In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operatio...In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.展开更多
Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal c...Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51976038)the Natural Science Foundation of Jiangsu Province,China for Young Scholars(Grant No.BK20190366)the China Scholarship Council(Grant No.202006090164).
文摘In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.
基金supported by the National Natural Science Foundation of China(Grant 52207180)Xi'an High Voltage Apparatus Research Institute Co.Ltd.(Grant K222301-01)the Anhui Provincial Natural Science Foundation(Grant 2208085UD18).
文摘Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.
