TY - JOUR
T1 - Topology Identification and Line Parameter Estimation for Non-PMU Distribution Network
T2 - A Numerical Method
AU - Zhang, Jiawei
AU - Wang, Yi
AU - Weng, Yang
AU - Zhang, Ning
N1 - Funding Information:
Manuscript received May 23, 2019; revised October 18, 2019 and January 20, 2020; accepted March 2, 2020. Date of publication March 9, 2020; date of current version August 21, 2020. This work was supported in part by the International (Regional) Joint Research Project of National Natural Science Foundation of China under Grant 71961137004, in part by the Tsinghua University Initiative Scientific Research Program under Grant 20193080026, in part by the Open Project of State Key Laboratory of Power Systems under Grant SKLD17KM02, and in part by the Technical Project of the State Grid: Research and Application of Internet-based Operation Platform for Ubiquitous Internet of Things in Electricity. Paper no. TSG-00734-2019. (Corresponding author: Ning Zhang.) Jiawei Zhang and Ning Zhang are with the State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China (e-mail: ningzhang@tsinghua.edu.cn).
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - The energy management system becomes increasingly indispensable with the extensive penetration of new players in the distribution networks, such as renewable energy, storage, and controllable load. Also, the operation optimization of the active distribution system requires information on operation state monitoring. Smart measuring equipment enables the topology identification and branch line parameters estimation from a data-driven perspective. Nevertheless, many current methods require the nodal voltage angles measured by phasor measurement units (PMUs), which might be unrealistic for conventional distribution networks. This paper proposes a numerical method to identify the topology and estimate line parameters without the information of voltage angles. We propose a two-step framework: the first step applies a data-driven regression method to provide a preliminary estimation on the topology and line parameter; the second step utilizes a joint data-and-model-driven method, i.e., a specialized Newton-Raphson iteration and power flow equations, to calculate the line parameter, recover voltage angle and further correct the topology. We test the method on IEEE 33 and 123-bus looped networks with load data from 1000 users in Ireland. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on limited samples of measurement without voltage angles.
AB - The energy management system becomes increasingly indispensable with the extensive penetration of new players in the distribution networks, such as renewable energy, storage, and controllable load. Also, the operation optimization of the active distribution system requires information on operation state monitoring. Smart measuring equipment enables the topology identification and branch line parameters estimation from a data-driven perspective. Nevertheless, many current methods require the nodal voltage angles measured by phasor measurement units (PMUs), which might be unrealistic for conventional distribution networks. This paper proposes a numerical method to identify the topology and estimate line parameters without the information of voltage angles. We propose a two-step framework: the first step applies a data-driven regression method to provide a preliminary estimation on the topology and line parameter; the second step utilizes a joint data-and-model-driven method, i.e., a specialized Newton-Raphson iteration and power flow equations, to calculate the line parameter, recover voltage angle and further correct the topology. We test the method on IEEE 33 and 123-bus looped networks with load data from 1000 users in Ireland. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on limited samples of measurement without voltage angles.
KW - Topology identification
KW - data-driven
KW - distribution network
KW - smart meter
KW - state estimation
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U2 - 10.1109/TSG.2020.2979368
DO - 10.1109/TSG.2020.2979368
M3 - Article
AN - SCOPUS:85089304336
SN - 1949-3053
VL - 11
SP - 4440
EP - 4453
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 5
M1 - 9027950
ER -