Fast fault detection method for modular multilevel converter semiconductor power switches

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories.

Modular multilevel converter (MMC) is a promising converter in medium and high voltage applications due to its modularity and easiness to meet any voltage level by cascading the sub-modules. However, large amount of modules not only increase the failure probability of the devices, but also increase the difficulty of the fault diagnosis, which have brought a challenging for popularization and application of the MMC. Therefore, an extended state observer based fault diagnosis strategy for MMC is proposed in this paper. The extended state observer is designed based on the circulating current equation. The output of the extended state observer is compared with the theoretical value calculated by the modified switching models to locate the fault. The simulation is modeled and carried out in Matlab/Simulink. The effectiveness of the proposed strategy is verified by the simulation results.

This paper presents a robust sensor fault reconstruction method for modular multilevel converter (MMC) based on sliding mode observer (SMO). An uncertain system with sensor failure is firstly established by resorting the MMC state space model. The main idea of sensor fault reconstruction is to introduce an appropriate filter and convert the sensor fault into a fictitious actuator fault form by constructing an augmentation system. Robust sliding mode observer is designed with convex optimization theory and linear matrix inequality (LMI) techniques. The sliding mode motion is maintained via equivalent output control method and the fault information is obtained directly to realize the fault information of the sensor. Finally, taking the parameters of the MMC system model as an example, the effectiveness and reliability of the proposed approach are verified by simulation.

The great number of power modules (PMs) imposes great computational challenge on the voltage balancing of modular multilevel converter (MMC) as the sorting of the PM capacitor voltages is usually adopted. This paper presents a distributed control method for PM capacitor voltage balancing in MMC. With this method, all PMs in one arm are grouped and the sorting algorithm of the voltage balancing in each group is implemented in decentralized control unit. To keep the voltage balancing among different groups, a close-loop control system is proposed, using the average capacitor voltage of each group as the feedback. With this method, the computational load is reduced compared to the conventional method while the voltage balancing effect and the switching frequency are almost the same. Real-time experimental results on a ±350 kV/1000 MW MMC based on RT-LAB (OP7020 and OP5607) verify the feasibility and effectiveness of the proposed method.

This chapter deals with the recent advancements in IGBT fault detection and protection methods for modular multilevel converters (MMCs). First, the IGBT short-circuit fault and open-circuit fault are briefly introduced. Then, the detection and protection methods are present for the IGBT short-circuit faults. Following the introduction of the features of the submodule's IGBT open-circuit faults, several fault detection methods are presented for the MMC, including a Kalman filter based method, an integrator-based method, a sliding-time window based method, and an isolation forest based method. Finally, a conclusion is given for the chapter.

Review of fault detection techniques in power converters: Fault analysis and diagnostic methodologies

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

SummaryThe intrinsic characteristics such as modular structure, filterless operation, and absence of DC‐link capacitor have made the modular multilevel converter (MMC) superior to the conventional multilevel converters. However, the submodule (SM) capacitor voltage balancing is crucial. This paper presents a voltage balance control method for MMC. The proposed method generates 2N + 1 levels (where N is the number of SMs per arm) which halves the step size of output voltage and improves the harmonic performance as compared to N + 1 level generation. It requires only one carrier per phase and no voltage and current measurements are needed for the implementation. Therefore, the proposed method makes the system cost‐effective and reduces the implementation issues related with carrier‐based methods substantially. It also alleviates the non‐uniform charge distribution problem with the phase disposition pulse width modulation (PDPWM) technique. Further, a detailed mathematical analysis is presented which enhances the understanding of balancing algorithm. In addition to this, the circulating current with 2N + 1 modulation is analyzed and a control technique is presented. The proposed voltage balancing method is simulated comprehensively in MATLAB/SIMULINK software and validated by the experimental results obtained from a laboratory prototype.

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.

The Modular Multilevel Converter (MMC) is one of interesting transformerless multilevel converter topology intended for medium and high-voltage power conversion. The MMC is formed by a cascade connection of several identical submodules and requires charge balancing of the multiple floating capacitors of the submodules. This paper proposes a new balancing method for the floating capacitor voltage. The proposed control system uses the common component in the arms current to achieve the total leg voltage and differential arm voltage control. The dynamic modeling of the converter is presented. Therefore, the design of a digital system control is included. Experimental results based on 2.2 kW prototype are presented to demonstrate the performance of the proposed system.

This article proposes a mission profile-based reliability prediction method for modular multilevel converters (MMCs). It includes key modeling steps, such as long-term mission profile, analytical power loss models, system-level and component-level thermal modeling, lifetime modeling, Monte Carlo analysis, and redundancy analysis. Thermal couplings and uneven thermal stresses among submodules are considered. A case study of a 15-kVA down-scale MMC has been used to demonstrate the proposed method and validate the theoretical analysis. The outcomes serve as a first step for developing realistic reliability analysis and model-based design methods for full-scale MMCs in practical applications.

To ensure the reliable operation of modular multilevel converters (MMCs) in their widespread applications, the isolation of the faulty components from the system is mandatory. Power switching devices are the components in MMCs with the highest failure probability. Among different failure types of switching devices, the diagnosis of open circuit (OC) faults is extensively instigated because it is the most common fault type of MMC. In this paper, the fault diagnostic techniques presented in the literature for MMC in case of switch OC failures are analyzed and summarized. The fault diagnostic strategies are studied from model-based perspectives, which has the advantages of easy implementation and lower cost.

Accurate evaluation of power losses in a modular multilevel converter (MMC) is very important for circuit component selection, cooling system design, and reliability analysis of power transmission systems. However, the existing converter valve loss calculation methods using the nearest level modulation (NLM) method and the traditional sorting-based capacitor voltage balancing strategy are inaccurate since the submodule (SM) switching logics in the MMC arms are uncertain. To solve this problem, the switching principle of the SMs in the sorting-based voltage balancing strategy is analyzed. An accurate MMC power loss calculation method based on the analysis of loss distribution of various SM topologies, including half-bridge submodule (HBSM), full-bridge submodule (FBSM) and clamp double submodule (CDSM), is proposed in this paper. The method can accurately calculate the losses caused by the extra switching actions during the capacitor voltage balancing process, thus greatly increasing the calculation accuracy of switching losses compared with existing methods. Simulation results based on a practical ± 350 kV/1000 MW MMC-HVDC system with variety of MMC topologies with different voltage balancing strategies demonstrate the effectiveness of the proposed method.

ABSTRACTThe modular multilevel converter (MMC) has attracted significant attention in the fields of high and medium‐voltage applications in recent years. Traditional indirect finite control set model predictive control (IFCS‐MPC) strategies for MMC require voltage measurements from each submodule (SM) for the voltage control loop. As a result, a considerable number of sensors is required to achieve energy balancing, leading to increased circuit complexity and higher hardware costs. This paper proposes an IFCS‐MPC method for MMC with a reduced number of sensors approach. In the approach, an SM capacitor voltage observer is designed based on the discrete‐time model of capacitor voltage, utilizing the measured arm current and SM switching state. Only one SM voltage sensor is needed per arm, which is used to correct the observed voltage errors caused by the discrete‐time model and the accuracy of the parameters. Subsequently, the observed values of the SM capacitor voltages are substituted for the measured values in the voltage sorting algorithm of IFCS‐MPC to reduce voltage sensors. The simulation and experiment results are carried out to demonstrate the effectiveness of the proposed control system and the feasibility of reducing the number of sensors.

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