Interval state estimation(ISE)can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements,thereby analyzing the impact of uncertain pseudo-measurements on states.Ho...Interval state estimation(ISE)can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements,thereby analyzing the impact of uncertain pseudo-measurements on states.However,predicted pseudo-measurements have prediction errors,and their confidence intervals do not necessarily contain the truth values,leading to estimation biases of the ISE.To solve this problem,this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression(GPR)model,thereby improving the prediction accuracy of pseudo-measurement confidence intervals.Besides,a weight assignment strategy for improving the robustness of weighted least squares(WLS)ISE is proposed.This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations,thereby improving the estimation accuracy and robustness of the ISE.This paper adopts the data from the supervisory control and data acquisition(SCADA)system of the New York Independent System Operator(NYISO).It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods.In addition,this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case.Finally,the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.展开更多
Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system s...Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.展开更多
Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in thr...Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in three-phase networks undermine the reliability of existing methods in practical applications.To address these issues,this paper proposes an interval harmonic state estimation method in three-phase unbalanced distribution networks,integrating data from multiple sources.First,the interval multi-source harmonic measurement dataset is constructed by integrating asynchronous harmonic measurement data from multiple sources.The time asynchrony of measurement data from power quality monitoring devices is calibrated using the sliding window weighted dynamic time warping algo-rithm.Second,the interval harmonic state estimation model for the three-phase asymmetric distribution network is constructed.The model is solved using the interval-weighted least squares method,enhanced by the improved Krawczyk operator,thereby minimizing the expansion resulting from interval operations.Finally,the feasibility and accuracy of the proposed interval harmonic state estimation method are validated.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51677012).
文摘Interval state estimation(ISE)can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements,thereby analyzing the impact of uncertain pseudo-measurements on states.However,predicted pseudo-measurements have prediction errors,and their confidence intervals do not necessarily contain the truth values,leading to estimation biases of the ISE.To solve this problem,this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression(GPR)model,thereby improving the prediction accuracy of pseudo-measurement confidence intervals.Besides,a weight assignment strategy for improving the robustness of weighted least squares(WLS)ISE is proposed.This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations,thereby improving the estimation accuracy and robustness of the ISE.This paper adopts the data from the supervisory control and data acquisition(SCADA)system of the New York Independent System Operator(NYISO).It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods.In addition,this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case.Finally,the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.
基金supported in part by the National Key Research and Development Program of China(No.2017YFB0902900)the State Grid Corporation of China
文摘Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.
基金supported by the National Natural Science Foundation of China(No.52377087).
文摘Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in three-phase networks undermine the reliability of existing methods in practical applications.To address these issues,this paper proposes an interval harmonic state estimation method in three-phase unbalanced distribution networks,integrating data from multiple sources.First,the interval multi-source harmonic measurement dataset is constructed by integrating asynchronous harmonic measurement data from multiple sources.The time asynchrony of measurement data from power quality monitoring devices is calibrated using the sliding window weighted dynamic time warping algo-rithm.Second,the interval harmonic state estimation model for the three-phase asymmetric distribution network is constructed.The model is solved using the interval-weighted least squares method,enhanced by the improved Krawczyk operator,thereby minimizing the expansion resulting from interval operations.Finally,the feasibility and accuracy of the proposed interval harmonic state estimation method are validated.
