Self-governing small regions of power systems, known as "microgrids", are enabling the integration of small-scale renewable energy sources (RESs) while improving the reliability and energy efficiency of the electricity network. Microgrids can be primarily classified into three types based on their voltage characteristics and system architecture; 1) AC microgrids, 2) DC microgrids, and 3) Hybrid AC/DC microgrids. This paper presents a comprehensive review of stability, control, power management and fault ride-through (FRT) strategies for the AC, DC, and hybrid AC/DC microgrids. This paper also classifies microgrids in terms of their intended application and summarises the operation requirements stipulated in standards (e.g., IEEE Std. 1547. The control strategies for each microgrid architecture are reviewed in terms of their operating principle and performance. In terms of the hybrid AC/DC microgrids, specific control aspects, such as mode transition and coordinated control between multiple interlinking converters (ILCs) and energy storage system (ESS) are analysed. A case study is also presented on the dynamic performance of a hybrid AC/DC microgrid under different control strategies and dynamic loads. Hybrid AC/DC microgrids shown to have more advantages in terms of economy and efficiency compared with the other microgrid architectures. This review shows that hierarchical control schemes, such as primary, secondary, and tertiary control are very popular among all three microgrid types. It is shown that the hybrid AC/DC microgrids require more complex control strategies for power management and control compared to AC or DC microgrids due to their dependency on the ILC controls and the operation mode of the hybrid AC/DC microgrid. Case study illustrated the significant effects of microgrid feeder characteristics on the dynamic performance of the hybrid AC/DC microgrid. It is also revealed that any transient conditions either in the AC or DC microgrids could propagate through the ILC affecting the entire microgrid dynamic performance. Additionally, the critical control issues and the future research challenges of microgrids are also discussed in this paper.INDEX TERMS Energy storage system (ESS), hybrid AC/DC microgrid, IEEE Std. 1547-2018, interlinking converter (ILC), microgrid stability, power management, renewable energy sources (RESs).

Tumor immune escape has emerged as the most significant barrier to cancer therapy. A thorough understanding of tumor immune escape therapy mechanisms is critical for further improving clinical treatment strategies. Currently, research indicates that combining several immunotherapies can boost antitumor efficacy and encourage T cells to play a more active part in the immune assault. To generate a more substantial therapeutic impact, it can establish an ideal tumor microenvironment (TME), encourage T cells to play a role, prevent T cell immune function reversal, and minimize tumor immune tolerance. In this review, we will examine the mechanisms of tumor immune escape and the limits of tumor immune escape therapy, focusing on the current development of immunotherapy based on tumor immune escape mechanisms. Individualized tumor treatment is becoming increasingly apparent as future treatment strategies. In addition, we forecast the future research direction of cancer and the clinical approach for cancer immunotherapy. It will serve as a better reference for researchers working in cancer therapy research.

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High penetration of dynamic loads, such as induction motors (IMs) could give rise to sustained voltage/frequency and power oscillations in hybrid AC/DC microgrids during disturbances. Majority of the published literature has investigated these stability issues with aggregated models of IMs in hybrid AC/DC microgrids, which do not properly reflect the actual dynamics of parallel operating IMs; hence, power oscillation damping (POD) controllers must be designed explicitly considering various oscillations induced by parallel operating IMs. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) based POD controller to damp low-frequency oscillations (LFOs) induced by IMs in hybrid AC/DC microgrids. The proposed supplementary POD controller was embedded to the energy storage system (ESS) controller, which provides additional damping power proportional to the frequency deviation. The following two features namely: 1) ability to adjust the gain based on the frequency deviation, and 2) ability to handle more non-linearity in the system dynamics, make the proposed adaptive ANFIS based POD controller more unique compared to conventional POD controllers. The effectiveness of the proposed ANFIS-POD controller is verified using non-linear dynamic simulations considering a range of disturbances in a hybrid AC/DC microgrid and different combinations of parallel operating IMs. Results indicate improved oscillatory stability performance in the hybrid AC/DC microgrid with the proposed ANFIS-POD controller. INDEX TERMS Adaptive neuro-fuzzy inference system (ANFIS), hybrid AC/DC microgrid, induction machine (IM), low frequency oscillations (LFOs), power oscillation damping (POD) controller. NOMENCLATURE

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