，分别训练3种BiLSTM模型对日功率曲线进行预测.所提出的预测模型通过沙漠知识澳大利亚太阳能中心历史气象和功率数据进行训练，并通过数值天气预报和功率数据进行测试.对比其他几种神经网络模型，实验表明所提出模型具有更好的综合预测性能，在晴空、轻度非晴空和重度非晴空条件下，决定系数(R~2)分别为0.994、0.940和0.782.
Keyword ：
二维灰度关联分析 二维灰度关联分析 光伏功率 光伏功率 双向长短期记忆神经网络 双向长短期记忆神经网络 日前预测 日前预测
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| GB/T 7714 | 陈柏恒 , 陈志聪 , 吴丽君 et al. 利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (01) : 20-28 . |
| MLA | 陈柏恒 et al. "利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法" . | 福州大学学报(自然科学版) 52 . 01 (2024) : 20-28 . |
| APA | 陈柏恒 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法 . | 福州大学学报(自然科学版) , 2024 , 52 (01) , 20-28 . |
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为了解决局部阴影下传统最大功率点追踪(maximum power point tracking, MPPT)算法容易陷入局部最优从而降低光伏系统发电效率的问题，本研究提出融合正弦余弦算法和自适应策略的布谷鸟优化算法(cuckoo search algorithm fusing sine cosine algorithm and adaptive strategy, AFCS),并应用于光伏全局MPPT控制中，以改善其收敛速度与追踪精度.设置多种光照情况，并与扰动观察法、花朵授粉算法和粒子群算法进行对比.经过Matlab/Simulink仿真验证，表明本算法拥有较快的收敛速度和较高的追踪精度，在各个光照条件下均能快速追踪到光伏阵列最大功率点，可以有效提高光伏系统的发电效率.
Keyword ：
光伏阵列 光伏阵列 局部阴影 局部阴影 最大功率点追踪 最大功率点追踪 自适应策略 自适应策略 融合算法 融合算法
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| GB/T 7714 | 张致用 , 陈志聪 , 吴丽君 et al. 利用改进布谷鸟优化算法的光伏全局MPPT方法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (02) : 139-146 . |
| MLA | 张致用 et al. "利用改进布谷鸟优化算法的光伏全局MPPT方法" . | 福州大学学报(自然科学版) 52 . 02 (2024) : 139-146 . |
| APA | 张致用 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用改进布谷鸟优化算法的光伏全局MPPT方法 . | 福州大学学报(自然科学版) , 2024 , 52 (02) , 139-146 . |
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针对目前大部分光伏功率预测模型采用批量离线训练方式，且新建光伏电站训练数据较少的问题，本文提出一种基于增量学习的卷积神经网络（CNN）和长短期记忆（LSTM）网络结合的光伏功率预测模型。首先，采用CNN对气象数据进行特征提取，并通过LSTM网络进行功率预测，以此CNN-LSTM混合模型进行背景学习，训练出可用于增量学习的基准模型。其次，根据不同的时间跨度进行增量学习训练，实现模型的在线更新。针对增量学习中的灾难性遗忘问题，采用弹性权重整合（EWC）算法和在线弹性整合（Online＿EWC）算法进行缓解。实验结果表明，相较于无约束的增量学习，采用EWC和Online＿EWC方法的增量学习可以明显缓解灾难性遗忘问题，降低预测平均绝对误差（MAE）和均方根误差（RMSE）；同时，在保证预测精度的前提下，增量学习的耗时大幅低于传统的批量学习。
Keyword ：
光伏功率预测 光伏功率预测 增量学习 增量学习 弹性权重整合(EWC)算法 弹性权重整合(EWC)算法 长短期记忆(LSTM)网络 长短期记忆(LSTM)网络
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| GB/T 7714 | 严璐晗 , 林培杰 , 程树英 et al. 基于增量学习的CNN-LSTM光伏功率预测 [J]. | 电气技术 , 2024 , 25 (05) : 31-40 . |
| MLA | 严璐晗 et al. "基于增量学习的CNN-LSTM光伏功率预测" . | 电气技术 25 . 05 (2024) : 31-40 . |
| APA | 严璐晗 , 林培杰 , 程树英 , 陈志聪 , 卢箫扬 . 基于增量学习的CNN-LSTM光伏功率预测 . | 电气技术 , 2024 , 25 (05) , 31-40 . |
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针对传统参数辨识方法中存在的易陷入局部最优和精度低问题，提出一种改进洗牌复杂演化算法(shuffed complex evolution, SCE).首先，提出描述电池的动态特性的二阶RC等效电路模型，并根据恒流放电工况测试数据集进行锂离子电池等效模型确定待辨识参数.其次，将模型模拟端电压值与电池真实测试端电压均方根误差作为目标函数，并通过所提出的优化算法来寻找模型最优参数.最后，使用DST、 FUDS的锂离子电池动态工况数据集进行仿真验证，并与粒子群算法、灰狼算法、遗传算法进行比较.仿真结果表明，本方法在辨识精度方面具有优势，算法的参数辨识均方根误差(E_(RMS))平均值是0.016 6 V,相比较其他优化算法，分别降低了7.8%、 8.3%、 14.9%.
Keyword ：
参数辨识 参数辨识 洗牌复杂演化算法 洗牌复杂演化算法 等效电路模型 等效电路模型 锂离子电池 锂离子电池
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| GB/T 7714 | 许雅玲 , 陈志聪 , 吴丽君 et al. 利用改进SCE算法的锂离子电池参数辨识 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (02) : 147-154 . |
| MLA | 许雅玲 et al. "利用改进SCE算法的锂离子电池参数辨识" . | 福州大学学报(自然科学版) 52 . 02 (2024) : 147-154 . |
| APA | 许雅玲 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用改进SCE算法的锂离子电池参数辨识 . | 福州大学学报(自然科学版) , 2024 , 52 (02) , 147-154 . |
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本发明涉及一种基于长短期记忆神经网络的智慧路灯控制方法。从气象网站获取路灯所在地的湿度、风速、PM2.5、PM10等气象数据，并利用照度传感器采集照度信息，以此作为智慧路灯控制的样本数据集；对每个样本信号进行归一化处理；调用长短期记忆神经网络算法，以湿度、风速、PM2.5、PM10作为模型的输入特征，能见度作为模型的输出，构建能见度检测算法模型；结合能见度检测算法模型所得的能见度情况与当前的照度情况构建路灯控制策略；根据所构建的路灯控制策略，在高能见度时，采用普通亮度与高色温照明模式，节约能源；在低能见度时输出更高的亮度与更低的色温，增强路灯透雾能力。本发明能够实现不同能见度下的路灯自适应调光。
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| GB/T 7714 | 林培杰 , 程树英 , 章杰 et al. 一种基于长短期记忆神经网络的智慧路灯控制方法 : CN202210682896.0[P]. | 2022-06-15 00:00:00 . |
| MLA | 林培杰 et al. "一种基于长短期记忆神经网络的智慧路灯控制方法" : CN202210682896.0. | 2022-06-15 00:00:00 . |
| APA | 林培杰 , 程树英 , 章杰 , 郑伟彬 , 陈志聪 , 吴丽君 et al. 一种基于长短期记忆神经网络的智慧路灯控制方法 : CN202210682896.0. | 2022-06-15 00:00:00 . |
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As photovoltaic (PV) arrays are exposed to the outdoors year-round, they are susceptible to various faults. The shading condition, degradation or dust coverage can make fault signals more complex, forming compound faults. These faults can lead to a large loss of power generation or irreversible damage to the PV modules, and even fires in severe cases. Moreover, unknown fault types that have never been seen in the training set may occur at actual working conditions. Therefore, accurate diagnosis of various types of single and compound faults (closed-set faults) by considering the identification of unknown faults, namely open-set faults diagnosis, is crucial to improve the efficiency of operation and maintenance. A 1D VoVNet-SVDD based open-set fault diagnosis model for PV arrays is proposed. The model is a two-stage network model consisting of a 1D VoVNet network and a multi-classification Support Vector Data Description (SVDD) in series. The 1D VoVNet network automatically extracts fault features from the input original I-V curve data. These extracted fault features are then combined with environmental parameters to construct the SVDD model. The SVDD identifies known fault types by con-structing a hypersphere for each fault type. Fault types that are not classified into any of the hyperspheres are considered as unknown faults, enabling open-set diagnosis. The experimental results show that the proposed model can accurately classify the closed-set faults among the three designed testing tasks while identify unknown type faults. The comparison demonstrates that the proposed algorithm is superior to the compared models.
Keyword ：
Compound faults Compound faults Deep learning Deep learning Fault diagnosis Fault diagnosis Open -set Open -set Photovoltaic arrays Photovoltaic arrays
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| GB/T 7714 | Lin, Peijie , Guo, Feng , Lu, Xiaoyang et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults [J]. | SOLAR ENERGY , 2023 , 267 . |
| MLA | Lin, Peijie et al. "A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults" . | SOLAR ENERGY 267 (2023) . |
| APA | Lin, Peijie , Guo, Feng , Lu, Xiaoyang , Zheng, Qianying , Cheng, Shuying , Lin, Yaohai et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults . | SOLAR ENERGY , 2023 , 267 . |
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Bruising is one of the key factors that causes postharvest losses, which decreases the economic efficiency of fruit. Nevertheless, the detection of bruises still relies mainly on manual work, which is strongly subjective with long labor time and low efficiency. Accordingly, it is necessary to design an efficient fruit bruise detection approach. Thermal imaging (TI) is a fast and effective nondestructive testing technology. However, the commonly applied thermal excitation TI-based bruise detection may lead to a decrease in the shelf life of the fruit. Therefore, this study uses apple as the research object, introduces cold excitation to improve the sensitivity of bruise detection, and then constructs a simple longwavelength infrared range (7.5-13 mu m) TI system to acquire the thermal image of bruised apples. In addition, the low signal-to-noise ratio of thermal images also leads to detection performance degradation. Thus, the YOLOv5s network is applied and improved to achieve better detection. The specific methods are described as follows: (1) Since the thermal images have the problem of duplicated RGB data, group convolution is used to reduce the feature duplication computation. (2) The bottleneck structure of YOLOv5s is replaced by the ghost bottleneck (GB), and the number of bottlenecks is reduced to decrease the computational quantity of extracting redundant features of thermal images. (3) The shrinkage module is inserted into the GB, and the threshold is automatically obtained through two fully connected layers without relevant professional knowledge to eliminate noise in the features that may cause performance degradation. The F2 score, mAP and mAP50 of the proposed model are 97.76%, 86.24% and 98.08%, respectively, which are better than those of YOLOv5s. Moreover, the computation and the FPS of the proposed model are 1.31 GFLOPs and 160, which are 31.95% and 121.21% of those of the YOLOv5s, respectively.
Keyword ：
Apple Apple Bruise detection Bruise detection Cold excitation Cold excitation Thermal imaging Thermal imaging YOLOv5s YOLOv5s
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| GB/T 7714 | Lin, Peijie , Yang, Hua , Cheng, Shuying et al. An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images [J]. | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
| MLA | Lin, Peijie et al. "An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images" . | POSTHARVEST BIOLOGY AND TECHNOLOGY 199 (2023) . |
| APA | Lin, Peijie , Yang, Hua , Cheng, Shuying , Guo, Feng , Wang, Lijin , Lin, Yaohai . An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images . | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
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Accurate faults diagnosis for photovoltaic (PV) array is one of the vital factors that guarantee the reliable operation of PV power plant. Artificial intelligence (AI) based fault detection and diagnosis (FDD) models are promising techniques. In order to automatically extract the faults features from the raw electrical data of PV array and create efficient FDD model with small dataset, a FDD scheme using Wasserstein generative adversarial network (WGAN) and convolutional neural network (CNN) is designed. The proposed FDD model is consisting of three modules, a discriminator, a generator and a classifier for fault diagnosis. By analyzing sequential PV data in a 2-Dimension way, the proposed discriminator and generator learn the distribution of PV data under various PV system operations. Then they are utilized to generate more labeled samples to improve the performance of the CNN based classifier. Thus, the proposed FDD model can be trained only requiring minor labeled samples. A laboratory grid-connected PV system is established to experimentally investigate the performance of the developed method. The results demonstrate that the designed FDD model can accurately diagnose line-line and open circuit faults.
Keyword ：
Convolutional Neural Network Convolutional Neural Network Deep Learning Deep Learning Faults Diagnosis Faults Diagnosis Generative Adversarial Network Generative Adversarial Network Photovoltaic Array Photovoltaic Array
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| GB/T 7714 | Lu, Xiaoyang , Lin, Yaohai , Lin, Peijie et al. Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset [J]. | SOLAR ENERGY , 2023 , 253 : 360-374 . |
| MLA | Lu, Xiaoyang et al. "Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset" . | SOLAR ENERGY 253 (2023) : 360-374 . |
| APA | Lu, Xiaoyang , Lin, Yaohai , Lin, Peijie , He, Xiangjian , Fang, Gengfa , Cheng, Shuying et al. Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset . | SOLAR ENERGY , 2023 , 253 , 360-374 . |
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提出一种基于伪标签-1D DenseNet-KNN的光伏阵列故障诊断方法,实现在少标签样本下的光伏阵列复合故障开集识别.首先,分析各种常见单一故障和灰尘覆盖下复合故障的I-V特性曲线;然后,为克服常规半监督机器学习算法需手动提取数据特征的问题,采用一种伪标签与1D DenseNet相结合的半监督方法自动提取特征;最后,将从训练数据中提取的特征、训练数据预测的标签和测试样本提取的特征输入KNN算法并进行开集复合故障诊断.实验表明,该方法不仅能准确分类各种已知类别样本,还能识别出未知类别故障,且模型训练只需要少量的标签数据.
Keyword ：
I-V特性曲线 I-V特性曲线 KNN算法 KNN算法 伪标签半监督学习 伪标签半监督学习 光伏阵列 光伏阵列 开集识别 开集识别 故障诊断 故障诊断
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| GB/T 7714 | 陈泽理 , 卢箫扬 , 林培杰 et al. 基于伪标签-1D DenseNet-KNN的光伏阵列开集复合故障诊断方法 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (4) : 490-497 . |
| MLA | 陈泽理 et al. "基于伪标签-1D DenseNet-KNN的光伏阵列开集复合故障诊断方法" . | 福州大学学报（自然科学版） 51 . 4 (2023) : 490-497 . |
| APA | 陈泽理 , 卢箫扬 , 林培杰 , 赖云锋 , 程树英 , 陈志聪 et al. 基于伪标签-1D DenseNet-KNN的光伏阵列开集复合故障诊断方法 . | 福州大学学报（自然科学版） , 2023 , 51 (4) , 490-497 . |
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