Load component decomposition and online modelling based on small disturbance response characteristic matching

Abstract

Accurate load model significantly influences the numerical simulations of power system. The load model obtained by the component-based method does not adequately reflect the load time-varying characteristics, and the measurement-based method relies on fault data, but system faults rarely occur. Inspired by the theory of non-intrusive load monitoring, the load component decomposition and online modelling method based on the small disturbance response characteristic matching is proposed. An optimization model for the load component decomposition based on the curve fitting of small disturbance response characteristics is established. The construction method of a typical load model library is given and a neural network-based solution is designed. The proposed method can determine the load components that constitute the synthesis load model and their proportions by only analysing the small disturbance real-time measurement data. To verify the effectiveness of the proposed method, the distribution of the decomposition errors was analysed by the Monte Carlo simulation. Furthermore, the accuracy of the synthesis load model was verified by a fault simulation using the CEPRI 36-node system. Experimental results show that the proposed method can provide accurate load compositions and dynamically update the synthesis load model for the online power system simulation, planning and operation.

Simulation‐data‐driven load disaggregation based on multi‐channel neural network for industrial and commercial users

“…Table 3 ranks load fluctuations (measured by standard deviation) and the power consumption share of individual load types. In more detail, the load types of high consumption are SMPS (screens, computers), rectifier, resistive load, lighting and large cold load, which are in line with the widespread application of these devices in a real situation, providing more choices of categories than [14] and [16], with a meaningful and observable distribution.…”

“…To overcome challenges arising from inadequate energy consumption data, simulated data are gradually accepted to partially or even completely take the place of realistic data in load disaggregation studies [13,14]. Part of power signatures is synthesized in [15] by leveraging a generative adversarial network, albeit with difficult parameter tuning.…”