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wyświetlamy 1000 najlepszych wyników Pomoc
Wyniki wyszukiwania dla: THERMOELECTRIC GENERATOR (TEG) MAXIMUM POWER POINT TRACKING (MPPT) SWARM INTELLIGENCE (SI) FEED-FORWARD NEURAL NETWORK (FFNN)
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Cleaner energy for sustainable future using hybrid photovoltaics-thermoelectric generators system under non-static conditions using machine learning based control technique
PublikacjaIn addition to the load demand, the temperature difference between the hot and cold sides of the thermoelectric generator (TEG) module determines the output power for thermoelectric generator systems. Maximum power point tracking (MPPT) control is needed to track the optimal global power point as operating conditions change. The growing use of electricity and the decline in the use of fossil fuels have sparked interest in photovoltaic-TEG...
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Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems
PublikacjaThe thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition,...
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Green energy extraction for sustainable development: A novel MPPT technique for hybrid PV-TEG system
PublikacjaThe Photovoltaic (PV) module converts only a small portion of irradiance into electrical energy. Most of the solar energy is wasted as heat, resulting in a rise in PV cell temperature and a decrease in solar cell efficiency. One way to harvest this freely available solar thermal energy and improve PV cell efficiency is by integrating PV systems with thermoelectric generators (TEG). This cogeneration approach of the hybrid PV-TEG...
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Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions
PublikacjaThe application of small-scale electrical systems is widespread and the integration of Maximum Power Point Tracking (MPPT) control for Photovoltaic systems with battery applications further enhances the techno-economic feasibility of renewable systems. For this purpose, a novel MPPT control system using Dynamic Group based cooperation optimization (DGBCO) algorithm is utilized for PV systems. The population in the DGBCO is divided...
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Fuzzy Based Maximum Power Point Tracking (MPPT) Control System for Photovoltaic Power Generation System
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Improved maximum power point tracking algorithms by using numerical analysis techniques for photovoltaic systems
PublikacjaSolar photovoltaic (PV) panels generate optimal electricity when operating at the maximum power point (MPP). This study introduces a novel MPP tracking algorithm that leverages the numerical prowess of the predictor-corrector method, tailored to accommodate voltage and current fluctuations in PV panels resulting from variable environmental factors like solar irradiation and temperature. This paper delves into the intricate dynamics...
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An Optimal Power Point Tracking Algorithm in a Solar PV Generation System
PublikacjaThe non-linearity in I-V characteristics of a PV panel requires to be operated at knee point to extract maximum power. In order to operate the panel at optimal point, maximum power point tracking (MPPT) algorithm is employed in the control structure. The main objective of MPP tracking is to keep the operation at knee point of I-V characteristics under varying condition of temperature and solar insolation. Under non uniform solar...
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Photovoltaic Maximum Power Point Technique based on Incremental Conductance (INCON) control algorithm
PublikacjaMaximum output power status can significantly improve the deployment rate of solar energy system. In order to get the maximum power output, issue of tracking maximum power point (MPP), reduced harmonics around MPP and improve efficiency of the solar power energy system, this paper presents the improved maximum power point tracking (MPPT) control...
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The Maximum Power Point Tracking (MPPT) of a Partially Shaded PV Array for Optimization Using the Antlion Algorithm
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Maximum Power Point Technique (MPPT) for PV System Based on Improved Pert and Observe (P&O) Method with PI Controller
PublikacjaPhotovoltaic power generation system has key rule in electricity production. Although, it is clean renewable energy with unlimited resources but it has some drawbacks in efficiency. In order to maximize the efficiency, PV array must drive at maximum power point. For the reason so, several algorithms are used in PV system to track MPP and reduce the...
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MPPT control - a way to the maximization of energy amount obtained from the PV module
PublikacjaW układzie MPPT (Maximum Power Point Tracking) poziom mocy maksymalnej jest dynamicznie śledzony dla różnych poziomów napromieniowania i temperatur przy użyciu pamięci, zaprojektowanej jako sterowany mikroprocesorem moduł. Zastosowany algorytm w sposób ciągły poszukuje maksymalnej wartości mocy podawanej ze źródła (modułów PV) oraz pobieranej przez obciążenie. Napięcie wyjściowe z układu MPPT zależy od aktualnych warunków, w jakich...
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Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur
PublikacjaMost of the literature models for condensation heat transfer prediction are based on specific experimental parameters and are not general in nature for applications to fluids and non-experimental thermodynamic conditions. Nearly all correlations are created to predict data in normal HVAC conditions below 40°C. High temperature heat pumps operate at much higher parameters. This paper aims to create a general model for the calculation...
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Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network
PublikacjaArtificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper pesents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to...
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Results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature
Dane BadawczeThis database present results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature. Databse contain one table and 7 figures.
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Olgun Aydin dr
OsobyOlgun Aydin finished his PhD by publishing a thesis about Deep Neural Networks. He works as a Principal Machine Learning Engineer in Nike, and works as Assistant Professor in Gdansk University of Technology in Poland. Dr. Aydin is part of editorial board of "Journal of Artificial Intelligence and Data Science" Dr. Aydin served as Vice-Chairman of Why R? Foundation and is member of Polish Artificial Intelligence Society. Olgun is...
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Resource constrained neural network training
PublikacjaModern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental, computational, and power constraints are inevitable consequences of such a shift. Limiting the bit count of neural network parameters proved to be a valid technique for speeding up and increasing efficiency of the inference process. Hence, it is understandable that a similar approach is gaining...
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Photovoltaic maximum power point varying with illumination and temperature
PublikacjaW pracy przedstawiono wyniki badań eksperymentalnych zmian położenia punktu maksymalnej mocy na charakterystyce prądowo-napięciowej modułu ogniw fotowoltaicznych, związanych ze zmieniającą się temperaturą i natężeniem padającego promieniowania. Badano czas narastania napięcia pracy i temperatury ogniw w początkowej fazie ekspozycji na promieniowanie słoneczne. Przedstawiono niektóre praktyczne aspekty zastosowania układu automatycznego...
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Neural network training with limited precision and asymmetric exponent
PublikacjaAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Control of a wind power generator in case of voltage sags in power network
PublikacjaW artykule poruszono problem związany ze sposobem sterowania generatorem elektrowni wiatrowej w przypadku wystąpienia po stronie sieci zapadów napięcia.Jako generator wykorzystano maszynę dwustronnie zasilaną której stojan podłączono bezpośrednio do sieci natomiast wirnik zasilano poprzez kaskadę przekształtników. Wystąpienie spadku lub zapadu napięcia sieci w przypadku pracy tego typu generatora może doprowadzić do uszkodzenia...
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Algorytmy MPPT dla modułów fotowoltaicznych w warunkach przesłonięcia
PublikacjaIntensywny rozwój technologii powoduje obniżenie ceny modułów fotowoltaicznych i dedykowanych przetwornic. Podstawą opłacalności jest wysoka sprawność całego układu na którą składają się sprawności modułów, przetwornic oraz algorytmu śledzenia maksymalnej mocy (MPPT - Maximum Power Point Tracking). Znane i stosowane algorytmy mają MPPT sprawności od ok. 95 do 99%, o ile ogniwa mają identyczne parametry i są jednakowo nasłonecznione....
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Michał Michna dr hab. inż.
OsobyJest absolwentem Wydziału Elektrycznego Politechniki Gdańskiej (1998). W 2004 r. uzyskał stopień doktora. Od 2004 r. zatrudniony w Katedrze Energoelektroniki i Maszyn Elektrycznych Politechniki Gdańskiej (asystent, adiunkt, starszy wykładowca). W latach 2010-2015 zastępca kierownik katedry. Jego zainteresowania naukowe i dydaktyczne obejmują szerokie spektrum zagadnień związanych z projektowanie, modelowanie i diagnostyką maszyn...
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A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions
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Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates
PublikacjaThis paper presents a novel approach to reduce undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two stand-ard patch antenna cells with 0.07λ edge-to-edge distance are designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator is applied between the antennas to suppress their mutual coupling. For the first time, the optimum values...
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Weryfikacja algorytmów MPPT dla modułów fotowoltaicznych w warunkach przesłonięcia
PublikacjaIntensywny rozwój technologii powoduje obniżenie ceny modułów fotowoltaicznych i dedykowanych przetwornic. Podstawą opłacalności jest wysoka sprawność całego układu na którą składają się sprawności modułów, przetwornic oraz algorytmu śledzenia maksymalnej mocy (MPPT - Maximum Power Point Tracking). Znane i stosowane algorytmy mają MPPT sprawności od ok. 95 do 99%, o ile ogniwa mają identyczne parametry i są jednakowo nasłonecznione....
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Swarm Intelligence
Czasopisma -
Intelligent turbogenerator controller based on artifical neural network
PublikacjaThe paper presents a desing of an intelligent controller based on neural network (ICNN). The ICNN ensures at the same time two fundamental functions : the maintaining of generator voltage at the desired value and the damping of the electromechanical oscillations. Its performance is evaluted on a single machine infinite bus power system through computer simulations. The dynamic and transient operation of the proposed controller...
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Issues of Estimating the Maximum Distributed Generation at High Wind Power Participation
PublikacjaThis paper presents the methods of estimating the maximum power that can be connected to the power system in distributed generation sources. Wind turbine generator systems (WTGS) were selected as the subject for analysis. Nonetheless, the considerations presented in this paper are only general and also apply to other types of power sources, including the sources that are not considered part of distributed generation.
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Artificial Neural Network in Forecasting the Churn Phenomena Among Costumers of IT and Power Supply Services
PublikacjaThis paper presents an attempt to use an artificial neural network to investigate the churn phenomenon among the customers of a telecommunications operator. An attempt was made to create a data model based on the customer lifetime value (CLV) rather than on activity alone. A multilayered artificial neural network was used for the experiments. The results yielded a 99% successful identification rate for customers in no danger of...
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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublikacjaIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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MPPT and GMPPT Implementation for Buck-Boost Mode Control of quasi-Z-Source Inverter
PublikacjaThe focus is on the maximum power point tracking implementation for the buck-boost voltage mode control of a single-phase multilevel inverter based on a three-level neutral point clamped quasi-Z-source topology. To utilize shoot-through states only when boost function is needed and avoid it in the buck mode, two different control approaches are required. This work proposes merged control system which provides switching between...
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Technika MPPT sposobem maksymalizacji wykorzystania energii elektrycznej generowanej przez moduły fotowoltaiczne
PublikacjaWprowadzenie techniki MPPT (Maximum Power Point Tracking). w systemach fotowoltaicznych umożliwia zwiększenie efektywności przetwarzania energii słonecznej w elektryczną. Technika ta polega na wprowadzeniu do instalacji fotowoltaicznej stosownej, sterowalnej przetwornicy DC/DC, która w połączeniu z odpowiednim algorytmem bądź algorytmami wyszukiwania punktu mocy maksymalnej zapewnia odpowiednie dopasowanie energetyczne modułów...
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Deep neural network architecture search using network morphism
PublikacjaThe paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification...
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Computational intelligence methods in production management
PublikacjaThis chapter presents a survey of selected computational intelligence methods used in production management. This group of methods includes, among others, approaches based on the artificial neural networks, the evolutionary algorithms, the fuzzy logic systems and the particle swarm optimization mechanisms. From the abovementioned methods particularly noteworthy are the evolutionary and the particle swarm algorithms, which are successfully...
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Interior Point Method Evaluation for Reactive Power Flow Optimization in the Power System
PublikacjaThe paper verifies the performance of an interior point method in reactive power flow optimization in the power system. The study was conducted on a 28 node CIGRE system, using the interior point method optimization procedures implemented in Power Factory software.
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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublikacjaIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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ReFlexeNN - the Wearable EMG Interface with Neural Network Based Gesture Classification
PublikacjaThe electromyographic activity of muscles was measured using a wireless biofeedback device. The aim of the study was to examine the possibility of creating an automatic muscle tension classifier. Several measurement series were conducted and the participant performed simple physical exercises - forcing the muscle to increase its activity accordingly to the selected scale. A small wireless device was attached to the electrodes placed...
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Effect of pine impregnation and feed speed on sound level and cutting power in wood sawing
PublikacjaThe sound levels along with the cutting power registered during the sawing process of the impregnated and non-impregnated pine wood at two feed speeds are shown and compared in this paper. Statistically significant differences in the acoustic signals occurred at the lower feed rate. The differences became smaller with an increase in the feed speed. In contrast to the sound signal, the differences...
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Categorization of emotions in dog behavior based on the deep neural network
PublikacjaThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublikacjaA Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...
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Methods for evaluating rated power of diesel-powered generator set
PublikacjaBasic concepts for evaluating rated power of generator sets powered by combustion engines are presented in this article. A relation between parameters of the engine and the generator are shown. Recommendations contained in the standards and technical literature are systematized. The scheme and methodology for evaluating rated power of the generator set, based on laboratory research, are suggested. Also, the main problems which...
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The impact of the AC922 Architecture on Performance of Deep Neural Network Training
PublikacjaPractical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report...
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Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System
PublikacjaIn this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. Such a local controller utilizes an input-output model of the system taking into consideration not only a generator voltage Ug but also an additional, auxiliary signal (e.g., α, Pg, or ωg). This additional piece of information allows for taking...
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Estimation of the Maximum Permissible PV Power to be Connected to the MV Grid
PublikacjaIn recent decades, a significant increase in the share of renewable energy sources in power grids at various voltage levels has been observed. A number of articles have been published highlighting emerging problems in low-voltage grids with a large share of prosumers and in medium- and high-voltage grids to which photovoltaic (PV) plants are connected. The article analyzes the medium-voltage grid in terms of the possibility of...
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Models of Brushless Synchronous Generator for Studying Autonomous Electrical Power System
PublikacjaThis is a PhD dissertation. The work presented in this monograph was carried out at the Department of Power Electronics and Electrical Machines, Faculty of Electrical and Control Engineering at the Gdansk University of Technology. Developed during the research models of brushless synchronous generator ware verified using FEM based simulations and measurements conducted on the prototype generator. The main focus of the research...
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Global Surrogate Modeling by Neural Network-Based Model Uncertainty
PublikacjaThis work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically...
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Neural Network Subgraphs Correlation with Trained Model Accuracy
PublikacjaNeural Architecture Search (NAS) is a computationally demanding process of finding optimal neural network architecture for a given task. Conceptually, NAS comprises applying a search strategy on a predefined search space accompanied by a performance evaluation method. The design of search space alone is expected to substantially impact NAS efficiency. We consider neural networks as graphs and find a correlation between the presence...
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Advanced Control Structures of Turbo Generator System of Nuclear Power Plant
PublikacjaIn the paper a synthesis of advanced control structures of turbine and synchronous generator for nuclear power plant working under changing operating conditions (supplied power level) is presented. It is based on the nonlinear models of the steam turbine and synchronous generator cooperating with the power system. Considered control structure consists of multi-regional fuzzy control systems with local linear controllers, including...
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Advanced Control Structures of Turbo Generator System of Nuclear Power Plant
PublikacjaIn the paper a synthesis of advanced control structures of turbine and synchronous generator for nuclear power plant working under changing operating conditions (supplied power level) is presented. It is based on the nonlinear models of the steam turbine and synchronous generator cooperating with the power system. Considered control structure consists of multi-regional fuzzy control systems with local linear controllers, including...
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Inverse and forward surrogate models for expedited design optimization of unequal-power-split patch couplers
PublikacjaIn the paper, a procedure for precise and expedited design optimization of unequal power split patchcouplers is proposed. Our methodology aims at identifying the coupler dimensions that correspond to thecircuit operating at the requested frequency and featuring a required power split. At the same time, thedesign process is supposed to be computationally efficient. The proposed methodology involves two typesof auxiliary models (surrogates):...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublikacjaIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...