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    Wyświetlanie 1-11 z 11
    Tytuł:
    Artificial neural network modelling to predict optimum power consumption in wood machining
    Autorzy:
    Tiryaki, S.
    Malkocoglu, A.
    Ozsahin, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/52411.pdf
    Data publikacji:
    2016
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Technologii Drewna
    Tematy:
    artificial neural network
    modelling
    optimization
    power consumption
    wood processing
    planing
    wood product
    Źródło:
    Drewno. Prace Naukowe. Doniesienia. Komunikaty; 2016, 59, 196
    1644-3985
    Pojawia się w:
    Drewno. Prace Naukowe. Doniesienia. Komunikaty
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052267.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Polish Power Exchange
    day ahead market
    DAM
    artificial neural network
    system modelling
    MATLAB
    Opis:
    The paper contains the results of research on the impact of the number of factors used to build the Day-Ahead Market model at Polish Power Exchange S.A. Five models with a different number of factors influencing the model were tested. To test the quality of models according to the adopted evaluation criteria, i.e., mean square error and the coefficient of determination for the weighted average prices sold in a given hour of the day, the influence of weather factors, socio-economic factors and energy demand were adopted. The results obtained from the analysis show a relatively high correctness of the simplest of the adopted models, which differs slightly from the best model.
    Źródło:
    Studia Informatica : systems and information technology; 2020, 1-2(24); 25-35
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selected algorithms of MEMS accelerometers signal processing in burglary detector application
    Autorzy:
    Fabiański, B.
    Nowopolski, K.
    Wicher, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/376276.pdf
    Data publikacji:
    2016
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    MEMS
    accelerometer sensor
    data streaming
    DSP
    low-power MCU
    alarm system
    artificial neural network
    Opis:
    In the paper, implementations and results of operation of artificial neural network applied as a burglary classifier are presented in comparison to solution with a direct digital signal processing (DSP) approach. The neural network operates in a mobile access control device, that may be easily attached to a door. The device is an integrated system, equipped with several sensors based on microelectromechanical systems (MEMS) technology. Due to limited effectiveness of simple, conditional logic algorithms on acquired signal samples, a more sophisticated approaches are investigated. Data acquisition during imitation of various burglary scenarios and further processing of the recorded signals are described in the paper. Selection of the neural network structure and pre-processing methods of sensor signals are presented as well. The direct DSP algorithm based on the application of the properties of application phenomena is shown in the same way. Finally, results of selected algorithms implementation in a low-power 32-bit microcontroller system are presented. Limitation of the platform responsiveness in the real-time conditions and comparison of used classification methods are discussed in the paper conclusions.
    Źródło:
    Poznan University of Technology Academic Journals. Electrical Engineering; 2016, 87; 267-278
    1897-0737
    Pojawia się w:
    Poznan University of Technology Academic Journals. Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Burglary detection based on accelometric data using selected signal processing algorithms
    Autorzy:
    Fabiański, B.
    Nowopolski, K.
    Wicher, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/97596.pdf
    Data publikacji:
    2016
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    MEMS
    accelerometer sensor
    data streaming
    DSP
    low–power MCU
    alarm system
    artificial neural network
    Opis:
    The paper presents two approaches to the problem of burglary detection. The first one utilizes direct signal processing, while the other – artificial neural network (ANN). Both algorithms are compared in real operating conditions. The implementation of the algorithms was performed in a portable, battery operating devices that can be easily attached to the door. For direct comparison, two identical devices including several MEMS accelerometers and 32 bit microcontroller have been used – each with one algorithm implemented. The goal of using artificial neural network algorithm was to improve the performance of the burglary detection system in comparison to classical direct signal processing. The structure of ANN and required pre – processing of the input data, is presented and discussed as well. The article also describes the research system required to collecting the data for ANN training and to directly compare both algorithms. Finally, the results of behavior of the classification methods in real actual conditions is discussed.
    Źródło:
    Computer Applications in Electrical Engineering; 2016, 14; 313-327
    1508-4248
    Pojawia się w:
    Computer Applications in Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Capabilities of MATLAB and Simulink related to modelling of Polish power exchange
    Autorzy:
    Tchórzewski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/94981.pdf
    Data publikacji:
    2016
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    artificial neural network
    identification
    MATLAB
    Simulink
    environment
    Polish Power Exchange
    simulation
    Day Ahead Market
    Opis:
    The paper presents selected results of research on modelling a system of the POLISH Power Exchange in the MATLAB and Simulink environment. Modelling capabilities of various toolboxes and Matlab language were presented. Special attention was paid to identification modelling using System Identification Toolbox, neural modelling using Neural Network Toolbox and simulation modelling using Simulink. Research experiments were preformed based on the Day Ahead Market quotations. The obtained models of th type in SIT, an artificial neural network (ANN) in NNT and a block diagram in Simulink were subjected to comparative and sensitivity tests. Final results were interpreted.
    Źródło:
    Information Systems in Management; 2016, 5, 3; 424-435
    2084-5537
    2544-1728
    Pojawia się w:
    Information Systems in Management
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of Proportional-Integral Controllers of Grid-Connected Wind Energy Conversion System Using Grey Wolf Optimizer Based on Artificial Neural Network for Power Quality Improvement
    Autorzy:
    Alremali, Fathi Abdulmajeed M.
    Yaylacı, Ersagun Kürşat
    Uluer, İhsan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201727.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    artificial neural network
    grey wolf optimizer
    PI controller
    grid connection
    power quality
    wind energy
    Opis:
    This research presents a combination of artificial neural network (ANN) with the grey wolf optimizer (GWO) to improve the power quality of a grid-connected distributed power generation system (DPGS). To assess the effectiveness of the proposed algorithm, a grid-tied of small-scale wind energy conversion system (WECS) is chosen. The term power quality refers to voltage and frequency regulation, and limited harmonics. Power quality improvement is achieved through the cascaded control system's optimal tuning of three proportional-integral (PI) controllers of the grid-side inverter (GSI). However, because the DPGS model is computationally costly, the Artificial Neural Network (ANN) model is utilized as an alternative model for DPGS. Furthermore, the ANN model is employed in conjunction with the GWO to boost the optimization precision and minimize the execution time of GWO. The considered power system was repetitively simulated to obtain the input-output datasets, which validate and train the ANN model. According to the ANN model's performance evaluation, the correlation coefficient (R) is close to one, while the mean squared error (MSE) is near zero. These findings demonstrate the ANN model's great accuracy in approximating the DPGS model. Using MATLAB/Simulink, the system's performance is evaluated using the optimum values obtained using GWO-ANN for various wind speed profiles. It showed the suggested power quality method’s improved stability, convergence behavior, the effectiveness of the control mechanism, and the robustness of the proposed topology.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 295--305
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Design and research on artificial neural networks as electrical power system development models based on IEEE RTS data
    Autorzy:
    Tchórzewski, J.
    Pytel, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/97683.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    artificial neural network
    IEEE RTS data
    electrical power system
    MATLAB and Simulink environment
    testing sensitivity
    simulation studies
    Opis:
    The paper presents selected results of research on the design of artificial neural networks and training them using the electrical power system development model (EPS or EP system) based on IEEE RTS 96 test data, i.a. creation of training and test files, development of architecture of the artificial neural network, selection of parameters of the network, selection of appropriate training and testing method, etc. As a result of the development and training an ANN, the following EP system development models were obtained, which were examined for sensitivity to changes of the number of hidden layers, number of neurons in a layer, activation function, training method, etc. Subsequently, simulation models for studying fitness of the obtained models to the real systems. Interesting results were obtained, e.g. the method of the neural modelling of the system, the optimal architecture of the ANN that is a model of the system, possibilities and directions to improve a neural model of the system, etc.
    Źródło:
    Computer Applications in Electrical Engineering; 2015, 13; 231-244
    1508-4248
    Pojawia się w:
    Computer Applications in Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network
    Autorzy:
    Prucnal, M.
    Polak, A. G.
    Powiązania:
    https://bibliotekanauki.pl/articles/220360.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sleep stage classification
    EEG signal
    power spectral density
    discrete wavelet transform
    empirical mode decomposition
    artificial neural network
    Opis:
    EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.
    Źródło:
    Metrology and Measurement Systems; 2017, 24, 2; 229-240
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality
    Autorzy:
    Agrawal, Seema
    Kumar, Mahendra
    Palwalia, D. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/41176533.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Warszawska, Instytut Techniki Cieplnej
    Tematy:
    SAPF
    shunt active power filter
    THD
    ANN
    artificial neural network
    AGPSO algorithm
    PCC
    bocznikowy filtr mocy czynnej
    sztuczna sieć neuronowa
    algorytmy
    Opis:
    This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.
    Źródło:
    Journal of Power Technologies; 2022, 102, 4; 121-131
    1425-1353
    Pojawia się w:
    Journal of Power Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie klasyfikatora neuronowego w systemie eksperckim oceny stanu układów izolacyjnych transformatorów elektroenergetycznych
    The Application of a Neural Classifier in an Expert System for the Evaluation of the Condition of Power Transformer Insulation System
    Autorzy:
    Boczar, T.
    Borucki, S.
    Cichoń, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/153524.pdf
    Data publikacji:
    2007
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    wyładowania niezupełne
    sieci neuronowe
    system ekspercki
    układ izolacyjny
    transformator elektroenergetyczny
    partial discharge
    artificial neural network
    expert's system
    paper-oil insulation
    power transformer
    Opis:
    Tematyka artykułu dotyczy oceny możliwości wykorzystania jednokierunkowej sieci neuronowej w systemie eksperckim diagnostyki stanu układów izolacyjnych transformatorów elektroenergetycznych. W artykule przedstawiono teoretyczne i praktyczne możliwości budowy systemu ekspertowego, bazującego na metodzie emisji akustycznej, wspomagającego diagnostykę układów izolacyjnych transformatorów dużej mocy. Zamieszczone w pracy wyniki przedstawiają skuteczność rozpoznawania badanych form WNZ (defektów izolacji), uzyskaną przy zastosowaniu klasyfikatora neuronowego, jak również ocenę możliwości jego zastosowania jako mechanizmu wnioskującego komputerowego systemu diagnostycznego.
    The subject matter of the paper refers to the evaluation of the application possibilities of a single-direction neural network in the expert system of the diagnostics of the insulation system condition. The paper presents theoretical and practical possibilities of building an expert system based on the acoustic emission method, assisting the diagnostics of insulation systems of high power transformers. The results presented in this paper show the recognition effectiveness of the PD forms under study (insulation defects), obtained by using a neural classifier as well as the evaluation of its application possibilities as an inferring mechanism of a computer diagnostic system.
    Źródło:
    Pomiary Automatyka Kontrola; 2007, R. 53, nr 10, 10; 29-32
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS
    Autorzy:
    Nazir, Awais
    Khan, Safdar Abbas
    Khan, Malak Adnan
    Alam, Zaheer
    Khan, Imran
    Irfan, Muhammad
    Rehman, Saifur
    Nowakowski, Grzegorz
    Powiązania:
    https://bibliotekanauki.pl/articles/27311430.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    wind energy conversion systems
    WECS
    robust control
    maximum power point tracking
    MPPT
    sliding mode control
    SMC
    super-twisting algorithm
    STA
    high gain observer
    artificial neural network
    ANN
    function fitting
    backstepping
    śledzenie maksymalnego punktu mocy
    obserwator o dużym wzmocnieniu
    sztuczna sieć neuronowa
    dopasowanie funkcji
    system konwersji energii wiatrowej
    sterowanie odporne
    sterowanie ślizgowe
    algorytm super skręcania
    Opis:
    The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 5; art. no. e147063
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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