Application of neural network observer for on-line estimation of salient-pole synchronous generators' dynamic parameters using the operating data

Parameter identification is critical for modern control strategies in electrical power systems which is considered both dynamic performance and energy efficiency. This paper presents a novel application of ANN observers in estimating and tracking Salient-Pole Synchronous Generator Dynamic Parameters using time-domain, on-line disturbance measurements. The data for training ANN Observers are obtained through off-line simulations of a salient-pole synchronous generator operating in a one-machine-infinite-bus environment. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANNs are organized in conformity with the results of the observability analysis of synchronous generator dynamic parameters in its dynamic behavior. A collection of ANNs with same inputs but different outputs are developed to determine a set of the dynamic parameters. The ANNs are employed to estimate the dynamic parameters by the measurements which are carried out within each kind of fault separately. The trained ANNs are tested with on-line measurements to identify the dynamic parameters. Simulation studies indicate the ANN observer has a great ability to identify the dynamic parameters of salient-pole synchronous generator. The results also show that the tests which have given better results in estimation of each dynamic parameter can be obtained

Modelling and identification of a six axes industrial robot

This paper deals with the modelling and identification of a six axes industrial St ¨aubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are linear in the inertia parameters. Joint friction is described by a friction model that describes the friction behaviour in the full velocity range necessary for identification. Experimental parameter identification by means of linear least squares techniques showed to be very suited for identification of the unknown parameters, provided that the problem is properly scaled and that the influence of disturbances is sufficiently analysed and managed. An analysis of the least squares problem by means of a singular value decomposition is preferred as it not only solves the problem of rank deficiency, but it also can correctly deal with measurement noise and unmodelled dynamics

Hawkes process as a model of social interactions: a view on video dynamics

We study by computer simulation the "Hawkes process" that was proposed in a recent paper by Crane and Sornette (Proc. Nat. Acad. Sci. USA 105, 15649 (2008)) as a plausible model for the dynamics of YouTube video viewing numbers. We test the claims made there that robust identification is possible for classes of dynamic response following activity bursts. Our simulated timeseries for the Hawkes process indeed fall into the different categories predicted by Crane and Sornette. However the Hawkes process gives a much narrower spread of decay exponents than the YouTube data, suggesting limits to the universality of the Hawkes-based analysis.Comment: Added errors to parameter estimates and further description. IOP style, 13 pages, 5 figure

Control Strategy of Hardware-in-the-Loop Simulator EPOS 2.0 for Autonomous Docking Verification

This paper briefly describes the hybrid simulator system called European Proximity Operation Simulator (EPOS 2.0) and the development of the hardware-in-the-loop (HIL) docking simulation concept. A critical requirement for the docking simulation of this HIL simulator is that the 6-DOF robots in the loop have to exactly mimic the dynamic response of the two satellites during a contact operation. The main challenges to meet this requirement are in the stiffness of the robots, which is unlike that of the satellites, as well as the time delay in the HIL simulator. The paper mainly presents the impedance parameter identification concept for matching the impedance between the satellites impact model and the EPOS robots. In addition it presents the contact dynamics model used, and the control strategies to meet the requirements of the docking simulator. Finally it presents the preliminary results and future work

Dynamic Modeling Accuracy Dependence on Errors in Sensor Measurements, Mass Properties, and Aircraft Geometry

A nonlinear simulation of the NASA Generic Transport Model was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of dynamic models identified from flight data. Measurements from a typical system identification maneuver were systematically and progressively deteriorated and then used to estimate stability and control derivatives within a Monte Carlo analysis. Based on the results, recommendations were provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using other flight conditions, parameter estimation methods, and a full-scale F-16 nonlinear aircraft simulation were compared with these recommendations

Optimal model parameters for multi-objective large-eddy simulations

A methodology is proposed for the assessment of error dynamics in large-eddy simulations. It is demonstrated that the optimization of model parameters with respect to one flow property can be obtained at the expense of the accuracy with which other flow properties are predicted. Therefore, an approach is introduced which allows to assess the total errors based on various flow properties simultaneously. We show that parameter settings exist, for which all monitored errors are "near optimal," and refer to such regions as "multi-objective optimal parameter regions." We focus on multi-objective errors that are obtained from weighted spectra, emphasizing both large- as well small-scale errors. These multi-objective optimal parameter regions depend strongly on the simulation Reynolds number and the resolution. At too coarse resolutions, no multi-objective optimal regions might exist as not all error-components might simultaneously be sufficiently small. The identification of multi-objective optimal parameter regions can be adopted to effectively compare different subgrid models. A comparison between large-eddy simulations using the Lilly-Smagorinsky model, the dynamic Smagorinsky model and a new Re-consistent eddy-viscosity model is made, which illustrates this. Based on the new methodology for error assessment the latter model is found to be the most accurate and robust among the selected subgrid models, in combination with the finite volume discretization used in the present study

Joint parameter identification, vibration and noise analysis of gearbox

A certain type of gearbox is investigated for the problem that the stiffness and damping of bearings are difficult to be accurately determined and then affect the analysis of vibration and noise of gearbox. Firstly, a coupled dynamic lumped parameter model of three-stage helical gear system with consideration of bearing stiffness, bearing damping, and transmission error is established. The modal parameters of gear system are obtained by using the experimental modal analysis method with single-input and multiple-output. The equation for joint parameter identification of gearbox is established which is based on the experimental modal analysis theory and the dynamic lumped parameter model, and subsequently the parameters of the joint are obtained by the least square method. Then, a gear-shaft-bearing- housing coupled dynamic finite element model is developed on the basis of the identified parameters, and after that the dynamic response results of gearbox are solved by using the modal superposition method and compared with the vibration test results. Finally, an acoustic boundary element model of gearbox is established by taking the dynamic response results as the acoustic boundary condition, and the surface sound pressure and radiation noise of gearbox are solved by the boundary element method (BEM), and then the results are compared with the noise test. The results show that the simulation laws and test laws are in good agreement, and thus the method of joint parameter identification, vibration and noise analysis of gearbox is feasible

Stability parameter identification for a centrifugal compression system

This paper presents the application of a lumped parameter model to describe the dynamic behavior of a centrifugal compression system including surge. The response of the model is compared with experimental surge measurements from an industrial single stage compressor test rig. A parametric analysis of the model reveals the large influence of the stability parameter on the transient response. However, a good value for this parameter is not easily obtained fromsurge data. Therefore, an identification method is proposed to uniquely determine the stability parameter that is based on an approximate realization algorithm, making use of the fact that the step response of the system has the characteristics of a first order system. Simulation results show that this method canprovide an estimate for the stability parameter of a centrifugal compression system