The mode-based damping torque analysis(M-DTA)method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper.First,based on the interconnection model betwe...The mode-based damping torque analysis(M-DTA)method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper.First,based on the interconnection model between the system and the controller in the frequency domain,the oscillation loop corresponding to the electromechanical oscillation mode is built,and then the mode-based damping torque of the controller can be calculated.Then,the application of the M-DTA method in the power system is illustrated.The derivation shows that in the single-machine infinite-bus power system,the M-DTA method is completely equivalent to the classical damping torque analysis(C-DTA)method.In the multi-machine power system,the mode-based damping torque directily reflects the effect of the controller on the oscillation mode,overcoming the shortcomings of the C-DTA method in which there is no direct correspondence between the damping torque and the oscillation mode.By deriving the relationship with the residue index,the M-DTA method shows higher accuracy than the residue method in applications,such as controller parameter adjustment.Finally,two example power systems are presented to demonstrate the application of the proposed M-DTA method.Index Terms-Electromechanical oscillation mode,FACTS,interconnection model in the frequency domain,mode-based damping torque analysis(M-DTA),power system low-frequency oscillation,PSS,residue method.展开更多
The major problem in current online diagnosis and analysis for power system oscillation is mainly concerned with finding the oscillation source in a fast and correct way using the data collected by the Wide Area Measu...The major problem in current online diagnosis and analysis for power system oscillation is mainly concerned with finding the oscillation source in a fast and correct way using the data collected by the Wide Area Measurement System(WAMS).This paper for the first time proposes a scheme of cut set energy based on WAMS.Independent of accurate parameters,the scheme can make full use of WAMS data based on cut set energy construction and fast calculation to locate the source during oscillation.Afterwards,a scheme of torque decomposition is proposed,based on which the controller’s torque can be divided into damping torque and synchronous torque by calculation through WAMS data,and this paper puts forward the abnormal response and simulation models calibration of influential controllers.Analysis of an oscillation case shows how the cut set energy scheme and the torque decomposition scheme are applied in a real-world power system,and the schemes are proven to be reliable and practical in identifying and locating oscillation sources.展开更多
This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also pr...This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.U1766202,51907179 and 51977197.
文摘The mode-based damping torque analysis(M-DTA)method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper.First,based on the interconnection model between the system and the controller in the frequency domain,the oscillation loop corresponding to the electromechanical oscillation mode is built,and then the mode-based damping torque of the controller can be calculated.Then,the application of the M-DTA method in the power system is illustrated.The derivation shows that in the single-machine infinite-bus power system,the M-DTA method is completely equivalent to the classical damping torque analysis(C-DTA)method.In the multi-machine power system,the mode-based damping torque directily reflects the effect of the controller on the oscillation mode,overcoming the shortcomings of the C-DTA method in which there is no direct correspondence between the damping torque and the oscillation mode.By deriving the relationship with the residue index,the M-DTA method shows higher accuracy than the residue method in applications,such as controller parameter adjustment.Finally,two example power systems are presented to demonstrate the application of the proposed M-DTA method.Index Terms-Electromechanical oscillation mode,FACTS,interconnection model in the frequency domain,mode-based damping torque analysis(M-DTA),power system low-frequency oscillation,PSS,residue method.
基金supported by the Science and Technology Project of the State Grid Corporation under Grant XT71-16-029。
文摘The major problem in current online diagnosis and analysis for power system oscillation is mainly concerned with finding the oscillation source in a fast and correct way using the data collected by the Wide Area Measurement System(WAMS).This paper for the first time proposes a scheme of cut set energy based on WAMS.Independent of accurate parameters,the scheme can make full use of WAMS data based on cut set energy construction and fast calculation to locate the source during oscillation.Afterwards,a scheme of torque decomposition is proposed,based on which the controller’s torque can be divided into damping torque and synchronous torque by calculation through WAMS data,and this paper puts forward the abnormal response and simulation models calibration of influential controllers.Analysis of an oscillation case shows how the cut set energy scheme and the torque decomposition scheme are applied in a real-world power system,and the schemes are proven to be reliable and practical in identifying and locating oscillation sources.
基金financially supported by Science and Technology Project of Quzhou(Grant Nos.2023K256,2023NC08)Research Grants Program of Department of Education of Zhejiang Province(No.Y202455709)+1 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LZY21E050001)University-Enterprise Cooperation Program for Visiting Engineers in Higher Education Institutions in Zhejiang Province(No.FG2020215).
文摘This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.
