This paper discusses the impact of uncertain power injections in the grid on the load margin. Two common analyses of voltage stability are those aiming for the closest saddle node bifurcation and those assuming a prefixed direction of load and production increase. In the case of large renewable based generation units or a significant degree of dispersed generation, the loading margin has to be interpreted as a stochastic variable itself. This allows to interpret load margins at different levels of probability of voltage collapse with or without corrective actions undertaken. The probabilistic margin is assessed with a minimum number of samples by use of a stochastic response surface method implementation. The method is illustrated on the IEEE 24-bus and 118-bus system considering stochastic wind generation and dispersed generation.
An EIT shutterless campaign was conducted on 11 July 2001 and provided 120 high-cadence (68 s) 30.4 nm images of the north-eastern quarter of the Sun. Systematic intensity variations are seen which appear to propagate along an off-disk loop-like structure. In this paper we study the nature of these intensity variations by confronting the EIT observations studied in De Groof et al. (2004Groof et al. ( , A&A, 415, 1141 with simultaneous Hα images from Big Bear Solar Observatory. With the goal to carefully co-register the two image sets, we introduce a technique designed to compare data of two different instruments. The image series are first co-aligned and later overplotted in order to visualize and compare the behaviour of the propagating disturbances in both data sets. Since the same intensity variations are seen in the EIT 30.4 nm and in the Hα images, we confirm the interpretation of De Groof et al. (2004Groof et al. ( , A&A, 415, 1141) that we are observing downflows of relatively cool plasma. The origin of the downflows is explained by numerical simulations of "catastrophic cooling" in a coronal loop which is heated predominantly at its footpoints.
This paper studies measurement uncertainty propagation and parameter sensitivity based on a torque-estimation model for induction machines. The model is based on the equation that describes the interaction of rotor flux and rotor currents. Contrary to classical schemes for induction motor control, this is an open-loop scheme; however, the model still requires different machine parameters. Therefore, the parameter sensitivity of the model is performed. For validation, the model is implemented in the real-time environment dSPACE, and a test induction machine is subjected to different combinations of speed and torque profiles. The identified model can be used to replace mechanical torque-measurement devices or as a backup for a low-cost torque sensor.
This paper studies error propagation and parameter sensitivity based on a torque estimation model for induction machines. The model is based on the equation describing the interaction of rotor flux linkage and rotor currents. Contrary to classical schemes for induction motor control this is an open loop scheme, however, the model still requires different machine parameters. Therefore the parameters sensitivity of the model is performed. For validation, the model is implemented in the real-time environment dSPACE and a test induction machine is subjected to different combinations of speed andtorque profiles. The identified model can be used to replace mechanical torque measurement devices or as a backup of a cheap torque sensor.
This paper studies measurement uncertainty propagation in a torque estimation model for induction machines. The model requires several machine parameters as input. Since the input parameters are deduced from measurements, it is necessary to take into account uncertainty in the input of the model. A first order and second order reliability method are performed to evaluate the probability of failure of the system under the uncertain inputs. The system failure is defined through the limit state function and occurs when the calculated value of the torque differs significantly from the rated torque at rated conditions. Monte Carlo simulations are performed for validation and importance sampling is discussed to reduce the Monte Carlo simulation error. The probability of failure is also derived from the torque probability density function by means of a stochastic response surface method. The analysis includes current and speed measurement uncertainties as well.
An extended equivalent model (EEM) is introduced for designing and analyzing steady state regime or transient dynamics of a Switched Reluctance Machine (SRM) and validated by a Finite Element Model (FEM). The EEM includes magnetic, electrical and mechanical behavior of the machine incorporating the invertor and can be extended with a thermal and/or mechanical stress analysis. The method is more time efficient than FEMs without serious loss of accuracy. This makes it suitable for investigating long dynamic behavior eg. startup of a microturbine/generator couple or for parameterizing during the design stage of the machine. EEMs are not restricted to SRMs but can be used for any electrical machine. The EEM described in this paper is developed in the context of an autonomous high-speed power generation project. The core unit of the power generation module consists of a SRM/micro-turbine pair. The SRM will be used to start the turbine as well as to generate electrical power once the turbine is self-sustainable.
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