PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the f...PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate,resulting in misjudgment.To improve the efficiency and economy of PV systems,an improved incremental conductance algorithm of MPPT control strategy is proposed.From the traditional incremental conductance algorithm,this algorithm is simple in structure and can discriminate the instantaneous increment of current,voltage and power when the external environment changes,and so can improve tracking efficiency.MATLAB simulations are carried out under rapidly changing solar radiation level,and the results of the improved and conventional incremental conductance algorithm are compared.The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence.It not only optimizes the system,but also improves the efficiency,response speed and tracking efficiency of the PV system,thus ensuring the stable operation of the power grid.展开更多
The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this p...The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.展开更多
Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of m...Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.展开更多
基金The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2019JM-544).
文摘PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate,resulting in misjudgment.To improve the efficiency and economy of PV systems,an improved incremental conductance algorithm of MPPT control strategy is proposed.From the traditional incremental conductance algorithm,this algorithm is simple in structure and can discriminate the instantaneous increment of current,voltage and power when the external environment changes,and so can improve tracking efficiency.MATLAB simulations are carried out under rapidly changing solar radiation level,and the results of the improved and conventional incremental conductance algorithm are compared.The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence.It not only optimizes the system,but also improves the efficiency,response speed and tracking efficiency of the PV system,thus ensuring the stable operation of the power grid.
文摘The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.
基金The National Key R&D Program of China supported this study(2017YFC1700303).
文摘Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.
