Robust State Estimation of Active Distribution Networks with Multi-source Measurements

The volatile and intermittent nature of distributed generators (DGs) in active distribution networks (ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units (D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming (SOCP) based robust state estimation (RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.

Automated summary

This paper proposes a second-order cone programming (SOCP) based robust state estimation (RSE) method considering multi-source measurements in active distribution networks (ADNs) with distributed generators (DGs). The method converts the phase angle measurements of distribution phasor measurement units (D-PMUs) to equivalent power measurements and utilizes weighted least absolute value estimator and multi-time slots of D-PMU measurements to improve convergence, bad data identification, and estimation accuracy.