Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribu...Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.展开更多
Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinat...Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target,offering high accuracy and efficiency.As a result,it is expected to be used in applications requiring rapid,large-scale measurements,such as main cable line shape measurement for suspension bridges.However,due to the large span and tall main towers of suspension bridges,the LiDAR field of view often encounters obstructions,making it difficult to obtain high-quality point clouds for the entire bridge.The collected point clouds are typically unevenly distributed and of poor quality.Therefore,LiDAR is used to monitor the local cable line shape.This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change.Additionally,the Random Sample Consensus(RANSAC)algorithm,the ordinary least squares,and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable.Given the strong design prior information available during suspension bridge construction,Bayesian theory is applied to predict and adjust the global line shape of the main cable.The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data,significantly enhancing data collection efficiency.The method proposed in this paper offers advantages such as highly automated,low risk,low cost,and sustainability,making it suitable for green monitoring throughout the entire main cable construction process.展开更多
基金supported by the National Key Research and Development Program under Grant 2017YFB0902900 and Grant 2017YFB0902902。
文摘Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.
基金funded by the 2024 STCSM Shanghai Natural Science Grants General Project"Online Intelligent Perception and Warning of Large span Structural Vortex Vibration Based on Structural Health Monitoring"and the Science and Technology Project"Research on Intelligent Monitoring System Scheme for Large span Bridges in Mountainous Areas"of PowerChina Road Bridge Group Co.,Ltd.
文摘Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target,offering high accuracy and efficiency.As a result,it is expected to be used in applications requiring rapid,large-scale measurements,such as main cable line shape measurement for suspension bridges.However,due to the large span and tall main towers of suspension bridges,the LiDAR field of view often encounters obstructions,making it difficult to obtain high-quality point clouds for the entire bridge.The collected point clouds are typically unevenly distributed and of poor quality.Therefore,LiDAR is used to monitor the local cable line shape.This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change.Additionally,the Random Sample Consensus(RANSAC)algorithm,the ordinary least squares,and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable.Given the strong design prior information available during suspension bridge construction,Bayesian theory is applied to predict and adjust the global line shape of the main cable.The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data,significantly enhancing data collection efficiency.The method proposed in this paper offers advantages such as highly automated,low risk,low cost,and sustainability,making it suitable for green monitoring throughout the entire main cable construction process.
