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    Wyświetlanie 1-6 z 6
    Tytuł:
    A simplified control strategy for single-phase UPS inverters
    Autorzy:
    Monfared, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/201075.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    single-phase UPS
    multi-loop feedback
    feedforward
    Opis:
    Though there are many strategies to control single-phase uninterruptible power supply (UPS) inverters, they suffer from some drawbacks, the main being complexity. This paper proposes a simple dual-loop controller for the single-phase UPS inverter with the LC filter. The suggested control scheme uses the capacitor current as the feedback signal in the inner current loop. No fictitious phase generation or reference frame transformations are required, and simple proportional gains are employed as both voltage and current regulators. A feedforward of the derivative of the output voltage is also proposed, which significantly improves the performance of the closed loop control system. Then, based on the model of the inverter with the proposed control strategy, a simple and systematic design procedure is presented. Finally, the theoretical achievements are supported by extensive simulations.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 2; 367-373
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Repetitive neurocontroller with disturbance feedforward path active in the pass-to-pass direction for a VSI inverter with an output LC filter
    Autorzy:
    Ufnalski, B.
    Grzesiak, L. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/200017.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    repetitive control
    feedforward neural network
    dynamic optimization problem
    repetitive disturbance rejection
    voltage source inverter
    disturbance dual feedforward path
    sterowanie powtarzalne
    sieci neuronowe
    problem optymalizacji dynamicznej
    przetwornica napięcia
    odrzucanie zakłóceń
    Opis:
    An enhancement to the previously developed repetitive neurocontroller (RNC) is discussed and investigated in the paper. Originally, the time-base generator (TBG) has been used to produce the only input signal for the neural approximator. The resulting search space makes the dynamic optimization problem (DOP) of shaping the control signal solvable with the help of a function approximator such as the feed-forward neural network (FFNN). The plant under consideration, i.e. a constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an output LC filter, is assumed to be equipped with the disturbance load current sensor to enable implementation of the disturbance feed-forward (pDFF) path as a part of the non-repetitive subsystem acting in the along the pass p-direction. An investigation has been undertaken to explore potential benefits of using this signal also as an additional input for the RNC to augment the approximation space and potentially enhance the convergence rate of the real-time search process. It is numerically demonstrated in the paper that the disturbance feed-forward path active in the pass-to-pass k-direction (kDFF) improves the dynamics of the repetitive part as well indeed.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 1; 115-125
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Earplug Actuator Selection for a Miniature Personal Active Hearing Protection System
    Autorzy:
    Pawełczyk, M.
    Latos, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/178052.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    active noise control
    fixed-parameter control
    high-level noise
    nonstationary noise
    feedforward control
    earplug
    hearing protection
    Opis:
    There are many industrial environments which are exposed to a high-level noise. It is necessary to protect people from the noise. Most of the time, the consumer requires a miniature version of a noise canceller to satisfy the internal working place requirements. Very important thing is to select the most appropriate personal hearing protection device, for example an earplug. It should guarantee high passive noise attenuation and allow for secondary sound generation in case of active control. In many cases the noise is nonstationary. For instance, some of the noisy devices are switched on and off, speed of some rotors or fans changes, etc. To avoid any severe transient acoustic effects due to potential convergence problems of adaptive systems, a fixed-parameter approach to control is appreciated. If the noise were stationary, it would be possible to design an optimal control filter minimising variance of the signal being the effect of the acoustic noise and the secondary sound interference. Because of noise nonstationarity for most applications, the idea of generalised disturbance defined by a frequency window of different types has been developed by the authors and announced in previous publications. The aim of this paper is to apply such an approach to different earplugs and verify its noise reduction properties. Simulation experiments are conducted based on real world measurements performed using the G.R.A.S. artificial head equipped with an artificial mechanical ear, and the noise recorded in a power plant.
    Źródło:
    Archives of Acoustics; 2010, 35, 2; 213-222
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Quiet Zone for the patient in an Ambulance : Active Noise Control Technology for Siren Noise Reduction
    Autorzy:
    Sharma, M. K.
    Vig, R.
    Pal, R.
    Shantharam, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/177099.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    active noise control
    ANC
    virtual sensing technique
    ambulance siren noise
    zone of silence
    feedforward ANC
    virtual ANC
    Opis:
    This paper proposes an active noise control (ANC) application to attenuate siren noise for the patient lying inside ambulance with no sound proofing. From the point of cost effectiveness, a local ANC system based on feedforward scheme is considered. Further, to handle the limitation of limited Zone of Silence (ZoS), the ANC based on virtual sensing is explored. The simulations are done in MATLAB for the recorded ambulance siren noise signal. The results indicate that ANC can be an effective solution for creating a silent environment for the patient.
    Źródło:
    Archives of Acoustics; 2018, 43, 2; 275-281
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram.
    Autorzy:
    Lin, Xiaohui
    Xu, Jianmin
    Powiązania:
    https://bibliotekanauki.pl/articles/224037.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic engineering
    oversaturated intersection
    multilayer boundary
    macroscopic fundamental diagram
    feedforward feedback
    iterative control
    inżynieria ruchu
    przesycone skrzyżowanie
    granica wielowarstwowa
    makroskopowy diagram fundamentalny
    sprzężenie zwrotne
    Opis:
    The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feed forward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.
    Źródło:
    Archives of Transport; 2020, 53, 1; 67-87
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic fire risk prevention strategy in underground coal gasification processes by means of artificial neural networks
    Dynamiczna strategia zapobiegania ryzyku pożarowemu z użyciem sztucznych sieci neuronowych w procesach podziemnego zgazowania węgla
    Autorzy:
    Krzemień, Alicja
    Powiązania:
    https://bibliotekanauki.pl/articles/218921.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    dynamiczna strategia zapobiegania ryzyku
    prewencja ryzyka pożarowego
    podziemne zgazowanie węgla (PZW)
    dynamic alarm strategy
    fire risk prevention
    Generalized Regression Neural Network
    Multi-Layer Feedforward Networks (MLFN)
    Multivariate Adaptative Regression Splines (MARS)
    underground coal gasification (UCG)
    Opis:
    Based on data collected during an UCG pilot-scale experiment that took place during 2014 at Wieczorek mine, an active mine located in Upper Silesia (Poland), this research focuses on developing a dynamic fire risk prevention strategy addressing underground coal gasification processes (UCG) within active mines, preventing economic and physical losses derived from fires. To achieve this goal, the forecasting performance of two different kinds of artificial neural network models (generalized regression and multi-layer feedforward) are studied, in order to forecast the syngas temperature at the georeactor outlet with one hour of anticipation, thus giving enough time to UCG operators to adjust the amount and characteristics of the gasifying agents if necessary. The same model could be used to avoid undesired drops in the syngas temperature, as low temperature increases precipitation of contaminants reducing the inner diameter of the return pipeline. As a consequence the whole process of UGC might be stopped. Moreover, it could allow maintaining a high temperature that will lead to an increased efficiency, as UCG is a very exothermic process. Results of this research were compared with the ones obtained by means of Multivariate Adaptative Regression Splines (MARS), a non-parametric regression technique able to model non-linearities that cannot be adequately modelled using other regression methods. Syngas temperature forecast with one hour of anticipation at the georeactor outlet was achieved successfully, and conclusions clearly state that generalized regression neural networks (GRNN) achieve better forecasts than multi-layer feedforward networks (MLFN) and MARS models.
    Przedstawione w niniejszej pracy badania koncentrują się na opracowaniu dynamicznej strategii zapobiegania ryzyku pożarowemu w procesach podziemnego zgazowania węgla (PZW) w czynnych kopalniach. Celem badań jest zapobieganie ekonomicznym i fizycznym stratom wynikającym z pożarów. W pracy wykorzystano dane zebrane podczas pilotowego eksperymentu podziemnego zgazowania węgla, który odbył się w 2014 r. w czynnej Kopalni Węgla Kamiennego „Wieczorek”, zlokalizowanej na Górnym Śląsku. W artykule przeanalizowano działanie dwóch różnych modeli sztucznych sieci neuronowych, tj. sieci neuronowych realizujących uogólnione regresje GRNN oraz wielowarstwowych sieci perceptronowych MLFN, w celu prognozowania temperatury gazu syntezowego na wyjściu z georeaktora z godzinnym wyprzedzeniem. Informacja na temat temperatury na godzinę „do przodu” daje wystarczająco dużo czasu operatorowi procesu PZW na dostosowanie ilości i właściwości czynników zgazowujących do zaistniałej sytuacji. Ten sam model można zastosować do uniknięcia niepożądanych spadków temperatury gazu syntezowego. Niska temperatura gazu sprzyja wytrącaniu się osadu (substancji smolistych), powodując zmniejszanie średnicy rurociągu odbioru gazu, co w konsekwencji może prowadzić do całkowitego zatrzymania procesu zgazowania. Model pozwala również na utrzymanie wysokiej temperatury, która prowadzi do zwiększonej wydajności procesu PZW, szczególnie biorąc pod uwagę, że PZW jest procesem bardzo egzotermicznym. Wyniki zrealizowanych badań porównano z rezultatami uzyskanymi za pomocą modelu MARS – nieparametrycznej metody regresji zdolnej do modelowania zależność nieliniowych, których nie można odpowiednio modelować przy użyciu innych metod regresji. Prognoza temperatury gazu na godzinę „do przodu” na wylocie georeaktora została osiągnięta z powodzeniem, a wnioski jasno pokazują, że sieci neuronowe realizujące uogólnione regresje (GRNN – Generalized Regression Neural Networks) osiągają lepsze rezultaty niż wielowarstwowe sieci jednokierunkowe (MLFN – Multi-Layer Feedforward Networks) i modele MARS (Multivariate Adaptative Regression Splines).
    Źródło:
    Archives of Mining Sciences; 2019, 64, 1; 3-19
    0860-7001
    Pojawia się w:
    Archives of Mining Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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