Hierarchical Estimation for Complex Multi-Domain Dynamical Systems

Complex power systems have dynamics spanning multiple energy domains and operating at multiple time scales. Hierarchical control has been proven to guarantee successful management of the coupling between the resulting fast transients and slow dynamics. It is usually prohibitively expensive or even infeasible to measure every signal in the system. Therefore, a reliable estimation framework that provides accurate estimates is vital to the success of the control design. This paper proposes a multi-level hierarchical estimation approach that can be used to supply reliable estimates to hierarchical controllers of complex multi-domain power systems. Models of complex multi-domain power systems can be accurately represented using graphs. System decomposition can be then achieved using clustering algorithms from graph theory. In this work, local estimates at each level of the hierarchical estimator are obtained using extended Kalman filters. A hierarchical estimator-controller is designed for an automotive electric vehicle as an illustrative example.