FPGA, physics-based modeling of IGBT and pin diode for hardware co-simulation of complex power electronic converters and systems

This paper presents the steps and the challenges for implementing analytical, physics-based models for the insulated gate bipolar transistor (IGBT) and the PIN diode in hardware and more specifically in field programmable gate arrays (FPGAs). The models can be utilised in hardware co-simulation of complex power electronic converters and entire power systems in order to reduce the simulation time without compromising the accuracy of results. Such a co-simulation allows reliable prediction of the system's performance as well as accurate investigation of the power devices' behaviour during operation. Ultimately, this will allow application-specific optimisation of the devices' structure, circuit topologies as well as enhancement of the control and/or protection schemes.

Language model combination and adaptation using weighted finite state transducers

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaptation may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences. ©2010 IEEE.

Minimize frequency fluctuations of isolated power system with wind farm by using superconducting magnetic energy storage

Wind power generation as one of the most popular renewable energy applications is absorbing more and more attention all over the world. However, output power fluctuations of wind farm due to random variations of wind speed can cause network frequency and voltage flicker in power systems. The power quality consequently declines, particularly in an isolated power system such as the power system in a remote community or a small island. This paper proposes an application of superconducting magnetic energy storage (SMES) to minimize output fluctuations of an isolated power system with wind farm. The isolated power system is fed by a diesel generator and a wind generator consisting of a wind turbine and squirrel cage induction machine. The control strategy is detailed and the proposed system is evaluated by simulation in Matlab/Simulink.

Voltage sensing buck converter-based technique for maximum photovoltaic solar energy delivery

A voltage sensing buck converter-based technique for maximum solar power delivery to a load is presented. While retaining the features and advantages of the incremental conductance algorithm, this technique is more desirable because of single sensor use. The technique operates by maximising power at the buck converter output instead of the input.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Low voltage ride-through of DFIG and brushless DFIG: Similarities and differences

The brushless doubly fed induction generator (BDFIG) has been proposed as a viable alternative in wind turbines to the commonly used doubly fed induction generator (DFIG). The BDFIG retains the benefits of the DFIG, i.e. variable speed operation with a partially rated converter, but without the use of brush gear and slip rings, thereby conferring enhanced reliability. As low voltage ride-through (LVRT) performance of the DFIG-based wind turbine is well understood, this paper aims to analyze LVRT behavior of the BDFIG-based wind turbine in a similar way. In order to achieve this goal, the equivalence between their two-axis model parameters is investigated. The variation of flux linkages, back-EMFs and currents of both types of generator are elaborated during three phase voltage dips. Moreover, the structural differences between the two generators, which lead to different equivalent parameters and hence different LVRT capabilities, are investigated. The analytical results are verified via time-domain simulations for medium size wind turbine generators as well as experimental results of a voltage dip on a prototype 250 kVA BDFIG. © 2014 Elsevier B.V.