随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法...随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法。首先,选取不同的历史时间断面,利用拓扑参数和量测信息生成数据集,通过将双向长短期记忆网络引入生成对抗网络填补数据中的缺失量测信息;其次,利用图注意力网络自适应地捕捉节点间的空间动态关系,利用双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络充分挖掘不同时间断面序列信息的时间耦合关系,拼接形成关于量测量到状态量的时空特征表达,得到改进图神经网络状态估计模型;最后,在IEEE 118节点系统中进行仿真实验,并与卷积神经网络、图注意力网络等算法进行对比。结果表明,文中所提算法在数据缺失和拓扑时变情况下具有更优的估计效果。展开更多
Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited compreh...Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.展开更多
Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance ...Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.展开更多
针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后...针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后在处理好的环境变量训练数据集上进行训练,得到大棚环境变量预测模型;将LSTM模型与传统RNN和GRU预测模型进行对比实验。实验结果表明,LSTM模型的预测精度更高,鲁棒性更强,预测结果的均方根误差(root mean square error,RMSE)低于0. 05,可以实现大棚环境变量的准确预测,为大棚的智能控制提供可靠依据。展开更多
文摘随着分布式新能源、可控资源等新型元素接入配电网,传统状态估计模型面临量测信息不全、配电网拓扑变化频繁和负荷时序性波动等新问题,模型估计精度降低。针对该问题,文中提出一种融合改进生成对抗与图注意力网络的配电网状态估计方法。首先,选取不同的历史时间断面,利用拓扑参数和量测信息生成数据集,通过将双向长短期记忆网络引入生成对抗网络填补数据中的缺失量测信息;其次,利用图注意力网络自适应地捕捉节点间的空间动态关系,利用双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络充分挖掘不同时间断面序列信息的时间耦合关系,拼接形成关于量测量到状态量的时空特征表达,得到改进图神经网络状态估计模型;最后,在IEEE 118节点系统中进行仿真实验,并与卷积神经网络、图注意力网络等算法进行对比。结果表明,文中所提算法在数据缺失和拓扑时变情况下具有更优的估计效果。
基金partly supported by Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowships for Research in Japan (P22370)by Key Project of Jiangsu Province (BE2022029) in China。
文摘Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.
文摘Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.
文摘针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后在处理好的环境变量训练数据集上进行训练,得到大棚环境变量预测模型;将LSTM模型与传统RNN和GRU预测模型进行对比实验。实验结果表明,LSTM模型的预测精度更高,鲁棒性更强,预测结果的均方根误差(root mean square error,RMSE)低于0. 05,可以实现大棚环境变量的准确预测,为大棚的智能控制提供可靠依据。
