Airan Frances

Due to the increasing presence of DC-DC power electronic converters in power distribution systems, having accurate models is essential for the analysis and design process. Black-box modeling approaches offer a convenient solution when the available information is limited, which is typically the case for commercial converters. Moreover, converters are known for their non-linear nature, which limits the applicability of linear models in applications where the operating point changes substantially, like in DC microgrids with renewable sources. This work proposes a model that improves on limitations of previous black-box non-linear models, particularly, the accuracy at capturing strong dynamic nonlinearities and a simple implementation. The model is based on performing a dynamic selection of local Wiener-Hammerstein models according to the state variables of the converter. The switching among models is accompanied by a state transfer to achieve a continuity in the response. Procedures for implementing local models of different order and identifying them are also addressed. The techniques are validated with experimental tests carried out on asynchronous buck and boost converters.

Membraneless micro redox flow batteries are a promising technology that can improve traditional redox flow batteries performance. However, a precise modeling and control of the microfluidic dynamics is a complex task for which only open loop control strategies can be found in the literature. In this work, a strategy for the adaptive modeling of the microfluidic dynamics of a membraneless micro redox flow battery is presented. The model is based on proposed equations whose constant parameters are identified using grey-box modeling techniques. Intrinsic limitations of applying these equations to the real system (stochasticity of the microfluidic system, non-considered variables, non-linearities) are overcome by adapting the model through the addition of correction factors calculated in real time. In the proposed use case, an extended Kalman filter is used to estimate the factors. Also, the model with real-time adaption is proven to be suitable for control design using a model-based control technique such as incremental state optimal control. The modeling and the controller adequacy are validated in simulation and real experiments. Model adequacy to real system is demonstrated, and it allows a model-based control design that improves microfluidic response, which is expected to result in higher battery efficiency and reactant conversion ratio.

Power electronic converters are envisaged to be key enablers of modern electric power distribution systems. Grid-connected three-phase inverters are widely used in smart grids and microgrids, but also in standard grids. They provide controllability and dynamic decoupling capabilities, which are fundamental in the integration of renewable sources and storage systems and in specialized applications such as FACTS. Nevertheless, power electronics-based systems can exhibit dynamic interactions, which may lead to power quality issues. Although the electrical model of each element of these systems is important for their system-level analysis, they are rarely available. Blackbox modeling strategies are useful for obtaining behavioral models of commercial electronic converters. Most blackbox modeling strategies are focused on dc–dc electronic converters; however, some works have extended these concepts to three-phase converters by means of the dq framework, under the assumption of symmetrical and balanced conditions. This work proposes a new structure in the sequence domain to represent the dynamic behavior of grid-connected commercial converters in asymmetrical conditions. Experimental tests have been performed on a three-phase inverter to identify its blackbox model and its performance has been validated during an asymmetric voltage sag.

Membraneless micro redox flow batteries are an incipient technology that has been shown to extend some properties of traditional redox flow batteries. Due to their microfluidic scale and the absence of membrane, the fluid dynamics operation is critical in the electrical response. In this work, an electrical model is established to evaluate the influence on three battery performance metrics: steady-state power, power transient dynamics, and mixing and self-discharge losses. First, an equivalent electrical circuit, derived from a state-of-the-art regular battery equivalent circuit, is defined by studying the influence of flow changes on its impedances and source, aggregating it as a variable. Then, empirical data are used to demonstrate the proposed equations defining the variation of the electrical response relative to fluid dynamics, and their parameters are identified with grey box methods. The steady-state power model incorporates the interphase position, extending conventionally used redox flow batteries expressions, such as Faraday’s Law and Nersnt’s equation, for the membraneless analysis. A transient response model is built, which becomes effectively relevant in intermittent power applications (such as many renewable energy storage ones). Finally, mixing and self-discharge losses are evaluated with the variation state of charge at the outputs of the cell, using spectrophotometry measurements, and compared with flowmeter mixing values. This demonstrates that flow-rate values can provide a precise quantification of these losses. The electrical model with dependent parameters from the three fluid dynamics analyses can be used to evaluate the performance of micro membraneless redox flow batteries and their response to fluidic operation.

The proliferation of renewable energy sources is promoting the use of dc-dc power electronic converters in power distribution systems. It is well-known that the interconnection of power electronic converters can be a source of instabilities. Traditionally, impedance-based stability analyses are performed using either the analytical description or the identification of the output impedance of the source converter and the input admittance of the load converter. However, this methodology is restricted by the small-signal conditions, which are usually violated in this kind of systems due to the high variability imposed by the renewable energy sources. In nonlinear systems, the stability analysis usually consists on the definition of the region of attraction around a stable equilibrium point. In the literature, the analysis of nonlinear systems is almost exclusively applied to analytical systems, where all the details are known. This paper proposes a black-box methodology to obtain the region of attraction of the equilibrium point of commercial off-the-shelf dc-dc converters working in power distribution systems. First, optimization algorithms are used to identify the parameters of a predefined structure, such that it is able to reproduce the dynamic behavior of the system. The parameters identified can be used to create a switching equivalent model that accounts for the nonlinearities produced by the switching process of the converters. Second, the bisection method is implemented to minimize the number of simulations needed to determine the region of stability accurately. The proposed methodology has been validated both with simulations and with an experimental setup.

At present, it is safe to say that the future belongs to sustainable technologies. Electric vehicles are one of the most representative sustainable solutions for mobility. Some of the many advantages they offer are a clean operation, a profit gain from a possible power return to the grid in vehicle to grid applications, grid power support, etc. As it is expected, an increase in the production of electric cars, and other battery-based systems, lead to an increment in battery chargers. Therefore, energy distributors must deal with many different schemes in the grid and with the amount of energy demanded from a fleet of cars and other electric vehicles. This paper deals with the modeling of AC/DC battery chargers by identifying the transfer functions in any operating condition without knowing the specific details inside those chargers. This approach is called black-box modeling and the strategy is based on the information provided by the input and output variables of the charger. The research illustrates the flexibility of the model to work with single-phase and three-phase AC/DC chargers which are linked to on-board and off-board charging stations. An extensive set of tests is shown, verifying the accuracy of the proposed models in a wide variety of operating conditions.

DC electric power distribution is becoming popular due to the proliferation of renewable sources and storage elements in applications such as electric vehicles, ships, aircrafts, microgrids, etc. These systems are characterized by a high integration of power electronic converters. From a system-level perspective, it would be desirable to design this kind of systems using commercial-off-the shelf converters. However, in general, the manufacturers do not provide a behavioral model of the devices in order to analyze the dynamic behavior of the interconnected system before the actual implementation. In the literature, several blackbox modeling techniques have been proposed to overcome this lack of information. This paper proposes the integration of dynamic weighting functions to the polytopic model in order to improve the accuracy of the behavioral models when the input variables change sharply. A boost converter is used as case study and the performance of the proposed model is compared with the most relevant techniques that can be found in the literature.

Modern electric power distribution systems are progressively integrating electronic power converters. However, the design of electronic-power-converter-based systems is not a straightforward task, as the interactions among the different converters can lead to dynamic degradation or instabilities. In addition, electric power distribution systems are expected to consist of commercial-off-the-shelf converters, which implies limited information about the dynamic behavior of the devices. Large-signal blackbox modeling approaches have been proposed in order to obtain accurate dynamic models of commercial converters that can be used for system-level analyses. However, most of the works are focused on DC-DC converters. In this work, a large-signal blackbox model is proposed to model grid-connected three-phase DC-AC converters. An experimental setup has been used to demonstrate the limitations of small-signal models and the capability of the proposed modeling approach to capture the dynamic behavior of the converter when large perturbations are applied. Finally, the automation of the model identification process is discussed.

The power electronic based dc distribution systems are becoming an interesting solution in many applications as electric vehicles (automobiles, planes, ships, etc.), data centers, and microgrids. On the one hand, due to the complexity and the amount of these installations, the use of sophisticated commercial-off-the-shelf converters is very convenient. On the other hand, the use of commercial converters complicates the system-level analysis of these systems, due to the lack of detailed information about the dynamic response of these devices. A solution to this issue is the blackbox modeling approach, which is able to identify behavioral models of the converters from their response to specific tests. However, the approaches available in the literature are not able to account for strong nonlinear dynamic behaviors, such as the ones shown by converters with discontinuous and continuous conductions modes or the ones that integrate different control modes. In this paper, a blackbox Parameter Varying Transfer Functions (PVTF) model is proposed in order to obtain very accurate models of commercial converters in dc microgrid applications.