The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation

analysis and control. In practical situations

the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy

which leads to the problem of identifiability. In this paper

an identifiability analysis methodology of load model parameters

by estimating the confidential intervals (CIs) of the parameters

is proposed. The load model structure and the combined optimization and regression method to identify the parameters are first introduced. Then

the definition and analysis method of identifiability are discussed. The CIs of the parameters are estimated through the profile likelihood method

based on which a practical identifiability index (PII) is defined to quantitatively evaluate identifiability. Finally

the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid. The results show that the impact of various disturbance magnitudes

measurement errors and data length can all be reflected by the proposed PII. Furthermore

the proposed PII can provide guidance in data length selection in practical load model identification situations.