16,818 research outputs found
Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
In this paper the empirical observability Gramian calculated around the
operating region of a power system is used to quantify the degree of
observability of the system states under specific phasor measurement unit (PMU)
placement. An optimal PMU placement method for power system dynamic state
estimation is further formulated as an optimization problem which maximizes the
determinant of the empirical observability Gramian and is efficiently solved by
the NOMAD solver, which implements the Mesh Adaptive Direct Search (MADS)
algorithm. The implementation, validation, and also the robustness to load
fluctuations and contingencies of the proposed method are carefully discussed.
The proposed method is tested on WSCC 3-machine 9-bus system and NPCC
48-machine 140-bus system by performing dynamic state estimation with
square-root unscented Kalman filter. The simulation results show that the
determined optimal PMU placements by the proposed method can guarantee good
observability of the system states, which further leads to smaller estimation
errors and larger number of convergent states for dynamic state estimation
compared with random PMU placements. Under optimal PMU placements an obvious
observability transition can be observed. The proposed method is also validated
to be very robust to both load fluctuations and contingencies.Comment: Accepted by IEEE Transactions on Power System
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
PMU-Based Estimation of Dynamic State Jacobian Matrix
In this paper, a hybrid measurement- and model-based method is proposed which
can estimate the dynamic state Jacobian matrix in near real-time. The proposed
method is computationally efficient and robust to the variation of network
topology. A numerical example is given to show that the proposed method is able
to provide good estimation for the dynamic state Jacobian matrix and is
superior to the model-based method under undetectable network topology change.
The proposed method may also help identify big discrepancy in the assumed
network model.Comment: IEEE International Conference on Circuits and Systems (ISCAS) 201
Lander Trajectory Reconstruction computer program
The Lander Trajectory Reconstruction (LTR) computer program is a tool for analysis of the planetary entry trajectory and atmosphere reconstruction process for a lander or probe. The program can be divided into two parts: (1) the data generator and (2) the reconstructor. The data generator provides the real environment in which the lander or probe is presumed to find itself. The reconstructor reconstructs the entry trajectory and atmosphere using sensor data generated by the data generator and a Kalman-Schmidt consider filter. A wide variety of vehicle and environmental parameters may be either solved-for or considered in the filter process
Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Networks of coupled dynamical systems provide a powerful way to model systems
with enormously complex dynamics, such as the human brain. Control of
synchronization in such networked systems has far reaching applications in many
domains, including engineering and medicine. In this paper, we formulate the
synchronization control in dynamical systems as an optimization problem and
present a multi-objective genetic programming-based approach to infer optimal
control functions that drive the system from a synchronized to a
non-synchronized state and vice-versa. The genetic programming-based controller
allows learning optimal control functions in an interpretable symbolic form.
The effectiveness of the proposed approach is demonstrated in controlling
synchronization in coupled oscillator systems linked in networks of increasing
order complexity, ranging from a simple coupled oscillator system to a
hierarchical network of coupled oscillators. The results show that the proposed
method can learn highly-effective and interpretable control functions for such
systems.Comment: Submitted to nonlinear dynamic
Simulation and control engineering studies of NASA-Ames 40 foot by 80 foot/80 foot by 120 foot wind tunnels
The development and use of a digital computer simulation of the proposed wind tunnel facility is described. The feasibility of automatic control of wind tunnel airspeed and other parameters was examined. Specifications and implementation recommendations for a computer based automatic control and monitoring system are presented
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