Modular Multilevel Converters (MMCs) offer significant advantages in the medium to high-voltage settings. The modular architecture of MMCs allows for redundant submodules (SMs) to improve overall reliability. These redundant SMs can be deployed using various redundancy strategies, such as Load-Sharing Active Redundancy Strategy (LS-ARS), Fixed-Level Active Redundancy Strategy (FL-ARS), and Standby Redundancy Strategy (SRS). The primary contribution of this paper is the introduction of guidelines for applying Monte Carlo Simulation (MCS) and a comprehensive methodology for its application across various redundancy strategies. This enables precise planning of preventive maintenance and estimation of the number of faulty SMs with a specific lifespan in the MMC. More importantly, MCS is applied to estimate the reliability of the MMC applying Mission Profile for SRS and LS-ARS where analytical solutions are unavailable. An analysis of uncertainty and the applicability of MCS is also presented to demonstrate the advantages of MCS over analytical methods. The computational time required for applying MCS across different redundancy strategies and arm levels is also assessed.

Targeting a climate-neutral maritime sector drives the adoption of the all-electric ship (AES). While AESs can utilize both ac and dc shipboard power systems (SPS), a dc system offers advantages in efficiency, power density, and source synchronization. However, the enhanced network complexity of dc grids combined with the high penetration of power electronic devices and harsh environmental conditions can compromise the system's reliability. Therefore, this article provides an overview of the reliability aspect of dc-SPSs, addressing the power system design, adequacy assessment, and reliability improvement. First, the performance tradeoffs associated with the SPS design are examined, revealing how changes in the power system topology and dc bus structure impact the vessel's reliability along with other performance parameters. Second, a hierarchical reliability model framework is proposed for the adequacy assessment of dc-SPSs, considering the reliability from the component level up to the system level. To determine the system-level reliability, multiple probabilistic methods, including simulation and analytical models, are compared using a propulsion subsystem example. Finally, an overview of the reliability improvement strategies is provided, addressing methods at the system, device, and component level. These three topics combined aim to provide guidance in the design of future reliable dc-SPSs.

High-power flexible dc links employ modular multilevel converters (MMC) for compact active power redirection in medium and high voltage grids. During contingencies, such converters may need to provide an enhanced active power capacity to avoid overload in vulnerable grid locations. This paper achieves this target by using the capacitor voltage ripple margin of the MMC submodules (SM) to enhance the dc voltage beyond the rated value. This voltage enhancement enables the enhanced active power capacity of the MMC while maintaining rated electro-thermal stresses on the components. Moreover, dynamically varying the dc side voltage reduces the MMC's circulating current, improving its operating efficiency. Because the average capacitor voltage is controlled to remain constant, the overall stresses and harmonic performance of the enhanced MMC remain the same as in the base case. In this paper, the analytical expressions for the voltage and power enhancement limits are derived, revealing a dependence on the grid-injected reactive power. Furthermore, a controller is designed to achieve stable operation during transient conditions when the power enhancement is carried out. Finally, the enhancement concept is validated using simulations and experiments with a down-scaled laboratory MMC prototype.

Modular multilevel converters are favorable for efficiently operating high-power usages. The required number of components significantly increases when higher modularity is introduced for the given voltage level, thus reducing the system's reliability. This article suggests a mixed redundancy strategy (MRS) that combines the operational concepts using active and spare redundant submodules. It is shown that more than 50% higher B10 lifetime (the point in time when the system has a 90% probability of survival) is achievable as compared to reliability improvement using fixed-level active redundancy strategy, load-sharing active redundancy strategy, and standby redundancy strategy with the same number of redundant submodules. The tradeoff between operational efficiency and investment cost is explored to define the boundary for selecting the MRS over other redundancy strategies with varying dc-link voltages and average converter loading, considering a ten-year payback period and equivalent B10 lifetime. The change in viability boundary for the MRS is established with increasing B10 lifetime and its sensitivity to power electronic component costs and assumed failure rate. The effect of power capacity with a higher switch current rating is evaluated. Also, the Monte Carlo simulation methodology is proposed to evaluate the practicality and effectiveness of the proposed MRS scheme. Finally, the insights of this study are applied to existing literature.

In this article, we provide a comprehensive review of defect formation at the atomic level in interfaces and gate oxides, focusing on two primary defect types: interface traps and oxide traps. We summarize the current theoretical models and experimental observations related to these intrinsic defects, as they critically impact device performance and reliability. By integrating theoretical insights with experimental data, this review provides a thorough understanding of the atomic-scale interactions that govern defect formation.

Power electronics converters are essential for power generation, transmission, and distribution. The modular multilevel converter (MMC) is highly valued for its versatility, high efficiency, and robust control capabilities. Since MMC is composed of many components, its reliability is crucial for maintaining the availability of electrical power systems. The reliability of the MMC can be evaluated using different methods, such as the military handbook (MIL) and the Mission Profile (MP) methods. By comparing the reliability estimation of the MMC using the MIL and MP methods, this study offers insights into the effectiveness of these approaches. Also, it shows the significant difference in final results between the two applied methods. These findings contribute to the understanding and improvement of the reliability assessment of power electronics converters. Also, the impact of redundancy is scrutinized to make the comparison more thorough.

The role of power electronics in advancing electrification and sustainability is pivotal. The Modular Multilevel Converter (MMC) is a leading candidate for connecting offshore wind farms to the power grid. However, one of the primary concerns with MMC is its reliability, primarily due to the high number of components, with semiconductors and capacitors being the main sources of failures. This study examines how the modularity and redundancy of the MMC affect its reliability and, consequently, its impact on power system adequacy. Our findings reveal a substantial influence of MMC's modularity and redundancy on power system adequacy. A high level of modularity with no redundancy leads to the worst-case scenario. On the other hand, lower modularity combined with higher redundancy results in the best scenario for power system adequacy. However, it's important to note that lower modularity and higher redundancy come with increased capital costs of MMC, representing a trade-off between reliability and affordability that we explore in this paper.

In the realm of electric mobility, fast chargers for electric vehicles (EVs) play a critical role in mitigating range anxiety while driving. The converter in these chargers usually has a load profile consisting of a high-current pulse to swiftly recharge the EV battery, followed by a cooling-off phase when the charging process is over. This pattern results in thermal cycles on the devices resulting in mechanical fatigue that leads to gradual deterioration of the power electronic components. Consequently, evaluating the power electronic converters reliability is critical to facilitating fast EV charging. This paper focuses on the reliability analysis of the phase-shifted full-bridge DC/DC converter within EV fast chargers, with a specific emphasis on the battery charging profile. The primary objective is to demonstrate how the charger load characteristics and number of charging sessions influence device reliability and, consequently, overall system reliability. Additionally, the investigation explores the effects of altering devices heatsinks and current ratings on system reliability. It was observed that in worst-case scenarios, increasing devices current rates extended the system lifetime from 0.7 to about 23 years, with 3p.u. ratings achieving 10.8 years, meeting industry targets, while reducing heatsink thermal resistance improves that to around 2 years.

The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.

Identifying faulty lines and their accurate location is key for rapidly restoring distribution systems. This will become a greater challenge as the penetration of power electronics increases, and contingencies are seen across larger areas. This paper proposes a single terminal methodology (i.e., no communication involved) that is robust to variations of key parameters (e.g., sampling frequency, system parameters, etc.) and performs particularly well for low resistance faults that constitute the majority of faults in low voltage DC systems. The proposed method uses local measurements to estimate the current caused by the other terminals affected by the contingency. This mimics the strategy followed by double terminal methods that require communications and decouples the accuracy of the methodology from the fault resistance. The algorithm takes consecutive voltage and current samples, including the estimated current of the other terminal, into the analysis. This mathematical methodology results in a better accuracy than other single-terminal approaches found in the literature. The robustness of the proposed strategy against different fault resistances and locations is demonstrated using MATLAB simulations.

This article discusses the various ways in which the stresses experienced by the IGBTs and diodes in a Dual Active Bridge (DAB) are asymmetric. This asymmetry can be between the two bridges or between IGBTs and diodes on both bridges. The terminal voltage, transformer ratio and the power through the DAB are varied to discuss the stresses. These asymmetrical stresses lead to devices' distinct temperatures. This unevenness of stresses can affect the lifetime of the devices employed. An analytical model of the DAB is used to analyse the currents and power losses in various devices. Some preliminary results of power losses in the devices are presented.

In DC shipboard power systems (DC-SPS), the enhanced network complexity and high penetration of power electronic devices make the system level reliability a critical design aspect. This paper proposes a stochastic framework for the reliability assessment of DC-SPSs based on a three-stage Monte Carlo (MC) simulation, including component failure sampling, active fault propagation, and reliability index calculation. The proposed MC framework is verified for a simplified meshed DC grid through comparison with an analytical method. Later, the advantages of the MC method are demonstrated for a dynamic positioning vessel equipped with a ring-type DC power system architecture. The results quantify the impact of redundancy on the reliability of a DC-SPS, show the spread in the subsystem repair times, and reveal the system's availability during both the initialization and steady-state. Combined, the simulation results reveal the strengths and weaknesses of the designed grid, guiding the focus for future reliability enhancements.

Power electronics converters are crucial for power generation, transmission, and distribution. The modular multilevel converter (MMC) is highly valued for its ability to handle high power levels, versatility in reconfiguration, high efficiency through small-capacity submodules (SMs), and robust control capabilities. A failure of a power electronics converter could result in disruptions in the flow of electrical power, which could have severe consequences for people and equipment relying on it. Thus, the reliability of power electronics converters is critical to maintaining the reliability of the electrical power system. Two well-known methodologies, the military handbook (MIL) and the more recent FIDES, can be used to evaluate the MMC's reliability. Both methods consider various factors to estimate the component's failure rate, resulting in different reliability parameters. In this paper, the reliability of the MMC is estimated using both methods, and the results are compared for standby and active redundancy strategies. Lastly, a generalized cost form that considers operational cost, capital cost, redundancy strategies, reliability methods (MIL and FIDES), and the MMC's annual average loading is presented.

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bidirectional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE; however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive.

The variability of the output power of distributed renewable energy sources (DRESs) that originate from the fast-changing climatic conditions can negatively affect the grid stability. Therefore, grid operators have incorporated ramp-rate limitations (RRLs) for the injected DRES power in the grid codes. As the DRES penetration levels increase, the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service (AS). Energy storage systems (ESSs) coordinated by RR control algorithms are often applied to mitigate these power fluctuations. However, no unified definition of active power ramps, which is essential to treat the RRL as AS, currently exists. This paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS (BESS) in distribution systems (DSs). The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system (TS). The proposed control is based on the direct control of the ΔP/Δt, which means that the control parameters are directly correlated with the RR requirements included in the grid codes. In addition, a novel method for restoring the state of charge (SoC) within a specific range following a high ramp-up/down event is proposed. Finally, a parametric method for estimating the sizing of central BESSs (BESS sizing for short) is developed. The BESS sizing is determined by considering the RR requirements, the DRES units, and the load mix of the examined DS. The BESS sizing is directly related to the constant RR achieved using the proposed control. Finally, the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.