Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Abstract

Abstract This paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D‐PMU) and the advanced measurement infrastructure. The synchronized and high‐precision D‐PMU is utilized to tackle the challenge risen by the a‐synchronization of smart meters. Correspondingly, a time‐alignment algorithm is proposed to obtain the time‐synchronous error (TSE) dataset for the up‐stream smart meter. The non‐parametric estimation method is performed then to evaluate the probabilistic density curve of TSE. Furthermore, TSE data of down‐stream smart meters are generated by implementing the acceptance‐rejection process based on the obtained probabilistic density curve. Leveraging the generated TSE dataset, a new time‐shifted D‐PMU curve is probabilistically aligned or fused with the down‐stream advanced measurement infrastructure curves. According to the complete voltage drop model, the line parameter estimation of resistance and reactance is formulated as a quadratic programming problem and solved by Optimal Toolbox in MATLAB by conducting multi‐run Monte‐Carlo simulations under various scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed methodology.

Keywords

• Other topics in statistics
• Optimisation techniques
• Power system measurement and metering