To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of H...To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of Henan Dispatcher Training Simulator (HNDTS) base on its exist SCADA/EMS. In order to i ntegrated with exist SCADA/EMS, the integration method and technique are propose d. Graph data integration discussed with emphasis. After integration implemented , HNDTS can share all data with SCADA/EMS and dispatchers can be trained in same environment as real work situation, in the same time it can avoid amout of work of maintenance engineers. Both advantages and disadvantages of integration are analyzed. In the end of paper, the requirement for future DTS is put forward bas e on the experience of author.展开更多
A fault management dispatcher training simulator for large-scale Distribution Automation System (TDAS) is developed to train operators in distribution control center. This simulator is composed of independent simulati...A fault management dispatcher training simulator for large-scale Distribution Automation System (TDAS) is developed to train operators in distribution control center. This simulator is composed of independent simulation server and operator consoles and can be used for network analysis, network operation, fault management and evaluation. TDAS DB is duplicated online to the simulation server keeping the data security. The system can model distribution network penetrated with distributed generations (DG) using the real data from the TDAS DB. Network fault scenarios are automatically generated by calculating fault current and generating fault indicators. Also, manual entry of cry wolf alarm is available. Moreover, operation solution for scenario of fault isolation and service restoration is generated automatically so that trainee can check their operation result. Operator actions during training session are saved and can be played back as well as displayed on one-line diagram pictures.展开更多
Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and a...Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.展开更多
文摘To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of Henan Dispatcher Training Simulator (HNDTS) base on its exist SCADA/EMS. In order to i ntegrated with exist SCADA/EMS, the integration method and technique are propose d. Graph data integration discussed with emphasis. After integration implemented , HNDTS can share all data with SCADA/EMS and dispatchers can be trained in same environment as real work situation, in the same time it can avoid amout of work of maintenance engineers. Both advantages and disadvantages of integration are analyzed. In the end of paper, the requirement for future DTS is put forward bas e on the experience of author.
文摘A fault management dispatcher training simulator for large-scale Distribution Automation System (TDAS) is developed to train operators in distribution control center. This simulator is composed of independent simulation server and operator consoles and can be used for network analysis, network operation, fault management and evaluation. TDAS DB is duplicated online to the simulation server keeping the data security. The system can model distribution network penetrated with distributed generations (DG) using the real data from the TDAS DB. Network fault scenarios are automatically generated by calculating fault current and generating fault indicators. Also, manual entry of cry wolf alarm is available. Moreover, operation solution for scenario of fault isolation and service restoration is generated automatically so that trainee can check their operation result. Operator actions during training session are saved and can be played back as well as displayed on one-line diagram pictures.
基金supported in part by the National Natural Science Foundation of China under Grant 62203468in part by the Technological Research and Development Program of China State Railway Group Co.,Ltd.under Grant Q2023X011+1 种基金in part by the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)under Grant 2022QNRC001in part by the Youth Talent Program Supported by China Railway Society,and in part by the Research Program of China Academy of Railway Sciences Corporation Limited under Grant 2023YJ112.
文摘Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.
