Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

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.