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Incomplete and inaccurate information of network topology and line parameters affects state monitoring, analysis, and control of active distribution networks. To solve this issue, this article proposes a method for identifying distribution network topology and line parameters using the measurements obtained from smart meters (SMs) and microphasor measurement units (μPMUs) installed at various locations in a distribution network. A data-driven approach was developed, which uses a probabilistic method (unscented Kalman filter (UKF) based) and a deterministic method (Newton Raphson (NR) based) iteratively for accurate identification of network topology and parameters. The impact of the measurement noise with SMs and μPMUs is analyzed, and the acceptable noise levels are quantified. The impact of the identification algorithm on the network state estimation is examined. Moreover, optimal installation locations of the μPMU equipment are identified based on the estimation accuracy of the algorithm. The method is validated on benchmarked IEEE 33-bus and IEEE 123-bus test systems, while the impact of the renewable power injections at the different network nodes is studied as well. The qualitative and quantitative analysis is performed over the state-of-the-art methods, to highlight the effectiveness of the proposed methodology.

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