Incorporating Temporal and Meteorological Data for Generating Pseudo-measurements in Active Distribution Power Networks

This paper proposes a new data-based algorithm to generate pseudo-measurements in active distribution networks with a high penetration of distributed generations and a limited number of real measurements. The real measurements, along with temporal and meteoritical features, are considered in order to improve the performance of the pseudo-measurement results. The proposed method consists of a set of base learners and a meta learner to generate pseudo-measurements. The proposed method is compared against a model with training data divided based on a random split without consideration of time, temperature, and seasonality data. To ensure the model training is robust against different dynamic behavior, temporal and meteorological information from Bozeman, MT, USA are considered. Furthermore, pseudo-measurements generated using the proposed method, along with the real measurements, are fed into a Weighted Least Squares method to perform state estimation calculations. The effectiveness of our proposed method is evaluated using a modified IEEE standard 69 bus distribution.