To accurately detect abnormal conditions of the power frequency impedance in front of smart electricity meters on low-voltage lines

it is necessary to conduct an in-depth analysis of the feasibility and adaptability of existing identification methods. Firstly

based on the single-phase loop impedance model of the low-voltage distribution network

this paper elaborates on the calculation principles and implementation steps of the loop impedance method

impedance parameter estimation method

and neighbor meter voltage impedance method. Then

a single phase multi-user electricity experiment platform is constructed to collect electricity meter voltage and current data under different states of the meter front line (switch). Finally

the feasibility and adaptability of the three identification methods are evaluated based on experimental data and actual electricity meter data from the distribution area. The study shows that the loop impedance method is only suitable for systems with relatively small loop impedance

requiring the load of the downstream meters to remain unchanged between two measurement instances

which is quite demanding on the data. The impedance parameter estimation method is not affected by system impedance

but its identification results depend on the size and variation of its own load

with a significant deviation between the estimated value and actual value. The neighbor meter voltage impedance method is simple and easy to implement

has a wide range of applications

requires less data

and has a low false positive rate

but is only applicable to scenarios with multiple meter positions.