The increasing integration of distributed generators (DGs) into AC microgrids (MGs) via power electronics reduces system inertia and causes frequency oscillations. To enhance the dynamic frequency response, this paper proposes a virtual asynchronous machine (VAM) control for the AC/DC interlinking converter (IC) that connects the MG and the heating system. Unlike conventional source-side methods (e.g., virtual synchronous generators) that provide inertia from generation units, this work addresses low inertia from the load side. The VAM emulates the slip-frequency regulation, natural damping, and self-regulating torque-speed characteristics of an asynchronous machine (AM). Furthermore, an adaptive virtual inertia and damping combination control is developed, which dynamically adjusts parameters based on frequency deviation and its rate of change. Meanwhile, the thermal inertia of buildings serves as a virtual energy buffer, allowing the VAM to temporarily reduce or increase power transmission to the heating system without compromising indoor thermal comfort. Simulation results demonstrate that the proposed VAM control reduces the maximum frequency deviation by 72% (from 0.25 Hz to 0.07 Hz) and the rate of change of frequency (RoCoF) by 64%.

Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device. The previous studies successfully perform steady-state power loss estimation for different applications, but which may be limited for the electric vehicles (EVs) with high dynamics. Based on two EV standard driving cycle profiles, this paper gives a comparative study of power loss estimation models with two different time resolutions, i.e., the output period average and the switching period average. The correspondingly estimated power losses, thermal profiles, and lifetime clearly pointed out that the widely applied power loss model with the output period average is limited for EV applications, in particular for the highly dynamic driving cycle. The difference in the predicted lifetime can be up to 300 times due to the unreasonable choice the loss model, which calls for the industry attention on the differences of the EVs and the importance of loss model selection in lifetime prediction.

This paper proposes a non-invasive fault location method for modular multilevel converters (MMC) considering light load conditions. The prior-art fault location methods of the MMC are mostly developed under full load conditions. However, it is revealed that the faulty arm current will be suppressed to be unipolar when the open-circuit fault happens on the submodule switch under a light load. This leads to the capacitor voltage of the healthy and faulty submodules rising or falling with the same variations, increasing the difficulty of fault location. The proposed approach of injecting the second-order circulating current will rebuild the bipolar arm current of the MMC and enlarge the capacitor voltage deviations between the healthy and faulty SMs. As a result, the fault location time is significantly shortened. The simulations are carried out to validate the effectiveness of the proposed approach, showing that the fault location time is reduced to 1/6 compared with the condition without second-order circulating current injection.

Modular multilevel converters (MMCs) are a well-known solution in high-voltage applications. Recently, new topologies such as MMCs with series/parallel (MMSPC) connectivity are attracting large amount focus, due to their improved efficiency as well as self-balancing capability. Although MMSPCs can operate without direct measurement of modules’ voltages in battery-integrated modules, monitoring of the voltage and resistance of each module can still provide useful information to assess the state of each battery. However, including a voltage and current sensor in each module is not a cost-effective approach. This paper proposes a method to estimate the voltage and resistance of each battery-module using only the voltage and current measurement at load terminal. The proposed technique can reduce the number of sensors from 2N + 2 to only 2. Additionally, due to the self-balancing capability, the estimation technique does not require fast convergence and can operate in the background during the idling intervals of the processor. MATLAB simulations confirm the applicability of the proposed approach. The result show a 99 % and also 96 % accuracy for balanced and imbalanced systems.

Modular multilevel converters (MMC) show great potential in high-voltage as well as emerging applications such as grid storage and electro mobility. The main advantages over the two−level converters include flexibility, scalability, and higher degrees of freedom. MMCs with series/parallel connectivity (MMSPC) are a more recent developed technology which can provide stable and efficient sensorless operation. However, although the additional parallel connection between the modules provides many advantages, it can change the equivalent resistance of the system and hence complicate the power loss analysis. This paper presents a simplified conduction loss calculation method based on equivalent resistance analysis for MMSPC. The presented method has the advantage of simplicity with relative accuracy that can speed up the process of loss analysis. Simulation results show that the accuracy of the presented method is above 95% percent for modulation indices above 0.5.

A novel switching power loss estimation method for modular multilevel converters (MMCs) with the nearest level modulation (NLM) is introduced in this paper. It focuses on the switching actions caused only by change of the inserted submodule (SM) number per arm, named as the essential switching transitions. Every essential switching action can be identified under the assumption of ideal sinusoidal arm voltage reference. The corresponding arm current value determining the switching energy can be obtained with the exclusive use of the exact time of the switching action. Effects of the modulation index and the power factor on the power loss are also taken into account. Simulations have been performed on a 30-MVA three-phase grid-connected MMC system with 20 SMs per arm. The results have confirmed the effectiveness of the proposed method, where high estimation accuracy can be achieved under a range of operating conditions.

The reliability aspect study of Modular Multilevel Converter (MMC) is of great interest in industry applications, such as offshore wind. Lifetime prediction of key components is an important tool to design MMC with fulfilled reliability specifications. While many efforts have been made to the lifetime prediction of IGBT modules in renewable energy applications by considering long-term varying operation conditions (i.e., mission profile), the justifications of using the associated mission profiles are still missed. This paper investigates the impact of mission profile data resolutions and electrical power modeling methods on the estimated lifetime of IGBT modules in an MMC for offshore wind power application. In a 30 MW MMC case study, an annual wind speed profile with a resolution of 1 s/data, 10 minute/data, and 1 hour/data are considered, respectively. A method to re-generate higher resolution wind speed data from lower resolution data is introduced as well. Based on the wind speed data, IEC 61400-12-1 power curve model and a wind speed-power stochastic model are compared as well. Five mission profile modeling scenarios are compared in terms of the predicted lifetime of the IGBT modules used in the MMC, resulting in significant differences. The study serves as a first step to quantify the impact of mission profile modeling on lifetime prediction, and to provide a guideline on mission profile collection for the presented application.