The State Monitoring of Energy Storage Power Stations Based on the Dynamic Local Preserving Projection–Local Outlier Factor Method

Monitoring the operating status of energy storage power stations can detect battery faults in enough time to prevent them from evolving into serious accidents capable of causing economic losses or even casualties. In this paper, a novel data-driven operating status monitoring method named dynamic local preserving projection–local outlier factor (DLPP-LOF) is proposed to improve the system safety performance of energy storage power stations. First, considering that the data collected by the battery management system of the energy storage power station have temporal correlations, the collected data are augmented so that the constructed augmentation matrix can effectively contain the autocorrelation in the data. Then, based on the local preserving projection (LPP) method, manifold features are effectively extracted from the augmented data matrix to preserve the meaningful local structure. Because the data in the battery management system of the energy storage power station do not display any specific distribution, it is not possible to perform traditional T2 and SPE statistical analysis. Therefore, we propose the use of the local outlier factor (LOF) method to obtain statistics from monitoring for early warning relating to fault status. Finally, the effectiveness and superiority of the proposed DLPP-LOF method are illustrated by applying it to a practical energy storage power station and comparing the results with those of classical methods.