An effective forecast method to trigger Thermal Runaway (TR) warning in an early stage is essential for monitoring battery safety. In this paper, we propose a novel data-driven approach to perform multi-step ahead forecast accurately for battery thermal runaway state at cell-level. We formulate this forecasting task as an imbalance data classification task and propose Meta Thermal Runaway Forecasting Neural Network (Meta-TRFNN) to solve it. Essentially, we exploit high-dimensional thermal images along with low-dimensional temperature and voltage data to capture a more representative thermal profile. Moreover, we adapt a meta-learning framework to handle the data deficiency problem. We evaluate Meta-TRFNN on simulated samples and real-world samples. Although this classification task is highly imbalanced, Meta-TRFNN is still proven effective with limited historical information. Our further comparison experiments not only demonstrate the forecasting ability of Meta-TRFNN, but also validate the benefit of involving high-dimensional thermal images and the efficacy of meta-learning framework.

Hybrid energy storage system (HESS) plays an important role in the operation of dc microgrids which have attracted significant research attention recently. The hierarchical control is widely adopted for the coordination of multiple energy storages in a HESS. As the hierarchical control comprises the centralized and the decentralized control levels, the time delays during signal transfer processes between two control levels may significantly affect HESS operation and may lead to instability. In this paper, considering the multiple delays in the hierarchical control processes, the maximum delayed time (MDT) is defined to assess the stability margin for a HESS. An accurate and effective method based on small signal stability model is then proposed to determine the MDT of a HESS to maintain its stability. The effectiveness and correctness of the proposed method are verified using a lab-scale dc microgrid.

AC mains frequency and dc-link voltage are two of the most important parameters that determine the stability of hybrid ac/dc microgrids. Frequency and voltage deviations beyond the acceptable threshold can lead to unscheduled load curtailments, cascading failures, or even power system blackouts. To enhance the stability of hybrid ac/dc microgrids, this article proposes a hybrid microgrid with bidirectional virtual inertia support, where virtual inertia is introduced to slow down dc voltage and ac frequency changes, thus enhancing the system stability. The proposed approach allows significant reductions in voltage and frequency fluctuations. With a standard hybrid microgrid configuration, inertia is delivered to both ac and dc subgrids via the change of bidirectional interlinking converter control. Specifically, the bidirectional interlinking converter uses the dc voltage of the dc grid and ac frequency of the ac grid as inputs through a proportional-integral (PI) controller and regulates the active power between two subgrids so that inertia and other supportive functions are implemented. To validate the merits of the proposed approach, experimental results are provided. According to experimental results, the frequency nadir, rate-of-change-of-frequency, and dc bus voltage deviation can be improved by the proposed strategy of bidirectional virtual inertia support in the hybrid ac/dc microgrid.

The digital control system of Modular Multilevel Converters normally synchronizes with the switching actions. The switching frequency of an MMC is generally selected as low as possible from an efficiency point of view, which leads to an unavoidable and relatively long time delay in the control system. Such time delay might deteriorate the MMC performance and even reduce the stability margin by introducing infinite uncertain eigenvalues into the system state functions. Without a proper time-delay model and quantitative system stability analyses, MMCs might suffer from uncertainties and unstable risks during their operations, especially when the switching frequency is low. This paper derives an MMC model considering the time delay in the digital control system. A Padé-based stability analysis method is then proposed with three steps. By the proposed model and method, the critical eigenvalue can be effectively extracted. With the Hopf bifurcation, the time delay in the digital control system induces a pair critical conjugate eigenvalues into MMC systems, which is detected as an instability cause. The critical angle (CA) and critical impact (CI) are defined to quantitatively assess the stability while selecting the controller parameters and minimum switching frequency. The proposed method for MMC stability assessment is experimentally verified.

The battery (ESb)-supercapacitor (ESsc) hybrid energy storage system (HESS) is the most promising solution for DC microgrids (MGs) to realize the power balance, where system instability caused by the high penetration of constant power loads (CPLs) is also a critical concern. To achieve the decentralized automatic power sharing and DC bus voltage regulation of the HESS in DC MGs towards large-signal stability, a novel autonomous finite-time backstepping control (FTBC) algorithm is proposed in this article. First, taking the characteristics of the DC bus configuration caused by CPLs into consideration, the $v_{0}^{2}-P_{0}$ droop algorithm has been proposed for the HESS to realize the dynamic power sharing among energy storages (ESs) and to maintain the DC bus voltage. With the help of the proposed scheme, the ESb supplies the low-frequency part of the load fluctuations while the ESsc compensates for the high-frequency ones. Second, with the help of extended finite-time observers (EFTOs), various disturbances, such as power coupling between ES-interfaced converters and parameter uncertainties are automatically eliminated by the EFTOs in a finite time, so that the proposed controller can achieve autonomous and decentralized control with no output current sensors and communication links. Then, the FTBC can precisely offset the estimations and stabilize the DC bus voltage in the large-signal sense. Simulation and experimental results verify the effectiveness and superiority of the proposed controller.