In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the m...In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.展开更多
Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizont...Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.展开更多
The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damag...The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.展开更多
To improve accuracy and efficiency in power systems dynamic modeling,the distributed online modeling approach is a good option.In this approach,the power system is divided into sub-grids,and the dynamic models of the ...To improve accuracy and efficiency in power systems dynamic modeling,the distributed online modeling approach is a good option.In this approach,the power system is divided into sub-grids,and the dynamic models of the sub-grids are built independently within the distributed modeling system.The subgrid models are subsequently merged,after which the dynamic model of the whole power system is finally constructed online.The merging of the networks plays an important role in the distributed online dynamic modeling of power systems.An efficient multi-area networks-merging model that can rapidly match the boundary power flow is proposed in this paper.The iterations of the boundary matching during network merging are eliminated due to the introduction of the merging model,and the dynamic models of the sub-grid can be directly“plugged in”with each other.The results of the calculations performed in a real power system demonstrate the accuracy of the integrated model under both steady and transient states.展开更多
Approaches to apply graph computing to power grid analysis are systematically explained using real-world application examples.Through exploring the nature of the power grid and the characteristics of power grid analys...Approaches to apply graph computing to power grid analysis are systematically explained using real-world application examples.Through exploring the nature of the power grid and the characteristics of power grid analysis,the guidelines for selecting appropriate graph computing techniques for the application to power grid analysis are outlined.A custom graph model for representing the power grid for the analysis and simulation purpose and an in-memory computing(IMC)based graph-centric approach with a shared-everything architecture are introduced.Graph algorithms,including network topology processing and subgraph processing,and graph computing application scenarios,including in-memory computing,contingency analysis,and Common Information Model(CIM)model merge,are presented.展开更多
基金The Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX11_0143)
文摘In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.
基金This research was funded by the China Scholarship Council(CSC)and partially supported by the Project 911(Vietnam).The data analysis was carried out as a part of the second author’s PhD studies at the School of Geodesy and Geomatics,Wuhan University,People’s Republic of China[grant number 2011GXZN02].
文摘Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.
基金financially supported by the General Program of National Natural Science Foundation of China(No.62373069)the Major Projects for Technological Transformation(No.H20201555)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project (No.CQYC202203091061)。
文摘The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.
基金This work was supported by the National Key Basic Research Program of China(973 Program)(2013CB228204)the National Natural Science Foundation of China(51137002,51190102,51407060).
文摘To improve accuracy and efficiency in power systems dynamic modeling,the distributed online modeling approach is a good option.In this approach,the power system is divided into sub-grids,and the dynamic models of the sub-grids are built independently within the distributed modeling system.The subgrid models are subsequently merged,after which the dynamic model of the whole power system is finally constructed online.The merging of the networks plays an important role in the distributed online dynamic modeling of power systems.An efficient multi-area networks-merging model that can rapidly match the boundary power flow is proposed in this paper.The iterations of the boundary matching during network merging are eliminated due to the introduction of the merging model,and the dynamic models of the sub-grid can be directly“plugged in”with each other.The results of the calculations performed in a real power system demonstrate the accuracy of the integrated model under both steady and transient states.
基金supported by National Natural Science Foundation of China under the Grant U1766214.
文摘Approaches to apply graph computing to power grid analysis are systematically explained using real-world application examples.Through exploring the nature of the power grid and the characteristics of power grid analysis,the guidelines for selecting appropriate graph computing techniques for the application to power grid analysis are outlined.A custom graph model for representing the power grid for the analysis and simulation purpose and an in-memory computing(IMC)based graph-centric approach with a shared-everything architecture are introduced.Graph algorithms,including network topology processing and subgraph processing,and graph computing application scenarios,including in-memory computing,contingency analysis,and Common Information Model(CIM)model merge,are presented.
