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This paper presents a passivity-based control for the DC/DC Buck-Boost converter-inverter-DC motor system. Such control exploits the energy structure associated with the system error dynamics. This in order to solve the trajectory tracking task for both the converter voltage and motor bidirectional angular velocity, without using electromechanical sensors. The successful experimental validation of the proposed control is performed in a built prototype of the system, using Matlab-Simulink and a DS1104 board.

Summary: Renewable energy sources (RESs) need power‐electronics‐based converters to deliver the acquired power to the grid. Those converters should provide voltage‐bucking/boosting capabilities to accommodate various grid modes specially for three‐phase distorted commonly unbalanced distribution utility networks. Several power electronic‐based converters have been elaborated to fulfill this high‐demand market. Single‐stage converters are the most dominant in the market where the current source inverters (CSIs), impedance source inverters (ZSIs), and boost inverters are the high‐end candidates. The aspects of cost, footprint, and minimal numbers of active switches, in addition to simplified controllability, build the main challenges that face the evolution of robust renewable energy‐associated grid‐tied converters. This paper presents a novel three‐phase differential‐mode buck‐boost inverter based on two bidirectional buck‐boost DC/DC converters and one differential power processor (DPP) unit. The proposed topology is a single‐stage DC/AC converter offering bucking/boosting capability, with reduced hardware requirements. The proposed topology features a simplified control methodology in addition to reduced size and cost of the hardware setup which makes it more suitable for grid‐tied renewable energy applications. The operation principles, small‐signal model, and control strategy of the proposed topology are also illustrated. Simulation and experimental results are presented in details to verify the topology‐enhanced performance under various operating conditions. The deducted results elucidate the viability of the proposed configuration alongside with the claimed merits. [ABSTRACT FROM AUTHOR]

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (P&O) is the most commonly used for its simplicity and ease of implementation; however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost) for a better matching between the PV and the load. This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

This work presents the modeling and validation of a bidirectional “DC/DC Buck-Boost converter‑DC motor” system. This configuration allows the change of rotation in both directions on the motor shaft. The experimental validation of the model is accomplished for time-varying duty cycles. This allows solving the trajectory tracking task in open-loop of the system. On the one hand, applying the Kirchhoff's laws along with the model of a DC motor, the mathematical model is obtained. While, the trajectory planning is carried out by expressing the system states and duty cycles in terms of the converter voltage and motor angular velocity. The validation of the deduced model is performed via Matlab‑Simulink and a DS1104 board in a laboratory prototype of the system. [ABSTRACT FROM PUBLISHER]

This paper has two aims. The first is to develop a robust hierarchical tracking controller for the DC/DC Buck-Boost–inverter–DC motor system. This controller considers a high level control for the inverter–DC motor subsystems and a low level control for the DC/DC Buck-Boost converter subsystem. Such controls solve the tracking task associated with the angular velocity of the motor shaft and the output voltage of the converter, respectively, via the differential flatness approach. The second aim is to present a comparison of the robust hierarchical controller to a passive controller. This, with the purpose of showing that performance achieved with the hierarchical controller proposed in this paper, is better than the one achieved with the passive controller. Both controllers are experimentally implemented on a prototype of the DC/DC Buck-Boost–inverter–DC motor system by using Matlab-Simulink along with the DS1104 board from dSPACE. According to experimental results, the proposal in the present paper achieves a better performance than the passive controller.

A controller to mitigate the destabilizing effect of constant power load (CPL) is proposed for a DC/DC buck-boost converter. The load profile has been considered to be predominantly of CPL type. The negative incremental resistance of the CPL tends to destabilize the feeder system, which may be an input filter or another DC/DC converter. The proposed sliding mode controller aims to ensure system stability under the dominance of CPL. The effectiveness of the controller has been validated through real-time simulation studies and experiments under various operating conditions. The controller has been demonstrated to be robust with respect to variations in supply voltage and load and capable of mitigating instabilities induced by CPL. Furthermore, the controller has been validated using all possible load profiles, which may arise in modern-day DC-distributed power systems. [ABSTRACT FROM AUTHOR]

Currently, most of the capacitive deionization (CDI) research is oriented to improving the electrode materials. However, if the CDI overall efficiency is to be improved, it is necessary to optimize the CDI cell geometry and the charge/discharge current used during the deionization process. In this paper, an experimental CDI module is electrically characterized, and its performance is derived by solving the differential equations that represent the behavior of a total system. The solving method, which provides a detailed description of energy losses, is applied to a set of theoretical CDI modules whose properties are related to those of actual modules. This allows the extrapolation of the performance obtained to that of other modules with different geometries. From this information, it is possible to derive the optimum geometry that gives rise to the highest efficiency in the whole system for each charging current. The system equations allow an optimal charging current to be derived and an optimal energy-recovery performance to be achieved for any CDI cell. This charging current is a function of parasitic resistances and voltages on the cells along the energy transfer. The theoretical results are compared to experimental measurements conducted on actual CDI cells; good agreement with theoretical predictions was observed. [ABSTRACT FROM PUBLISHER]

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper has two aims. The first is to develop a robust hierarchical tracking controller for the DC/DC Buck-Boost–inverter–DC motor system. This controller considers a high level control for the inverter–DC motor subsystems and a low level control for the DC/DC Buck-Boost converter subsystem. Such controls solve the tracking task associated with the angular velocity of the motor shaft and the output voltage of the converter, respectively, via the differential flatness approach. The second aim is to present a comparison of the robust hierarchical controller to a passive controller. This, with the purpose of showing that performance achieved with the hierarchical controller proposed in this paper, is better than the one achieved with the passive controller. Both controllers are experimentally implemented on a prototype of the DC/DC Buck-Boost–inverter–DC motor system by using Matlab-Simulink along with the DS1104 board from dSPACE. According to experimental results, the proposal in the present paper achieves a better performance than the passive controller. [ABSTRACT FROM AUTHOR]