A novel approach of full state tendency measurement for complex systems based on information causality and PageRank: A case study of a hydropower generation system

The hydropower generation system is a typical complex nonlinear system with hybrid state responses. The interaction between the state responses of the system is affected closely by the coupling of hydraulic, mechanical, and electromagnetic factors and the frequent changes in working conditions during operation. However, the precise roles of the interaction relationship are unknown. Here, we show that this interaction depends on the causal coupling between subsystems, and use this relationship to propose a time series data mining and data prediction strategy based on the information causality and the PageRank algorithm. A nonlinear model is used to prove that the proposed prediction strategy can effectively reduce the dimension of auxiliary variables. Finally, the strategy is validated with a 250 MW hydropower unit. Our results show that the information causal coupling between variables is cross-scale with definite Markov orders of time series, and the prediction accuracy can be improved by considering the information transfer sequences between the prediction object variable and the causal variable in the absence of state auxiliary variables. Furthermore, the proposed method can be also applied to data mining of other complex systems and variable selection of prediction models and builds a bridge between mechanism- and data-driven approaches, which has a high engineering application value.

Automated summary

The hydropower generation system is a complex nonlinear system with hybrid state responses. This study proposes a data mining and data prediction strategy based on information causality and the PageRank algorithm, which is validated using a 250 MW hydropower unit. The results show that considering the information transfer sequences between variables improves prediction accuracy.