Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU.

As we all know that most of the power systems are interconnected through long distance tie lines, in order to transfer a large amount of electric power. There are some constraints which affect to build new transmission lines, which are: limited availability of resources, strict environment and shortage of funds. Because of these constraints it necessitates the operation of transmission lines near or for short term on their thermal loading limits. Day by day the demand in electric power rapidly increases and is expected to continue growing; while growth of generation is restricted too, due to the same constraints mentioned above, this necessitates the generators to operate near their maximum stability limits. As a result of all these factors, power systems oscillations occurs and if not damped out these oscillations, the magnitude of these oscillations will go on increasing until loss of the system synchronism. So, for the improvement of power transfer capability within the safe stability limit it is necessary to enhance the transient stability of the power system. The objective of this paper is to investigate the enhancement of transient stability on power system by different types of power system stabilizers, which are: (I) Speed Input (II) Power Input (III) Frequency Input (IV) Del-P-Omega (V) Combination of frequency-electrical power input PSS and (VI) Speed-electrical power input PSS. The implementation and simulation carried out on the Single Machine Infinite Bus system. Index Terms-Transient Stability, Power System Stabilizer(PSS), Single Machine Infinite Bus (SMIB).

Solid-state transformers (SSTs) are pivotal for next-generation power systems, yet their widespread adoption is hindered by reliability uncertainties under diverse operational conditions. This paper introduces a mission-profile-aware, physics-of-failure (PoF) reliability assessment framework that explicitly links operating conditions, degradation mechanisms, and statistical uncertainty. The approach combines PoF models for stress-sensitive components with handbook-based reliability models for passive elements, employing Joint Electron Device Engineering Council (JEDEC)-aligned statistical methods for failure rate extraction. Quantitative failure-in-time (FIT) estimates are integrated into a physics-informed Failure mode and effects analysis (FMEA), replacing subjective occurrence rankings with FIT-derived quantitative occurrence ratings while retaining explicit severity and detection criteria. The framework is demonstrated on a 10 kW Type-IV SST across multiple mission profiles and ambient temperatures. The results reveal that the system-level mean time between failures (MTBF) varies from 4.6 to over 8 years, with gate-driver electronics and DC-link capacitors dominating FIT contributions. Mission-dependent shifts in failure mechanisms highlight the inadequacy of single MTBF metrics. This scalable framework provides a foundation for reliability-oriented SST design, qualification, and deployment.