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In this paper, a new modulation strategy based on the carrier-based switching strategy for the non-square direct matrix converters (MC) is proposed to control the active power of distributed generation (DG) units. In this strategy, the active power of DGs is controlled by the central input current control of the non-square direct MC independent from the voltage and frequency. Conventionally, each DG has a converter, and for supplying a load with N number of DGs, N number of converters are needed and each converter has its own modulation switching and control strategy to control the power output of each DG. Needless to say, in a microgrid with N number of DGs, the control strategy of each converter has more complex structure than that of a microgrid with one converter, and surely the former strategy entails more volume and price. Using the proposed converter in this paper, it is possible to supply a load with N number of DGs through one converter. Also, the power outputs of all DGs are controlled by a central control strategy. The proposed central control strategy is described and simulated for a typical microgrid. Experimental and simulation results validate the effectiveness of the proposed converter and the proposed strategy. The results demonstrate the applicability and efficiency of the system and verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids. [ABSTRACT FROM AUTHOR]

The quasi-type-l phase-locked loop (QT1-PLL) is a grid synchronization technique that has become very popular in recent years thanks to its attractive performance such as easy implementation, fast dynamic response, and good accuracy in steady-state operation. However, it is still vulnerable to operation under harmonically distorted grid voltages with frequency drift. This paper proposes a novel QT1-PLL based synchronization algorithm that makes an appropriate combination of two filters’ types: an in-loop third-order moving average filter (MAF) with a reduced window width, and a simplified second-order fast delayed signal cancellation (FDSC) based prefiltering stage. The proposed PLL is named third-order MAF based QT1-PLL (TQT1-PLL). Though both TQT1-PLL's filters do not need any adaptive algorithm, it is able to reject non-triplen odd-harmonics and the fundamental frequency negative sequence (FFNS) even under grid frequency drift. Its correct operation is confirmed through numerical simulations and real-time implementation on a digital signal processor (DSP). Moreover, the obtained results confirm its ability to reduce the ripple in the estimated frequency and phase under distorted grid voltages and off-nominal frequency operation. Authors show also through an analytical development that the topology of the proposed TQT1-PLL can be extended to enable the rejection of the DC-offset. [ABSTRACT FROM AUTHOR]

With the development of power system electronization, distributed new energy generation-based DC microgrid system has become a hot spot of the current research. Aiming to implement the collection, transformation and transmission of the new energy sources, the dual active bridge (DAB) converter has become a key energy conversion device. Aiming to promote the transmitted efficiency for such DAB converter, a deep reinforcement learning (DRL) optimization scheme with the triple-phase-shift (TPS) is proposed. Specifically, the deep deterministic policy gradient (DDPG) algorithm is used for solving optimal solutions with the minimum power losses. Then, the trained DDPG agent is used as a fast surrogate predictor, which giving the suitable control decisions under whole operation conditions. The main control objective is to minimize the power losses for getting the target maximum efficiency. Besides, the soft switching constrains are added to the DDPG algorithm. Based on this, the proposed optimized method can be used for different operation conditions with excellent performance. The operation principles, the loss analyses, and the training consideration and evaluation of the DDPG algorithm are given. Finally, simulation and experimental results using a 200 W hardware prototype are displayed and compared to prove to effectiveness of theoretical analysis. [ABSTRACT FROM AUTHOR]