Abstract
Parameter estimation based on the measurement data of the phasor measurement unit (PMU) is an important approach for identifying the Thévenin equivalent parameters (TEPs) of power systems. However, in the process of acquiring or transmitting data in PMU, measurement errors due to external interference or internal system faults will affect the accuracy of parameter estimation. In this paper, a TEP estimation algorithm based on local PMU measurement is proposed. The algorithm considers the errors of the PMU and introduces Huber function and projection statistics (PS) to eliminate the effects of outliers and leverage measurements, respectively. Additionally, a variable forgetting factor (VFF) is used to quickly eliminate the historical data with measurement deviation and track the changes of the system. The regularization technique is used to solve the divergence problem in the inverse process of the ill-conditioned matrix, thereby improving the stability and generalization performance of the algorithm. Finally, by minimizing the cost function of this algorithm, a recursive formula for the equivalent parameter estimation is derived. The effectiveness of the algorithm is verified on the IEEE 118-bus and IEEE 30-bus systems, and compared with recursive least squares (RLS) and Huber’s M-Estimation; the mean relative errors decreased by 94.75% and 84.77%, respectively.
Highlights
The Thévenin equivalent (TE) method is a method that can simplify the power system and increase the analysis rate
Huber’s M-Estimation, mean absolute error (MAE) of HU-projection statistics (PS) reduced by 97.46% and 96.22%, respectively, which indicates that algorithm inalgorithm this was paper hadhigher
This paper proposes an equivalent parameter identification algorithm based on variable forgetting factor (VFF) and PS
Summary
The Thévenin equivalent (TE) method is a method that can simplify the power system and increase the analysis rate. The above methods have reduced the influence of erroneous measurement and noise in some PMU measurement data Their performances cannot solve the problem of algorithm divergence caused by the ill-conditioned matrix inverse and lack the robustness and adaptive tracking ability when the system changes. In order to reduce the influence of erroneous measurement of PMU and ill-conditioned matrix inverse on the estimation accuracy of TEPs, a Huber’s M-Estimation algorithm based on the variable forgetting factor (VFF) and projection statistics, abbreviated as HU-PS, is proposed in this paper. The M-estimation based on projection statistics is used to filter the measurement data and determine the TEPs through the recursion formula In this way, the effect of erroneous measurement on the equivalent parameters is eliminated, and the problem of ill-conditioned matrix inversion is solved.
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