Model based Switch Open Circuit Fault Diagnosis Techniques for Modular Multilevel Converters: A Survey

Submodule (SM) fault diagnosis is necessary for the reliability of modular multilevel converters (MMC) and the complex operating environment of MMC may cause missing data and noise in signal detections, where some of the existing open circuit fault diagnosis methods can be disabled. An artificial intelligence (AI) diagnosis method for open circuit faults in MMC based on extreme gradient boosting system (XG-Boost) is proposed in this paper which can diagnose the open circuit fault quickly and accurately in the mentioned conditions. Compared with neural network diagnosis method, the proposed method has higher accuracy with the same non-ideal data. Faster speed and better processing ability of the proposed method are also validated by simulation of a three-phase MMC in MATLAB/Simulink.

An open-set fault diagnosis framework for MMCs based on optimized temporal convolutional network

Detection and location of open-circuit fault for modular multilevel converter

When the five-phase permanent magnet fault-tolerant servo motor (PMFSM) for aerospace is in the typical condition, rotating over a small position angle range, the motor phase current fluctuates up and down in a small range rather than presenting a sinusoidal waveform. Therefore, the current-based open-circuit (OC) fault diagnosis strategy would be ineffective. The OC fault diagnosis strategy based on the lower power switch voltage and line voltage can only diagnose the power switch OC fault, but not the winding OC fault. Therefore, we propose an OC fault diagnosis strategy based on phase voltage to solve the problem that the present OC fault diagnosis method cannot effectively diagnose the OC faults of power switches and windings under the typical condition of servo motors. The proposed strategy can diagnose not only the single power switch OC fault but also the single winding OC fault under the typical condition. In addition, the proposed strategy can also diagnose OC faults in multiple power switches, OC faults in multiple windings, as well as mixed OC faults in windings and power switches. The simulation results verify that the proposed strategy can quickly and accurately diagnose the OC faults of power switches and windings under the typical condition. The phase voltage diagnosis method used in this paper is the first to be used in the fault diagnosis of five-phase motors. Two thresholds are set by combining the phase voltage to diagnose the winding fault and the power switch fault respectively.

Modular multilevel converter (MMC) is an important part of high voltage direct current (HVDC) transmission system. With the increase of the number of submodules in series, the open circuit failure of IGBT is the most common fault type of MMC. Therefore, it is necessary to study IGBT open circuit fault diagnosis of MMC. The diagnosis method of IGBT open circuit faults for MMC is proposed based on probabilistic neural network (PNN) and Markovian distance (MD) algorithm to further improve the reliability of MMC. The fault diagnosis model based on PNN-MD theory is established by using Matlab/Simulink software. Then the MMC circuit model is established by using PSIM circuit model. The voltage and current characteristic values of each sub module circuit in normal working state and IGBT open circuit state are collected. Meanwhile, these values are used as training samples and input into pnn-md for training. Through simulation, compared with the application of BP neural network in fault diagnosis, the PNN-MD algorithm can diagnose IGBT open circuit fault of MMC faster and more accurately. The innovation of this paper is to combine machine learning with MMC fault diagnosis, and has a broad application prospect.

This study proposes a new fault detection method for modular multilevel converter (MMC) semiconductor power switches. While in common MMCs, the cells capacitor voltages are measured directly for control purposes, in this study voltage measurement point changes to the cell output terminal improving fault diagnosis ability. Based on this measurement reconfiguration, a novel fault detection algorithm is designed for MMCs semiconductor power switches. The open circuit and short circuit faults are detected based on unconformity between modules output voltage and switching signals. Simulation and experimental results confirm accurate and fast operation of the proposed method in faulty cell diagnosis. Implementation simplicity, no need to extra voltage measurements and multi faults detection ability are some other advantages of the proposed method.

Electrical power systems are currently facing a steady increase of converter-based generation and a loss of conventional power plants. In order to compensate the decrease of inertia and to retain grid restoration capability, the converters have to operate in grid-forming mode and act as a voltage source. While the grid-forming concept itself is applicable to many converter types, the challenge for Modular Multilevel Converters (MMC), in which power input and power output are decoupled, is the voltage control of the submodule capacitors.This paper deals with the laboratory implementation of a grid-forming MMC with submodule-integrated as well as DC-bus-connected batteries. The objective was to enhance the established grid-feeding control, which decouples power input and power output of the converter, towards grid-forming control and operation. Therefore, the inner current controllers and outer energy balancing controllers were extended by voltage and PQ droop controllers and virtual impedances. The two main features shown in the test cases were instantaneous active power support and parallel operation of the grid-forming MMC with other voltage sources, i.e. with the grid and with another grid-forming converter. Load switching in islanded mode proved the ability to provide active power instantaneously while maintaining the voltage balance of the submodule capacitors.From these results, important functions of a grid-forming MMC can be derived for future power systems, e.g. instantaneous frequency support and inertia emulation or grid restoration capability.

Modular multi-level converters (MMCs) are one of the promising topologies in recent years for medium or high voltage applications. They are considered as the next generation DC/AC converters for medium/high voltage (MV/HV) motor drive applications due to their transform-less structures, high efficiency and modularity. Reliability is one of the most important challenges in MMCs, since a large number of power switching devices are used and each of these devices can be considered as a potential failure point. It is significant to detect and locate the fault accurately and apply appropriate protections in a timely manner. This paper investigates the behavior of the failure submodules and healthy submodules and use artificial neural network (ANN) classification algorithms for single-submodule open-circuit fault diagnosis. The ANN algorithm is implemented in field programmable gate array (FPGA) and the ANN parameters training is completed offline in Matlab. Experimental results of the proposed fault diagnosis for a single-module open-circuit fault are presented in this paper.

Modular multilevel converter with integrated battery energy storage system (MMC‐BESS) has been proposed for energy storage requirements in high‐voltage applications with large‐scale renewable energy resources. The MMC‐BESS is essentially a three‐port converter which can transfer energy between any two of the ac port, dc port, and BESS, constituting multimode operation of the system. Different from conventional MMCs, the state‐of‐charge (SOC) inconsistency among batteries would magnify the submodule capacitor voltage unbalance issue in the MMC‐BESS. In this study, an improved capacitor voltage balancing method applicable for multimode operation of MMC‐BESS is proposed by adjusting ac and dc modulation indexes simultaneously. After that, the ratio of ac and dc modulation indexes is optimised to enhance the tolerance of unbalanced power among SMs within phase arm, greatly improving the SOC equalisation rate. Based on the proposed capacitor voltage balancing method, the control structure is given and the dynamic model is conducted for the analytic design of the closed‐loop controller. Finally, the simulations and experimental results validate the effectiveness and feasibility of the proposed control strategy.

Quasi-Two-Level PWM Operation of modular multilevel converters enables a significantly smaller amount of installed module capacitance, even with low output frequencies required for machine drives. This article presents an implementation of this operation mode with an improved current control that leads to fast and precise current transitions. The proposed current control combines a deadbeat controller with a simple hysteresis controller that uses a predictive estimation of branch currents, which are both implemented using an FPGA. The enhanced performance of the proposed control is demonstrated by a comparison to the original one. Experimental results on a laboratory prototype prove the feasibility of Quasi-Two-Level PWM Operation as a machine drive and validate the advantage of reduced overvoltages with long machine cables in comparison to those of conventional two-level inverters.

The modular multilevel converter (MMC) in high voltage direct current (HVDC) is equipped with hundreds of sub-modules (SMs), so the detailed model consist of hundreds of switching devices and consequently large computational effort but mathematical converter model with Arm Level Averaged (ALA) allows to know the dynamic and converter control decreasing simulation time. This paper presents a control strategy decou- pling the modulation index implemented in MMC-ALA model, it allows MMC operation with variable DC bus voltage but keeping constant the capacitor voltage in upper and lower arms, the evaluations consider the next operations: boost mode reducing DC bus voltage to 0.5.p.u., DC fault and SM voltage increased. The upper and lower capacitor voltages in all cases follow the reference independently to DC bus voltage value, the strategy is simulated in 150 MW converter using Matlab/Simulink.

Submodule (SM) fault detection and location (FDL) is essential for the reliable operation of modular multilevel converters (MMCs). Once SM failures occur, the capacitor voltages of faulty SMs will be out of control and deviate from the rated value. As analyzed in this paper, in normal state, the charging abilities of driving signals are constant in one fundamental period. Thus, the voltage variation of SMs in one fundamental period should coincide with the charging ability of driving signals applied to them. So this paper proposes an FDL method based on the capacitor voltage variation observations, which monitors the health state of SMs by comparing the expected voltage variations with the measured ones. The proposed method can detect and locate the faulty SM simultaneously in no more than two fundamental periods and requires no arm current measurement. Effectiveness of the proposed method and influence of capacitance inconsistency are validated by simulation results of a three-phase 101-level MMC in MATLAB/Simulink.

Submodule (SM) open-circuit failures severely affect the reliable operation of modular multilevel converter (MMCs). A comprehensive SM open-circuit failure diagnosis process includes three steps with failure detection, localization, and classification. However, the existing diagnosis methods perform these steps separately, which makes the diagnosis process complicated. Furthermore, the efficiency is relatively low with separate diagnosis steps. To address this issue, a diagnosis method with variance measurement is proposed in this paper to combine failure detection, localization, and classification. The proposed method makes use of two fault characteristics of SM open-circuit failures: 1) SM capacitor voltages have distinct distributions upon different faulty IGBTs; 2) Numerous SMs in the MMC make the failure diagnosis equivalent to statistical outlier analysis. On account of the fault characteristics, a variance-based index called VAR is proposed to evaluate the distributions of SM capacitor voltages. The VAR values are then analyzed with quartile analysis, which is a statistical outlier analysis method, to complete the diagnosis process. The proposed failure diagnosis method features high reliability, high compatibility, and high cost-effectiveness. Finally, the feasibility of the proposed method is verified through both simulations and experiments under various failure scenarios.

Reducing the power loss and handling short‐circuit faults in HVDC systems are crucial for economical and reliable operation of modular multilevel converters (MMC). This paper proposes a loss reduction method for AC voltage‐boosted FBSM‐MMC by controlling the second harmonic component in the circulating current. The relationship between converter loss and second harmonic circulating current (SHCC) is first derived for AC voltage‐boosted FBSM‐MMC. From the analysis, it can be seen that the converter loss can be reduced by injecting the optimal SHCC into the arm current. The topology comparison between traditional FBSM‐MMC and AC voltage‐boosted FBSM‐MMC is carried out to show the benefit of this method. Simulation results using PSCAD/EMTDC confirm the feasibility and validity of the proposed loss reduction method for AC voltage‐boosted FBSM‐MMC.

The electric power grid is undergoing significant changes and updates nowadays, especially on a production and transmission level. Initially, the move towards a distributed generation in contrast to the existing centralized one implies a significant integration of renewable energy sources and electricity storage systems. In addition, environmental awareness and related concerns regarding pollutant emissions have given rise to a high interest in electrical mobility. Advanced power electronics interfacing systems are expected to play a key role in the development of such modern controllable and efficient large-scale grids and associated infrastructures. During the last decade, a global research and development interest has been stimulated in the field of modular multilevel conversion, due to the well-known offered advantages over conventional solutions in the medium- and high-voltage and power range. In the context of battery energy storage systems, the Modular Multilevel Converter (MMC) family exhibits an additional attractive feature, i.e., the capability of embedding such storage elements in a split manner, given the existence of several submodules operating at significantly lower voltages. This thesis deals with several technical challenges associated with Modular Multilevel Converters as well as their enhancement with battery energy storage elements. Initially, the accurate submodule capacitor voltage ripple estimation for a DC/AC MMC is derived analytically, avoiding any strong assumptions. This is beneficial for converter dimensioning purposes as well as for the implementation improvement of several control schemes, which have been proposed in the literature. The impact of unbalanced grid conditions on the operation and design of an MMC is then investigated, drawing important conclusions regarding the choice of line current control and required capacitive storage energy during grid faults. [...]