Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks

Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced and implemented. Various Kalman filters and the observer are then tested on the 16-machine, 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Gaussian filters for parameter and state estimation: A general review of theory and recent trends

Real-time control systems rely on reliable estimates of states and parameters in order to provide accurate and safe control of electro-mechanical systems. The task of extracting state and parametric values from system's partial measurements is referred to as state and parameter estimation. The main goal is minimizing the estimation error as well as maintaining robustness against the noise and modeling uncertainties. The development of estimation techniques spans over five centuries, and involves a large number of contributors from a variety of fields. This paper presents a tutorial on the main Gaussian filters that are used for state estimation of stochastic dynamic systems. The main concept of state estimation is firstly described based on the Bayesian paradigm and Gaussian assumption of the noise. The filters are then categorized into several groups based on their applications for state estimation. These groups involve linear optimal filtering, nonlinear filtering, adaptive filtering, and robust filtering. New advances and trends relevant to each technique are addressed and discussed in detail

SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS

Filtering strategies play an important role in estimation theory, and are used to extract knowledge of the true states typically from noisy measurements or observations made of the system. The name ‘filter’ is appropriate since it removes unwanted noise from the signal. In 2007, the smooth variable structure filter (SVSF) was introduced. This filter is based on the sliding mode control and estimation techniques, and is formulated in a predictor-corrector fashion. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. This creates a robust and stable estimation strategy. The research presented in this thesis focuses on advancing the development and implementation of the SVSF. In its original form, the SVSF does not utilize a state error covariance matrix, which is a measure of the accuracy of the state estimates. Therefore, the first major contribution of this research is the formulation of an SVSF strategy with a covariance derivation. This creates a number of research opportunities that can only be pursued and rely on the availability of the error covariance matrix. In an effort to further improve the estimation accuracy, a time-varying smoothing boundary layer is created by minimizing the covariance. This contribution significantly improves the SVSF, and provides a mechanism for combining the SVSF with other popular estimation strategies. A linear system example with the presence of uncertainties is studied which demonstrates that the proposed SVSF improves the estimation accuracy by approximately 20%. Furthermore, a new model-based fault detection strategy is created based on the interacting multiple model (IMM) method. This new method (IMM-SVSF) is applied on an experimental apparatus for the purposes of fault detection. It is able to improve upon the fault detection probability by 10-30% (depending on the fault), when compared with the most commonly used strategy. The IMM-SVSF method is also found to work extremely well for target tracking problems, demonstrating an improvement of roughly 40%. This research results in a number of novel contributions, and significantly advances the development of the SVSF.Doctor of Philosophy (PhD