A statistical approach to dynamic load modelling and identification with high frequency measurements

As distribution systems become less passive and more complex, accurate dynamic load models are essential to the safe and reliable operation of the network. Dynamic load modelling can be addressed using black-box models, which, while effective in some circumstances, do not provide insight into elements in the network. On the other hand, dynamic load modelling using white-box models can provide insight, but requires extensive knowledge about the number, parameters and type of elements composing the load, which is generally not available. In this work, using white-box modelling, load aggregation, available knowledge of the network, and high frequency measurements, we contribute a statistical methodology to estimate the number, parameters and types of elements composing the load, and also quantify the uncertainty in these estimates. We validate our aggregation technique and estimation framework using simulated data, and test the sensitivity of these results to our underlying assumptions. November 7, 2016.