Identification of Distribution Network Parameters Based on Particle Swarm Optimization Method

Incomplete and inaccurate information of line parameters affects the condition monitoring, analysis and control of the distribution network. The accuracy of distribution network parameters is of great significance for its optimal operation. However, in recent years, the structure of distribution networks has become more and more complex, and there are large errors between the distribution network parameters and the actual values calculated by traditional theories. At the same time, there are errors in the actual measurement data, which makes it difficult to identify its parameter information accurately. Many current methods require the use of a large number of phasor measurement units to measure the phase angle of node voltages, which is currently not feasible for relatively conventional distribution networks. For this reason, this paper uses different parameter equations for the line for parameter identification by considering the presence or absence of phase measurement units
$\mu\text{PMU}$
. Firstly, the bad data are removed and then the parameter identification equation is derived and established by the formula, and the parameter identification is carried out by the particle swarm optimization algorithm, and the 13-node system is tested. The results verify the feasibility and good performance of the proposed parameter identification method, which is beneficial to the safe and stable operation of the distribution network.