Robust Sliding Mode Observers for Large Scale Systems with Application to a Multimachine Power System

In this paper, a class of interconnected systems with structured and unstructured uncertainties is considered where the known interconnections and uncertain interconnections are nonlinear. The bounds on the uncertainties are employed in the observer design to enhance the robustness when the structure of the uncertainties is available for design. Under the condition that the structure distribution matrices of the uncertainties are known, a robust sliding mode observer is designed and a set of sufficient conditions is developed to guarantee that the error dynamics are asymptotically stable. In the case that the structure of uncertainties is unknown, an ultimately bounded approximate observer is developed to estimate the system states using sliding mode techniques. The results obtained are applied to a multimachine power system, and simulation for a two machine power system is presented to demonstrate the feasibility and effectiveness of the developed methods

An Energy-Based State Observer for Dynamical Subsystems with Inaccessible State Variables

This work presents an energy-based state estimation formalism for a class of dynamical systems with inaccessible/ unknown outputs, and systems at which sensor utilization is impractical, or when measurements can not be taken. The power-conserving physical interconnections among most of the dynamical subsystems allow for power exchange through their power ports. Power exchange is conceptually considered as information exchange among the dynamical subsystems and further utilized to develop a natural feedback-like information from a class of dynamical systems with inaccessible/unknown outputs. This information is used in the design of an energybased state observer. Convergence stability of the estimation error for the proposed state observer is proved for systems with linear dynamics. Furthermore, robustness of the convergence stability is analyzed over a range of parameter deviation and model uncertainties. Experiments are conducted on a dynamical system with a single input and multiple inaccessible outputs (Fig. 1) to demonstrate the validity of the proposed energybased state estimation formalism

Distributed Fault Diagnosis of Interconnected Nonlinear Uncertain Systems

Fault diagnosis is crucial in achieving safe and reliable operations of interconnected control systems. This dissertation presents a distributed fault detection and isolation (FDI) method for interconnected nonlinear uncertain systems. The contributions of this dissertation include the following: First, the detection and isolation problem of process faults in a class of interconnected input-output nonlinear uncertain systems is investigated. A novel fault detection and isolation scheme is devised, and the fault detectability and isolability conditions are rigorously investigated, characterizing the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Second, a distributed sensor fault FDI scheme is developed in a class of interconnected input-output nonlinear systems where only the measurable part of state variables are directly affected by the interconnections between subsystems. A class of multimachine power systems is used as an application example to illustrate the effectiveness of the proposed approach. Third, the previous results are extended to a class of interconnected input-output nonlinear systems where both the unknown and the measurable part of system states of each subsystem are directly affected by the interconnections between subsystems. In this case, the fault propagation effect among subsystems directly affects the unknown part of state variables of each subsystem. Thus, the problem considered is more challenging than what is described above. Finally, a fault detection scheme is presented for a more general distributed nonlinear systems. With a removal of a restrictive limitation on the system model structure, the results described above are extended to a class of interconnected nonlinear uncertain systems with a more general structure. In addition, the effectiveness of the above fault diagnosis schemes is illustrated by using simulations of interconnected inverted pendulums mounted on carts and multi-machine power systems. Different fault scenarios are considered to verify the diagnosis performances, and the satisfactory performances of the proposed diagnosis scheme are validated by the good simulation results. Some interesting future research work is also discussed

Comparisons of an Adaptive Neural Network Based Controller and an Optimized Conventional Power System Stabilizer

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN\u27s weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design