Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

DNA Sequence Classification Based on the Side Chain Radical Polarity of Amino Acids

The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.

Optimization of satellite searching strategy of the non-stationary antenna

The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application,the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.

Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems

The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering （HC） and self-organizing map neural networks （SOMNNs） are shown to provide robust and user-friendly tools for application to industrial gas turbine （IGT） systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps （CMs） that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.

Two-level hierarchical feature learning for image classification

In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.