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Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution to increase electricity coverage in these areas. An extraordinary case occurs in the Galapagos Islands due to their biodiversity in flora and fauna, where the primary energy source comes from fossil fuels despite their significant amount of solar resources. Therefore, RES use, especially photovoltaic (PV) and wind power, is essential to cover the required load demand without negatively affecting the islands' biodiversity. In this regard, the design and installation planning of PV systems require perfect knowledge of the amount of energy available at a given location, where power forecasting plays a fundamental role. Therefore, this paper presents the design and comparison of different deep learning techniques: long-short-term memory (LSTM), LSTM Projected, Bidirectional LSTM, Gated Recurrent Units, Convolutional Neural Networks, and hybrid models to forecast photovoltaic power generation in the Galapagos Islands of Ecuador. The proposed approach uses an optimized hyperparameter-based Bayesian optimization algorithm to reduce the forecast error and training time. The results demonstrate the accurate performance of all the methods by achieving a low-error short-term prediction, an excellent correlation of over 99%, and minimizing the training time. [ABSTRACT FROM AUTHOR]

The battery State of Charge (SoC) is critical information to overcome agricultural robots' limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%. [ABSTRACT FROM AUTHOR]

One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators’ decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power’s systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support. [ABSTRACT FROM AUTHOR]

This article evaluates the power conversion efficiency of the buck three-level dc–dc converter, when operating in unbalanced bipolar dc microgrids. Bipolar dc microgrids adopt a positive, neutral, and negative wire to double the power transfer capability, reduce conduction losses, and provide two voltage levels. Additional converters are however required to balance the pole-to-neutral voltages in the presence of unbalanced loading conditions. Previous work has shown that the buck three-level dc–dc converter features voltage balancing capability and can serve, at the same time, as an interface for battery storage and photovoltaic systems for example. Nevertheless, available conversion loss models are only valid for balanced loading conditions. Therefore, this article derives a conversion loss model for a buck three-level dc–dc converter, also valid in unbalanced conditions. The model is decomposed in balanced and unbalanced components in order to separate losses arising in balanced and unbalanced conditions. Furthermore, the model accounts for nonideal common-mode currents as experimental results will reveal that they have a profound impact on the conversion efficiency in unbalanced and balanced conditions. [ABSTRACT FROM AUTHOR]

This study presents the performance comparison of two scenarios to obtain high voltage gain. A high step-up converter (HSU) and a cascaded boost converter are chosen for this study. For analytical analysis, a loss model for both converters is developed on MATLAB. Gallium nitride- based prototypes of HSU and cascade boost converter provided the validation of the analytical analysis. Based on the theoretical evaluation and practical tests, efficiencies of both converters are expressed in terms of their voltage gain to find the optimal point of operation. Results showed that the cascaded boost converter is more efficient than the HSU converter at higher voltage gain values. However, the HSU converter has a higher energy density and lower switch voltage stress than those of the cascaded boost converter. [ABSTRACT FROM AUTHOR]

A new implementation of an iron-loss model for laminated magnetic cores in the MATLAB/Simulink environment is proposed in this paper. The model is based on numerically solving a one-dimensional diffusion problem for the eddy currents in the core lamination and applying an accurate hysteresis model as the constitutive law. An excess loss model is also considered. The model is identified merely based on the catalog data provided by the core material manufacturer. The implementation is validated with analytical and finite-element models and experimentally in the case of a toroidal inductor supplied from a GaN FET full-bridge inverter with 5–500 kHz switching frequencies and a deadtime of 300 ns. Despite the simple identification, a good correspondence is observed between the simulated and measured iron losses, the average difference being 3.3% over the wide switching frequency range. It is shown that accounting for the skin effect in the laminations is significant, in order to correctly model the iron losses at different switching frequencies. Some differences between the measured and simulated results at high switching frequencies are also discussed. The model is concluded to be applicable for designing and analyzing laminated magnetic cores in combination with power-electronics circuits. The Simulink models are openly available. [ABSTRACT FROM AUTHOR]

Multiphase topologies are preferably employed in power conversion systems to lessen the per-phase circuit current, conduction losses, devices thermal stresses, and to reduce the output current ripples. A multiphase LLC resonant dc/dc converter usually possesses a number of magnetic cores equal to the number of phases. These magnetic cores are the major contributors to supply volume, weight, and size. For these reasons, circuit designers tend to select the topologies that have a minimal number of magnetic cores. In this paper, the authors aim to promote the industrial applications of the three-phase LLC resonant converter by integrating three transformers into a single commercially available magnetic core to reduce the volume, weight, and cost of the power converter. A comprehensive magnetic analysis for the three-phase-integrated transformer is conducted. Finite element method (FEM) simulation and experimental tests are carried out to validate the proper operation of the integrated transformer utilized in a 390-/12-V to 500-W prototype. Furthermore, the power losses distribution has been presented. The proposed integrated transformer has been proven effective, and it realized a uniform thermal distribution along the core compared to the three-discrete transformers. [ABSTRACT FROM AUTHOR]

Reconfiguration, sizing and downsizing of the storage unit, in electrically propelled vehicles, are techniques that have been reported effective to improve the shelf life and performance of the storage cells. However, these solutions might decrease the rated voltage of the storage unit and therefore DC-DC converters with high voltage gain are suitable solutions to connect these low-voltage units to the motor drive, keeping a good performance of the vehicle. Moreover, parasitic resistances presented in the components of these converters have proved to influence the efficiency and the voltage gain of the converter. The ideal voltage gain of four high step-up converters is analysed, derived, and compared. These converters were selected because of their potential to be applied in electric mobility and their similarity in the techniques that use to achieve high voltage gain: interleaving phases and magnetic integration. One of the analysed topologies is proposed by the authors. Afterwards, the parasitic resistance effect is analysed to obtain the non-ideal voltage gain and the efficiency of these four topologies. Finally, the topology that presents the best trade-off between the non-ideal voltage gain and the efficiency is experimentally tested with a 100 W prototype [ABSTRACT FROM AUTHOR]

High power density and high efficiency in dc/dc converters are required in various applications such as the automotive application. Interleaved multiphase circuits with integrated magnetic components can fulfill these requirements because passive components occupying significant space in power converters can be downsized without high-switching frequency driving of power devices. However, dc-biased magnetization is a drawback of integrated magnetic components because of unbalanced inductor average currents. This imbalance arises from the tolerance among the phase components. To overcome this problem, inductor average current control is implemented in interleaved multiphase dc/dc converters. Nevertheless, the imbalance cannot be completely eliminated because the current sensors inserted into each phase have gain errors. The purpose of this paper is to present a magnetic design method to improve the immunity to unbalanced currents. A comprehensive analysis is carried out with two main objectives: to prevent magnetic saturation, which may arise due to the current unbalance and to downsize the magnetic components by selecting the optimal coupling coefficient taking into consideration the maximum permissible percentage of unbalanced currents. Simulation case studies are presented to support the analysis. Finally, a 1-kW prototype of the interleaved boost converter is built to validate the accuracy of the design method. [ABSTRACT FROM PUBLISHER]

Recently, novel magnetic materials have been developed for high-efficiency and high power density electric motors. In addition, next-generation semiconductor devices, like silicon carbide (SiC) or gallium nitride (GaN), have been introduced for power converters due to their high-frequency operation. Therefore, high-frequency operation of new magnetic materials is possible when they are driven by GaN or SiC inverters. Nevertheless, iron loss characterization of magnetic materials when they are excited by high-frequency signals have not been conducted yet. This paper introduces the iron losses characterization of a magnetic material at high carrier frequency excitation using a GANFET inverter. This characterization was carried out by the experimental evaluation of iron losses at carrier frequencies from 5 to 500 kHz at different deadtimes. As a result of the measurements, iron losses seem to have a trend to increase at high carrier frequencies and large deadtimes. In addition, filtering is introduced and it seems to be an effective technique for reducing iron losses. [ABSTRACT FROM AUTHOR]

One of the main problems in electric vehicles is the volume of their electrical systems because their bulky components carry additional mass and high cost to the total system. On this topic, interleaving-phases and magnetic coupling techniques have been reported as effective methods for increasing the power density of the DC-DC converters that work between the storage unit and the motor inverter. In that sense, a volume assessment of these topologies would provide a better understanding of the problems to be faced when an electric power train is designed. In this paper, a volume modelling methodology is introduced with the purpose of comparing four different DC-DC converters: Single-Phase Boost, Two-Phase Interleaved with non-coupled inductors, Loosely Coupled Inductor (LCI), and Integrated Winding Coupled Inductor (IWCI). The analysis considers the volume of magnetic components, power devices (conventional and next-generation), cooling devices and capacitors. The methodology can be used as a part of an optimization procedure to minimize the volume of DC-DC converters. Conclusively, LCI and IWCI were found effective to miniaturize power converters with a power density of 8.4 W/cc and 9.66 W/cc, respectively. Moreover, a maximum efficiency of 98.04% and 97.61% was obtained for a 1kW LCI and IWCI prototypes, respectively. [ABSTRACT FROM AUTHOR]

Interleaved dc–dc converters with integrated magnetic components capable to achieve high power density have recently gained attention in automotive applications for eco-friendly transportation. These circuits may contribute to the downsizing of powertrains for electric vehicles (EVs) and hybrid electric vehicles (HEVs) using close-coupled inductors (CCIs) and loosely coupled inductors (LCIs). In this study, a novel core model for size and loss calculation of the interleaved converter with integrated winding coupled inductors (IWCIs) is proposed to assess the size of magnetic components. This assessment was carried out using the parameters of a 1-kW circuit. As a result, interleaved converters with IWCIs proved to be more effective than the converters with LCIs and CCIs for downsizing the magnetic components. In contrast, it was also found that the interleaved converter with LCIs achieved downsizing of the magnetic component at a duty ratio close to 50%. Finally, the effectiveness of the calculation model was validated through experimental tests. [ABSTRACT FROM PUBLISHER]

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index. [ABSTRACT FROM AUTHOR]

State-of-the-art reliability assessment typically starts from a given circuit topology, for which the most suitable components are selected using a Physics of Failure analysis. This paper, however, addresses the topology selection stage, which is the foundation in designing reliable converters. Based on an overview of the reliability performance of different components, a methodology is presented as a guideline for comparing topologies to one another. The focus is directed at practical consequences associated with certain designs. Furthermore, an overview is provided on the latest developments in component technology reliability improvements. The developed methodology is mainly intended for demanding applications, where long lifetimes are required or elevated ambient temperatures are present. After the topology selection, an overview of possibilities is given that allows further increasing converter availability. Finally, the methodology is applied to the design of module level converters for building integrated photovoltaics, which is a high temperature application with a high desired lifetime. A prototype and experimental results are presented. [ABSTRACT FROM AUTHOR]