18 research outputs found

    RBF Neural Network Approach for Identification and Control of DC Motors

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    Modelling and diagnosis of solid oxide fuel cell (SOFC)

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    The development of mathematical models and numerical simulations is crucial to design improvement, optimization, and control of solid oxide fuel cells (SOFCs). The current study introduces a novel and computationally efficient pseudo-two-dimensional (pseudo-2D) model for simulating a single cell of a high-temperature hydrogen-fueled SOFC. The simplified pseudo-2D model can evaluate the cell polarization curve, species concentrations along the channel, cell temperature, and the current density distribution. The model takes the cell voltage as an input and computes the total current as an output. A full-physics three-dimensional model is then developed in ANSYS Fluent, with a complete step-by-step modeling approach being explained, to study the same cell with the identical operating conditions. The 3D model is validated against the other numerical and experimental studies available in the literature. It is shown that although the pseudo-2D solution converges significantly faster in comparison with the 3D case, the results of both models thoroughly match especially for the case of species distributions. The simplified model was then used to conduct sensitivity analysis of the effects of multi-physiochemical properties of porous electrodes on the polarization curve of the cell. A systematic inverse approach was then used to estimate the mentioned properties by applying the pattern search optimization algorithm to the polarization curve found by the pseudo-2D model. Finally, nine different input parameters of the model were changed to find the hydrogen distribution for each case, and a huge dataset of nearly half a million operating points was generated. The data was successfully employed to design a novel classifier-regressor pair as a virtual hydrogen sensor for online tracking of hydrogen concentration along the cell to avoid fuel starvation

    Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

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    Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

    Contributions for microgrids dynamic modelling and operation

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

    Comprehensive summary of solid oxide fuel cell control : a state-of-the-art review

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    Hydrogen energy is a promising renewable resource for the sustainable development of society. As a key member of the fuel cell (FC) family, the solid oxide fuel cell (SOFC) has attracted a lot of attention because of characteristics such as having various sources as fuel and high energy conversion efficiency, and being pollution-free. SOFC is a highly coupled, nonlinear, and multivariable complex system, and thus it is very important to design an appropriate control strategy for an SOFC system to ensure its safe, reliable, and efficient operation. This paper undertakes a comprehensive review and detailed summary of the state-of-the-art control approaches of SOFC. These approaches are divided into eight categories of control: proportional integral differential (PID), adaptive (APC), robust, model predictive (MPC), fuzzy logic (FLC), fault-tolerant (FTC), intelligent and observer-based. The SOFC control approaches are carefully evaluated in terms of objective, design, application/scenario, robustness, complexity, and accuracy. Finally, five perspectives are proposed for future research directions

    Control of Proton Exchange Membrane Fuel Cell System

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    265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system

    Tehnike računarske inteligencije u modeliranju i identifikaciji indikatora ponašanja brane

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    Indikatori ponašanja brane su relevantne veličine, čijim se praćenjem utvrđuje da li je stvarno stanje brane u eksploataciji u saglasnosti sa onim što je predviđeno i očekivano u fazi projektovanja. Veličine koje se prate treba da se kreću u nekom unapred definisanom opsegu koji garantuje stanje stabilnosti brane. U ovoj disertaciji su predloženi različiti pristupi modeliranja i parametarske identifikacije indikatora ponašanja brane, poput horizontalnih pomeranja i nivoa vode u pijezometrima, tehnikama računarske inteligencije. Prvi pristup je da se linearno preslikavanje uzročnih veličina u indikatore ponašanja, koje se koristi kod višestruke linearne regresije, zameni nelinearnim. Drugi pristup, predložen u ovom radu, zasniva se na primeni postupka parametarske identifikacije nelinearnih sistema. Horizontalna pomeranja i nivoi vode u pijezometrima su nelinearne, složene funkcije uzročnih veličina, pa je za njihovo modeliranje korišćena NARX (Nonlinear Auto Regresive eXogenous- nelinearni auto-regresioni model sa spoljašnjim ulazom) struktura, kojom je opisana široka klasa nelinearnih dinamičkih procesa. Predloženi pristupi formiranja modela primenjeni su za modeliranje i parametarsku identifikaciju horizontalnih pomeranja tačaka brane Bočac, kao i nivoa vode u pijezometrima brana Đerdap II i Prvonek. Nelinearni modeli zasnovani na tehnikama računarske inteligencije implementirani su korišćenjem programskog jezika Java i programskog paketa Matlab. Tehnike računarske inteligencije korišćene u ovom radu su višeslojni perceptron, RBF (RBF - Radial Basis Function – radijalna osnovna funkcija) neuronska mreža i ANFIS (ANFIS - Adaptive-Network-Based Fuzzy Inference System - fazi sistem za zaključivanje zasnovan na adaptivnoj mreži). Nedostajući podaci u skupu merenja mogu biti uzrok problema u okviru procesa učenja i loših performansi dobijenih modela. U cilju nadomeštanja nedostajućih podataka korišćene su tehnike iz domena matematičke statistike. Prisustvo autlajera u mernim podacima ima veliki uticaj na predviđanja podataka koji nedostaju, pa je njihovo prisustvo posebno analizirano. Takođe je analiziran i problem optimizacije ulazno-izlaznih modela, koji podrazumeva određivanje broja prediktora i dimenzije regresionog vektora, kao i broja parametara neuronskih mreža i neuro-fazi sistema. Performanse modela, formiranih na osnovu predloženog koncepta, poređeni su sa rezultatima dobijenim drugim metodama modeliranja istih indikatora ponašanja prikazanim u relevantoj literaturi objavljenoj u poslednjih nekoliko godina. Na osnovu rezultata zaključeno je da je moguće kreirati i obučiti modele zasnovane na tehnikama računarske inteligencije koji će sa velikom preciznošću predviđati bitne indikatore ponašanja brane.The dam behavior indicators are relevant factors whose monitoring indicates whether the actual operational state of the dam is in accordance with what is expected and anticipated in the design phase. Such indicators should move in a predefined range, in order to guarantee stability of the dam. This dissertation proposes different approaches to modeling and parametric identification of the dam behavior indicators, such as radial displacements or piezometric water levels, using the techniques of artificial intelligence. The first approach is to replace linear mapping of causal variables into behavior indicators, which is used in multiple linear regression, with nonlinear. The second approach proposed in this paper is based on applying the method of parametric nonlinear system identification. Radial displacements and piezometric water levels are nonlinear, complex functions of causal variables, so for their modeling NARX (Nonlinear Auto Regresive eXogenous), which is employed to describe a wide class of nonlinear dynamic systems, is used. These proposed approaches are used for modeling and parametric identification of radial displacements of dam Bočac, and piezometric water levels of dams Iron Gate II and Prvonek. Nonlinear models based on artificial intelligence techniques have been implemented using the Java programming language and MATLAB. Artificial intelligence techniques used in this work are the multilayer perceptron, RBF (Radial Basis Function) neural network and ANFIS (Adaptive-Network-Based Fuzzy Inference System). The presence of missing data in a set of measurements may be causing problems in the learning process and the poor performance of the obtained models. In order to predict the missing data, the techniques of mathematical statistics have been used. Outliers present in a set of measurements have a big effect on the prediction of missing data, and their presence is specifically analyzed. The problem of optimizing the inputoutput model, which involves determining the number of predictors and dimensions of the regression vector, and the number of parameters of neural networks and neuro-fuzzy systems, is also analyzed. The performance of the models, formed on the basis of the proposed concept, are compared with those obtained by other methods of modeling the same behavioral indicators presented in relevant accompanying literature published in the last few years. Based on the results, it was concluded that it is possible to create and train models based on computational intelligence techniques to predict with great accuracy the essential dam behavior indicators

    PEMFC performance improvement through oxygen starvation prevention, modeling, and diagnosis of hydrogen leakage

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    Catalyst degradation results in emerging pinholes in Proton Exchange Membrane Fuel Cells (PEMFCs) and subsequently hydrogen leakage. Oxygen starvation resulting from hydrogen leaks is one of the primary life-limiting factors in PEMFCs. Voltage reduces as a result of oxygen starvation, and the cell performance deteriorates. Starved PEMFCs also work as a hydrogen pump, increasing the amount of hydrogen on the cathode side, resulting in hydrogen emissions. Therefore, it is important to delay the occurrence of oxygen starvation within the Membrane Electrode Assembly (MEA) while simultaneously be able to diagnose the hydrogen crossover through the pinholes. In this work, first, we focus on catalyst configuration as a novel method to prevent oxygen starvation and catalyst degradation. It is hypothesized that the redistribution of the platinum catalyst can increase the maximum current density and prevent oxygen starvation and catalyst degradation. Therefore, a multi-objective optimization problem is defined to maximize fuel cell efficiency and to prevent oxygen starvation in the PEMFC. Results indicate that the maximum current density rises about eight percent, while the maximum PEMFC power density increases by twelve percent. In the next step, a previously developed pseudo two-dimensional model is used to simulate fuel cell behavior in the normal and the starvation mode. This model is developed further to capture the effect of the hydrogen pumping phenomenon and to measure the amount of hydrogen in the outlet of the cathode channel. The results obtained from the model are compared with the experimental data, and validation shows that the proposed model is fast and precise. Next, Machine Learning (ML) estimators are used to first detect whether there is a hydrogen crossover in the fuel cell and second to capture the amount of hydrogen cross over. K Nearest Neighbour (KNN) and Artificial Neural Network (ANN) estimators are chosen for leakage detection and classification. Eventually, a pair of ANN classifier-regressor is chosen to first isolate leaky PEMFCs and then quantify the amount of leakage. The classifier and regressor are both trained on the datasets that are generated by the pseudo two-dimensional model. Different performance indexes are evaluated to assure that the model is not underfitting/overfitting. This ML diagnosis algorithm can be employed as an onboard diagnosis system that can be used to detect and possibly prevent cell reversal failures

    A new high power efficient electronic converter for fuel cell applications

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    Documento confidencial. Não pode ser disponibilizado para consultaTese de mestrado integrado. Engenharia Electrotécnica e Computadores. Faculdade de Engenharia. Universidade do Porto. 201
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