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In this paper, a family of non-isolated interleaved high-voltage-gain DC-DC converters is presented. This family can be used in a wide variety of applications, such as in a photovoltaic systems interface to a high voltage DC distribution bus in a microgrid and an X-ray system power supply. The general structure of this family is illustrated and consists of two stages: an interleaved boost stage and a voltage multiplier stage. The interleaved boost stage is a two-phase boost converter, and it converts the input DC voltage to an AC square waveform. Moreover, using the interleaved boost stage increases the frequency of the AC components so that it can be easily filtered with smaller capacitors and, therefore, makes the input current smoother than the one from the conventional boost converter. The voltage multiplier cell (VMC) can be a Dickson cell, Cockcroft-Walton (CW), or a combination of the two. The VMC stage rectifies the squareshaped voltage waveform coming from the interleaved boost stage and converts it to a high DC voltage. Several combinations of VMCs and how they can be extended are illustrated, and the difference between them is summarized so that designers can be able to select the appropriate topology for their applications. An example of this converter family is illustrated with detailed modes of operation, a steady-state analysis, and an efficiency analysis. The example converter was simulated to convert 20 V DC to 400 V DC , and a 200 W hardware prototype was implemented to verify the analysis and simulation. The results show that the example has a peak efficiency of 97% of this family of converters and can be very suitable for interfacing renewable energy sources to a 400 V DC DC distribution system.

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This paper introduces a distributed economic dispatch strategy for microgrids with multiple energy storage systems. This strategy overcomes the challenges of dynamic couplings among all decision variables and stochastic variables in a centralized dispatching formulation. The proposed strategy can be implemented in the microgrid central controller as multiple problems with simplified and decomposed formulations. It can also be implemented in the local controllers and managed by the central controller. Numerical studies and experimental results illustrate the effectiveness of the proposed dispatching systems.

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In this paper, an inductance surface estimation and learning for utilization with a stochastic Model Predictive Control (MPC) scheme for the current control of Switched Reluctance Motors (SRM) is introduced. This MPC is equipped with state estimators and is implemented as a recursive linear quadratic regulator for practical deployments in hybrid vehicle applications. Additionally, a learning mechanism is developed to dynamically adapt to the inductance profile of the machine and update the MPC and Kalman filter parameters. The introduced control scheme can cope with noise as well as uncertainties within the machine nonlinear inductance surface. The introduced system will benefit from a fixed switching frequency and will offer low current ripples by calculating the optimal duty cycles using the SRM model. Lastly simulations and experimental results are provided to evaluate the proposed method.