Measurement-based/Model-less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

Abstract: The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimated using raw measurements and then used in the control problem. This work focuses on measurement-based estimation of the voltage sensitivity coefficients which can be used for voltage control. In the existing literature, these coefficients are estimated using regression-based methods, which do not perform well in the case of high measurement noise. This work proposes tackling this problem by using neural network (NN)-based estimation of the voltage sensitivity coefficients which is robust against measurement noise. In particular, we propose using Feedforward and Long-Short Term Memory (LSTM) neural networks. The trained NNs take measurements of nodal voltage magnitudes and active and reactive powers and output the vector of voltage magnitude sensitivity coefficients. The performance of the proposed scheme is compared against the regression-based method for a CIGRE benchmark network.