1,076 research outputs found

    Contactless excitation for electric machines: high temperature superconducting flux pumps

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    With the intensification of global warming and climate change, the pace of transformation to a neutral-emission society is accelerating. In various sectors, electrification has become the absolute tendency to promote such a movement, where electric machines play an important role in the current power generation system. It is widely convinced that electric machines with very high power density are essential for future applications, which, however, can be hardly achieved by conventional technologies. Owing to the maturation of the second generation (2G) high temperature superconducting (HTS) technologies, it has been recognized that superconducting machine could be a competitive candidate to realize the vision. One significant obstacle that hinders the implementation of superconducting machines is how to provide the required magnetic fields, or in other words, how to energise them appropriately. Conventional direct injection is not suitable for HTS machines, because the current leads would bridge ambident temperature to the cryogenic environment, which can impose considerable heat load on the system and increase the operational cost. Thus, an efficient energisation method is demanded by HTS machines. As an emerging technology that can accumulate substantial flux in a closed loop without any physical contact, HTS flux pumps have been proposed as a promising solution. Among the existing developed HTS flux pumps, rotary HTS flux pumps, or so-called HTS dynamo, can output non-zero time-averaged DC voltage and charge the rest of the circuit if a closed loop has been formed. This type of flux pump is often employed together with HTS coils, where the HTS coils can potentially work in the persistent current mode, and act like electromagnets with a considerable magnetic field, having a wide range of applications in industry. The output characteristics of rotary HTS flux pumps have been extensively explored through experiments and finite element method (FEM) simulations, yet the work on constructing statistical models as an alternative approach to capture key characteristics has not been studied. In this thesis, a 2D FEM program has been developed to model the operation of rotary HTS flux pumps and evaluate the effects of different factors on the output voltage through parameter sweeping and analysis of variance. Typical design considerations, including the operating frequency, air gap, HTS tape width, and remanent flux density have been investigated, in particular, the bilateral effect of HTS tape width has been discovered and explained by looking at the averaged integration of the electric field over the HTS tape. Based on the data obtained from various simulations, regression analysis has been conducted through a collection of machine learning methods. It has been demonstrated that the output voltage of a rotary HTS flux pump can be obtained promptly with satisfactory accuracy via Gaussian process regression, aiming to provide a novel approach for future research and a powerful design tool for industrial applications using rotary HTS flux pumps. To enhance the applicability of the proposed statistical models, an updated FEM program has been built to take more parameters into account. The newly added parameters, namely the rotor radius and the width of permanent magnet, together with formerly included ones, should have covered all the key design parameters for a rotary HTS flux pump. Based on data collected from the FEM model, a well-trained semi-deep neural network (DNN) model with a back-propagation algorithm has been put forward and validated. The proposed DNN model is capable of quantifying the output voltage of a rotary HTS flux pump instantly with an overall accuracy of 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behaviour of rotary HTS flux pumps by integrating all design parameters, and the output characteristics of rotary HTS flux pumps have been successfully demonstrated and visualized using this model. Compared to conventional time-consuming FEM-based numerical models, the proposed DNN model has the advantages of fast learning, accurate computation, as well as strong programmability. Therefore, the DNN model can greatly facilitate the design and optimization process for rotary HTS flux pumps. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of rotary HTS flux pumps. A new variant inspired by the working principles of rotary HTS flux pumps has been proposed and termed as stationary wave HTS flux pumps. The superiority of this type is that it has a simple structure without any moving components, and it utilises a controllable current-driven electromagnet to provide the required magnetic field. It has been demonstrated that the origin of the output voltage is determined by the asymmetric distribution of the dynamic resistance in the HTS tape, for which the electromagnet must be placed at such a position that its central line is not aligned with that of the HTS tape. A numerical model has been built to simulate the operation of a stationary wave HTS flux pump, based on which the output characteristics and dynamic resistance against various parameters have been investigated. Besides, accurate and reliable statistical models have been proposed to predict the open circuit voltage and effective dynamic resistance by adapting the previously developed machine learning techniques. The work presented in this PhD thesis can bring more insight into HTS flux pumps as an emerging promising contactless energisation technology, and the proposed statistical models can be particularly useful for the design and optimization of such devices

    A Review “The Research Status of Switched Reluctance Generator"

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    Switched Reluctance Generators (SRGs) have become quite popular lately because they offer some big advantages compared to regular generator technologies. This comprehensive review paper is dedicated to illuminating the latest developments in the realm of SRGs, offering a deep dive into their operational intricacies, diverse types, design principles, control strategies, versatile applications, and the formidable challenges that lay ahead. The paper begins by explaining in simple terms how Switched Reluctance Generators (SRGs) work and uncovering the main ideas that make them special and different from other generators. It then proceeds to categorize SRGs into various types, providing an insight into the diverse design features that distinguish them. This foundational understanding sets the stage for a more profound exploration of SRGs. The review focuses significantly on studying the complex control methods that manage SRGs, exploring the advanced techniques used to make them work better and more efficiently. As SRGs continue to evolve, their applicability spans a broad spectrum, ranging from clean energy generation to enhancing transportation systems and powering industrial machinery. This review carefully explains how SRGs can help change many different industries for the better. It shows how they have a lot of potential to make big improvements. Nevertheless, SRGs do not come without their share of challenges, encompassing issues related to efficiency, reliability, and the complexities inherent in control strategies. This review serves as an informative exploration of SRGs, highlighting their potential across various industries. It acknowledges their development and urges them to keep exploring their promising abilities. As switched reluctance generators become more popular, this discussion provides a detailed guide for those interested in understanding how they work and navigating their complex features

    Development of the C-GEN generator technology for vertical axis wind turbines

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    In this thesis, 5 MW, 7.5MWand 10MWat 6 rpm C-GEN generator models were designed and optimized respectively for a vertical axis wind turbine. Although VAWTs have lower rotational speed than HAWTs, the offered C-GEN VAWT generators have significant higher power density compared to conventional PM and superconducting HAWT generators of equivalent power. The inner radius of the 5 MW C-GEN generator is 5.35 m and mass is 41.2 tonnes. The inner radius of the 7.5 MW C-GEN generator is 5.35 m and active mass is 44.1 tonnes. The inner radius of the 10 MW C-GEN generator is 7.50 m and active mass is 41.6 tonnes. Annual generation results show that offshore VAWT can generate as much energy as HAWT of the same power. In addition, by the end of 2022, the most powerful single offshore HAWT is 14 MW and due to the tower head mass of HAWTs, it is limited to increase the power further. Multi-power platform VAWTs can take the power of a single turbine further. The C-GEN model with wavy and comb steel structure has higher power density than the C-GEN model with straight steel structure. In addition, in the multi-stage C-GEN models, the comb steel structure will allow the passage of air between the stages, and since the wavy steel structure has more surface area than straight steel, it can help to increase the thermal performance of the machine. Since machine mass is an important factor in aviation, automotive propulsion systems and renewable energy converters, these structures can provide advantage. The machines are analysed and optimised electromagnetically using 2-D FEA simulations. A software algorithm has been developed for the simulations. This algorithm allows any electrical machine to be modelled and optimized easily and quickly. In addition, this algorithm can be applied to 3-D models and other branches of engineering such as mechanical, civil, naval, aircraft and etc. for use CAD and FEA models

    INTELLIGENT MAINTENANCE OF ELECTRIC VEHICLE BATTERY CHARGING SYSTEMS AND NETWORKS CHALLENGES AND OPPORTUNITIES

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    Electric Vehicles (EVs) have become a trending topic in recent years due to the industry’s race for competitive pricing as well as environmental awareness. These concerns have led to increased research into the development of both affordable and environmentally friendly EV technology. This paper aims to review EV-related issues beginning with the component level, through the system level, based on intelligent maintenance aspects. The paper will also clarify the existing gaps in practical applications and highlight the potential opportunities related to the current issues in EVs for the EV industry moving forward. More specifically, we will briefly start with an overview of the fast-growing EV market, showing the urgent demand for Prognostics and Health Management (PHM) applications in the EV industry. At the component level, the issues of the major components such as the motor, battery, and charging system in EVs are elaborated, and the relevant PHM research of these components is surveyed to show the development in the era of EV expansion. Moreover, the impact of an increasing number of EVs at the system level such as power distribution systems and power grid are explored to uncover possible research in the future. The combination of existing PHM techniques and robust measurement or feature extraction methods can provide better solutions to address the motor, battery, or transformer issues at the component level. A comprehensive optimization and cybersecurity strategy will help to address the issues of the whole network at a system level. Four aspects of vision in the overall charging network – battery innovation, charging optimization, infrastructure evolution, and sustainability – that cover the demands of research in new battery materials, innovative charging techniques, new architectures of the charging network, and reliable waste treatment mechanisms are outlined. A conclusion is reached in this paper by summarizing the opportunities for future EV research and development

    Control solutions for multiphase permanent magnet synchronous machine drives applied to electric vehicles

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    207 p.En esta tesis se estudia la utilización de un accionamiento eléctrico basado en una máquina simétrica dual trifásica aplicada al sistema de propulsión de un vehículo eléctrico. Dicho accionamiento está basado en una máquina síncrona de imanes permanentes interiores. Además, dispone de un bus CC con una configuración en cascada. Por otra parte, se incorpora un convertidor CC/CC entre el módulo de baterías y el inversor de seis fases para proveer el vehículo con capacidades de carga rápida, y evitando al mismo tiempo la utilización de semiconductores de potencia con altas tensiones nominales. En este escenario, el algoritmo de control debe hacer frente a las no linealidades de la máquina, proporcionando un comando de consigna preciso para todo el rango de par y velocidad del convertidor. Por lo tanto, deben tenerse en cuenta los efectos de acoplamiento cruzado entre los devanados, y la tensión de los condensadores de enlace en cascada debe controlarse y equilibrarse activamente. En vista de ello, los autores proponen un novedoso enfoque de control que proporciona todas estas funcionalidades. La propuesta se ha validado experimentalmente en un prototipo a escala real de accionamiento eléctrico de 70 kW, probado en un laboratorio y en un vehículo eléctrico en condiciones reales de conducción.Tecnali

    IEOM Society International

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    IEOM Society Internationa

    Improving Power Delivery of Grid-Connected Induction Machine Based Wind Generators under Dynamic Conditions Using Feedforward Linear Neural Networks

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    In the conventional grid-connected Wind Energy Conversion System (WECS), the generator side inverter is typically controlled via Field Oriented Control (FOC), while Voltage Oriented Control (VOC) controls the grid side inverter. However, robust operation cannot be guaranteed during sudden changes in wind speeds and weak grid connections. This paper presents a novel method to improve the overall dynamic performance of on-grid induction machine-based wind generators. An online mechanical parameter estimation technique is devised using Recursive Least Squares (RLS) to compute the machine inertia and friction coefficient iteratively. An adaptive feedforward neural (AFN) controller is also proposed in the synchronous reference frame, which is constructed using the estimated parameters and the system's inverse. The output of the neural controller is added to the output of the speed PI controller in the outer loop of the FOC to enhance the speed response of the wind generator. A similar approach is taken to improve the classical VOC structure for the grid-side inverter. In this case, the RLS estimates the equivalent Thevenin's grid impedance in real-time. As for the adaptive action, two identical neural networks are integrated with the inner loop direct and quadrature axis current PI controllers. Under nominal operating conditions, it is observed that the PI+AFN provides a faster settling time for the generator's speed and torque response. Upon being subjected to variations in the wind speed, the PI+AFN outperforms the classical PI controller and attains a lower integral-time error. In addition, the proposed PI+AFN controller has a better ability to maintain the grid-side inverter stability during stochastic variations in grid impedance. One significant advantage of the proposed control approach is that no data for training or validation is required since the neural network weights are directly the output of the RLS estimator. Hardware verification for the improved FOC for wind generators using the adaptive controller is also made using the DSPACE 1007 AUTOBOX platform

    Energy management of hybrid and battery electric vehicles

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    This work focuses on improving the fuel economy of parallel Hybrid Electric Vehicles (HEVs) and dual-motor Electric Vehicles (EVs) through energy management strategies. Both vehicle models have two propulsion branches, each powering a separate axle: An engine and an electric motor in the HEV and two electric motors in the EV. This similarity in the vehicle models emphasises the need for similar energy management solutions. In Part Energy Management of HEVs of this thesis, a high-fidelity parallel Through-The-Road (TTR) HEV model is developed to study and test conventional control strategies. The traditional control strategies serve as a guide for developing novel heuristic control strategies. The Equivalent Consumption Minimisation Strategy (ECMS) is an optimisation-based control strategy used as the benchmark in this part of the work. A family of rule-based energy management strategies is proposed for parallel HEVs, including the Torque-levelling Threshold-changing Strategy (TTS) and its simplified version, the Simplified Torque-levelling Threshold-changing Strategy (STTS). The TTS applies a concept of torque-levelling, which ensures the engine works efficiently by operating with a constant torque as the load demand crosses a certain threshold, unlike the load-following approach commonly used. However, the TTS requires finely tuned constant torque and threshold parameters, making it unsuitable for real-time applications. To address this, two feedback-like updating laws are incorporated into the TTS to determine the constant torque and threshold online for real-time applications. Real-time versions of these strategies, Real-time Torque-levelling Threshold-changing Strategy (RTTS) and Real-time Simplified Torque-levelling Threshold-changing Strategy (RSTTS) are developed using a novel Driving Pattern Recognition (DPR) algorithm. The effectiveness of the RTTS is demonstrated by implementing it on a high-fidelity parallel hybrid passenger car and benchmarking it against ECMS. In Part Energy Management of EVs of the thesis, a low-fidelity model of a novel EV powertrain with two electric propulsion systems, one at each axle, has been developed to study and test its energy management with one of the main conventional optimal control methods, Dynamic Programming (DP). The EV model uses two differently sized traction motors at the front and rear axles. The thermal dynamics of the utilised Permanent Magnet Synchronous Motors (PMSMs) are studied. DP is first implemented onto the Baseline model that does not include any PMSM thermal dynamics, referred to as the Baseline DP, which acts as a benchmark since it is the conventional case. The thermal dynamics of the traction motors are then introduced in the second DP problem formulation, referred to as the Thermal DP, which is compared against the Baseline DP to evaluate the possible benefits of energy efficiency by the more informed energy management optimisation formulation. The best method is chosen to include these thermal dynamics in the overall energy management control strategy without significantly compromising computational time.Open Acces

    BADANIE WPŁYWU UŁAMKOWEJ LICZBY SZCZELIN BIEGUNÓW NA GENERACJĘ TURBINY WIATROWEJ PRZY UŻYCIU ULEPSZONEGO ALGORYTMU OPTYMALIZACJI CĘTKOWANEJ HIENY

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    The design of machines with permanent magnets is actively developing day by day and is often used in wind energy. The main advantages of such variable speed drives are high efficiency, high power density and torque density. When designing a wind generator with two rotors and permanent magnets, it is necessary to solve such a problem as the correct choice of the number of poles and slots to increase efficiency and minimize the cost of the machine. In this work, an improved spotted hyena optimization algorithm is used to obtain the optimal combination of slots and poles. This optimization algorithm makes it possible to obtain the number of fractional slots per pole and evaluate the operating efficiency of a wind generator with a double rotor and ferrite magnets. At the first stage of machine design, various combinations of slots are installed. Next, the optimal combination is selected from various slot-pole combinations, taking into account the Enhanced Spotted Hyena Optimization (ESHO) algorithm, in which a multi-objective function is configured. Accordingly, the multi-objectives are the integration of reverse electromotive force, output torque, gear torque, flux linkage, torque ripple along with losses. Analysis of the results obtained shows that the proposed algorithm for determining the optimal slot combination is more efficient than other slot combinations. It has also been found that the choice of slot and pole combination is critical to the efficient operation of permanent magnet machines.Projektowanie maszyn z magnesami trwałymi aktywnie rozwija się z dnia na dzień i jest często wykorzystywane w energetyce wiatrowej. Głównymi zaletami takich napędów o zmiennej prędkości są wysoka sprawność, wysoka gęstość mocy i gęstość momentu obrotowego. Podczas projektowania generatora wiatrowego z dwoma wirnikami i magnesami trwałymi konieczne jest rozwiązanie takiego problemu, jak prawidłowy dobór liczby biegunów i szczelin w celu zwiększenia wydajności i zminimalizowania kosztów maszyny. W niniejszej pracy zastosowano ulepszony algorytm optymalizacji hieny plamistej w celu uzyskania optymalnej kombinacji szczelin i biegunów. Ten algorytm optymalizacji umożliwia uzyskanie liczby ułamkowych szczelin na biegun i ocenę wydajności operacyjnej generatora wiatrowego z podwójnym wirnikiem i magnesami ferrytowymi. Na pierwszym etapie projektowania maszyny instalowane są różne kombinacje szczelin. Następnie wybierana jest optymalna kombinacja spośród różnych kombinacji szczelin i biegunów, biorąc pod uwagę algorytm Enhanced Spotted Hyena Optimization (ESHO) (ulepszony algorytm optymalizacjihieny cętkowanej hieny), w którym skonfigurowana jest funkcja wielocelowa. W związku z tym, celami wielozadaniowymi są integracja odwrotnej siły elektromotorycznej, wyjściowego momentu obrotowego, momentu obrotowego przekładni, połączenia strumienia, tętnienia momentu obrotowego wraz ze stratami. Analiza uzyskanych wyników pokazuje, że proponowany algorytm określania optymalnej kombinacji szczelin jest bardziej wydajny niż inne kombinacje szczelin. Stwierdzono również, że wybór kombinacji szczelin i biegunów ma kluczowe znaczenie dla wydajnej pracy maszyn z magnesami trwałymi
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