DC microgrids encounter the challenges of constant power loads (CPLs) and pulsed power loads (PPLs), which impose the requirements of fast dynamics, large stability margin, high robustness that cannot be easily addressed by conventional linear control methods. This necessitates the implementation of advanced control technologies in order to significantly improve the robustness, dynamic performance, stability and flexibility of the system. This paper presents an overview of advanced control technologies for bidirectional dc/dc converters in DC microgrids. First, the stability issue caused by CPLs and the power balance issue caused by PPLs are discussed, which motivate the utilization of advanced control technologies for addressing these issues. Next, typical advanced control technologies including model predictive control, backstepping control, sliding mode control, passivity-based control, disturbance estimation techniques, intelligent control and nonlinear modeling approaches are reviewed. Then the applications of advanced control technologies in bidirectional DC/DC converters are presented for the stabilization of CPLs and accommodation of PPLs. Finally, advanced control techniques are explored in other high gain non-isolated (e.g., interleaved, multilevel, cascaded) and isolated converters (e.g., dual active bridge) for high power applications.

Power allocation is a major concern in hybrid energy storage system. This paper proposes an extended droop control (EDC) strategy to achieve dynamic current sharing autonomously during sudden load change and resource variations. The proposed method consists of a virtual resistance droop controller and a virtual capacitance droop controller for energy storages with complementary characteristics, such as battery and supercapacitor (SC). By using this method, battery provides consistent power and SC only compensates high-frequency fluctuations without the involvement of conventionally used centralized controllers. To implement the proposed EDC method, a detailed design procedure is proposed to achieve the control objectives of stable operation, voltage regulation, and dynamic current sharing. System dynamic model and relevant impedances are derived and detailed frequency domain analysis is performed. Moreover, the system level stability analysis is investigated and system expansion with the proposed method is illustrated. Both simulations and experiments are conducted to validate the effectiveness of the proposed control strategy and analytical results.

Transportation electrification involves the wide utilization of power electronics based DC distribution networks and the integration of a large amount of power electronic loads. These power electronic loads, when tightly controlled, behave as constant power loads (CPLs) and may cause system instability when interacting with their source converters. In this paper, a composite nonlinear controller is proposed for stabilizing DC/DC boost converter feeding CPLs by integrating a nonlinear disturbance observer (NDO) based feedforward compensation with backstepping design algorithm. First, the model is transformed into the Brunovsky’s canonical form using the exact feedback linearization technique, to handle the nonlinearity introduced by the CPL. Second, the NDO technique is adopted to estimate the load power variation within a fast dynamic response, serving as a feedforward compensation to increase the accuracy of output voltage regulation. Then a nonlinear controller is developed by following the step-by-step backstepping algorithm with strictly guaranteed large signal stability. The proposed controller not only ensures global stability under large variation of the CPL, but also features fast dynamic response with accurate tracking over wide operating range. Both simulations and experiments are conducted to verify the proposed strategy.

This paper presents a distributed secondary control scheme for accurate power allocation and voltage restoration in islanded dc microgrids. Conventionally the droop control function is employed in the primary control layer to realize power sharing among the distributed generators (DGs) in a decentralized fashion. However, the dc bus voltage may deviate from its nominal value due to different load profiles. In order to restore the dc bus voltage to its nominal value while maintaining the power sharing accuracy, a distributed control scheme is proposed in the secondary control layer. By using limited information of dc bus voltage and the secondary control inputs of their neighboring controllers, a distributed secondary control input can be designed and then sent to its corresponding primary controller. Besides, compared to most existing methods, no global information is required and only dc bus voltage feedback is needed. In addition, by employing the idea of pinning control, our proposed secondary control can be further simplified by only sending the dc bus voltage to one DG. An islanded dc microgrid test system is built in the MATLAB Simulink environment to validate our proposed method. An experimental prototype consisting of two DGs is designed to demonstrate the newly proposed approach and verify the obtained theoretical results.

WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device free, cost effective and privacy preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this article, we first analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely, EfficientFi. The EfficientFi works with edge computing at WiFi access points and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi channel state information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized autoencoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first Internet of Things-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition (HAR) and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368 Mb/s to 0.768 kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for HAR.

For hybrid energy storage system in dc microgrid, effective power split, bus voltage deviation, and state-of-charge (SoC) violation are significant issues. Conventionally, they are achieved by centralized control or hierarchical control methods with communications. This paper proposes a simple and effective strategy to solve the problem in a decentralized manner. A high-pass filter-based droop (HPFD) controller is proposed to regulate the battery converter, and a virtual capacitance droop (VCD) controller is implemented for a supercapacitor (SC) converter. The cooperation of HPFD and VCD first achieves autonomous power split that high-frequency fluctuation is buffered by SC and low-frequency power is supplied by battery. Meanwhile, the bus voltage deviation induced by the droop-based power sharing is eliminated automatically at steady state. The resulted bus voltage restoration simultaneously enforces SC SoC back to its nominal value, and, thus, ensures continuous operation of SC as a power buffer without the violation of its SoC boundary. A design guideline is developed to ensure expected system dynamics. The effectiveness of the proposed method and analytical results are validated by simulations and experiments.