An PM2.5-driven parametric adaptive ESDD prediction model

ESDD is the main cause of high voltage transmission line ESDD and flashing accidents, and it is of great importance to study the accumulation law of insulator ESDD. The existing ESDD prediction model assumes that the rate of ESDD accumulation is a relatively constant quantity, and the observed data show that this assumption does not hold true in China. The rate of ESDD accumulation is closely related to the air pollution, and our study establishes the relationship between the rate of ESDD accumulation and the particulate matter 2.5 (PM2.5). The dynamic prediction model of dirt accumulation was constructed on this basis, and the idea of data assimilation was introduced to complete the parameter optimization estimation. The experimental results show that the model can not only describe the dynamic change law of the equivalent salt density well, but also make more accurate and stable prediction of the equivalent salt density. Considering the easy accessibility and large wide coverage of PM2.5 data, the proposed model is expected to significantly reduce the cost of regional ESDD assessment.