IGBT Open-circuit Fault Research Articles

A Simple Online Fault Location and Tolerant Control Strategy for Power Electronic Transformers With a Single IGBT Open-Circuit Fault

A Simple Online Fault Location and Tolerant Control Strategy for Power Electronic Transformers With a Single IGBT Open-Circuit Fault

Multiple IGBT Open-Circuit Fault Diagnosis Strategy for MMC Using Feature Reconstruction Based Recurrent Neural Network

Multiple IGBT Open-Circuit Fault Diagnosis Strategy for MMC Using Feature Reconstruction Based Recurrent Neural Network

An Improved Switching Method-Based Diagnostic Strategy for IGBT Open-Circuit Faults in Hybrid Modular Multilevel Converters

An Improved Switching Method-Based Diagnostic Strategy for IGBT Open-Circuit Faults in Hybrid Modular Multilevel Converters

A High-Accuracy-Light-AI Data-Driven Diagnosis Method for Open-Circuit Faults in Single-Phase PWM Rectifiers

Data-driven solutions like artificial intelligence (AI) are more upright and effectual for fault diagnosis in the power converter system. To decrease computing load and improve the diagnosis speed of the intelligent algorithm, a high-accuracy-light-AI data-driven open-circuit diagnosis method is proposed to diagnose single or multiple IGBT open-circuit faults in the single-phase pulse width modulation (PWM) rectifiers. Firstly, the fault characteristics of three fault scenarios are analyzed to seek the most critical features and provide prior knowledge for developing the intelligent diagnostic algorithm. Then, a fast-learning algorithm named random vector function link network (RVFL) is utilized to extract the mapping knowledge between the well-selected features and fault modes. To balance the testing accuracy and calculation time of the diagnostic algorithm, the RVFL parameters are tuned to decrease the computing burden. After that, a decision-making framework is designed for identifying the faults in real-time. Finally, the effectiveness of the proposed fault diagnosis method is thoroughly verified by extensive experimental tests. A substantial comparison to state-of-the-art techniques shows the proposed scheme’s superiority in diagnostic accuracy, computing time, and diagnosis time.

Fault Detection and Ride Through of CHB Converter-Based Star-Connected STATCOM Through Exploring the Inherent Information of Multiloop Controllers

A simple detection and ride-through operation approach is proposed for star-connected cascaded H-bridge (CHB) converter-based STATCOMs with a single IGBT open circuit fault. It adopts a modified current regulator with high gains at both dc and fundamental frequencies. Then, the output signals of the current regulator branches and the dc voltage balancing regulator are synthesized as an inherent indicator to quickly determine the position of the open-circuit fault IGBT. With this approach, the controller can also be considered as an inherent system state monitor, but without any additional sensors or computing burdens. Afterwards, in the ride-through operation stage, the faulty H-bridge module can switch to half-bridge operation and the corresponding dc offset voltage is compensated by the remaining healthy modules. It has been validated that the proposed approach is accurate in various conditions, including grid voltage and load disturbances.

Disturbance auto-encoder generation model: Few-shot learning method for IGBT open-circuit fault diagnosis in three-phase converters

With the rapid development of converters in a variety of industrial fields, the fault diagnosis of power switching devices has become an important factor in ensuring the safe and reliable operation of related systems. In recent years, machine learning has performed well in many fault diagnosis tasks. The success of these advanced methods depends on sufficient marked samples for each fault type. However, in most industrial applications, it is expensive and difficult to collect fault samples, and the fault diagnosis model trained under the limited samples cannot meet the requirements of fault diagnosis accuracy. In order to solve this problem, this study proposes a few-shot learning method based on fault sample generation to realize the open-circuit fault diagnosis of IGBT in a three-phase PWM converter. This method is the deformation of the auto-encoder called the disturbance auto-encoder generation model. By designing the model structure and training algorithm constraints, the encoder learns the nonlinear transferable disturbance from the normal operating sample pairs. Then, the disturbance is applied to the decoder to synthesize new fault samples to realize the training of the fault diagnosis model with limited samples. The biggest advantage of this method is that it can achieve 95.90% fault diagnosis accuracy by only collecting the samples in the normal operating conditions of the target system. Finally, the feasibility and advantages of the proposed method are verified by comparative experiments.

A Novel Adaptive SMO-Based Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Incipient Faults of Inverters in PMSM Drives for Electric Vehicles

A Novel Adaptive SMO-Based Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Incipient Faults of Inverters in PMSM Drives for Electric Vehicles

A data-driven method for IGBT open-circuit fault diagnosis for the modular multilevel converter based on a modified Elman neural network

Modular multilevel converter is widely used in electrical energy with many advantages, its safety and reliability has become a research hotspot. Since the structure of MMC is composed of multiple cascaded sub-modules, including a large number of IGBTs and capacitors. Therefore, fault diagnosis measures must be taken to quickly eliminate the faults. In order to solve this problem, a data-driven method is proposed based on a modified Elman neural network. By comparing the distance Ek between the predicted and true value of bridge arm current, this method can quickly realize fault detection. The original contribution of this paper is using the modified cuckoo search (MCS) to optimize the parameters of Elman model, so as to achieve the optimal balance between fault diagnosis accuracy and diagnosis speed. The simulation results proved that it can quickly detect the open-circuit fault of IGBT by data-driven, and the detection time is about 20 ms.

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives.

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

Voltage reconfiguration fault-tolerant technique for two-level three-phase inverter integrated in a wind energy system

The present paper focuses on the study of a system composed of a doubly-fed induction generator (DFIG) linked to the network by the back-to-back converter and related to a wind turbine constituting the wind energy conversion system (DFIG-WECS). In order to reach a maximum power point tracking (MPPT), a control strategy has been used. The IGBT open circuit fault presence can lead to disturbance of the continuity of service. To overcome such a problem a new voltage reconfiguration fault tolerant technique for the two-level three-phase inverter is integrated to our DFIG-WECS system in the aim to preserve its performance and stability under faulty operation. The paper also introduces a performance study of the resulted reconfiguration voltage under MATLAB-Simulink environment and illustrates well the merits and efficiency of the proposed fault tolerant method through the faulty condition.

A Transferrable Data-Driven Method for IGBT Open-Circuit Fault Diagnosis in Three-Phase Inverters

Machine learning (ML) based data-driven methods have shown promising performance in power converter fault diagnosis. However, the existing ML model trained by one fault database can only work for the corresponding converter system, but cannot work accurately for a different system with the same topology but different parameters. In this article, a novel transferrable data-driven fault diagnosis is proposed for insulated gate bipolar transistor open-circuit fault diagnosis in three-phase inverters. First, three-phase current signals are sampled as original inputs and fault features are generated by manifold feature learning. Then, an extreme learning machine model is trained by the data from the source system to form an initial diagnostic model. After that, a model adaption process is designed to adjust the model's parameter by minimizing the distribution divergence between the data from the source and target systems. As the biggest advantage, one trained diagnostic model can be applied for different systems without the need for multiple training processes. Finally, offline tests and real-time experiments are conducted to verify the performance of the proposed transferrable method.

IGBT Open-Circuit Fault Diagnosis for MMC Submodules Based on Weighted-Amplitude Permutation Entropy and DS Evidence Fusion Theory

Modular multilevel converters (MMCs) have a complex structure and a large number of submodules (SMs). If there is a fault in one of the SMs, it will affect the reliable operation of the system. Therefore, rapid fault diagnosis and accurate fault positioning are crucial to ensuring the continuous operation of the system. However, the IGBT open-circuit faults in different submodules of MMCs have similar fault features, and the traditional fault feature extraction method cannot effectively extract the key features of the fault so as to accurately locate the faulty submodules. In response to this problem, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy (WAPE) and DS evidence fusion theory. The simulation results show that WAPE has better feature extraction ability than basic permutation entropy, and the fused multiscale feature decision output has better diagnostic accuracy than the single-scale feature. Compared with traditional fault diagnosis methods, this method achieves the diagnosis of multiple fault types by collecting a single signal, which greatly reduces the number of samples and leads to higher diagnostic accuracy and faster diagnostic speed.

Fault diagnosis of VSG inverter under grid connection

Under different working conditions, the output variable waveform corresponding to the same fault will be different. This article introduces the VSG inverter model under grid connection and verifies the correctness of the model. The three-phase current of the inverter bridge arm is selected as the research object, and the time-domain two-dimensional graph is formed as the And classify single IGBT and 2 IGBT open circuit faults, and encode various faults. eature vector; the deep convolutional neural network is established and the fault diagnosis is performed, and the feature vector is used as the input and the corresponding fault code is the output. The training simulation realizes and verifies the feasibility and accuracy of the fault diagnosis of the VSG inverter using the deep convolutional neural network.

IGBT open-circuit fault detection for voltage source inverters using DC bus magnetic field signal

This paper proposes a detection method of open-circuit fault of insulated-gate bipolar transistor in the voltage source inverters. The magnetic field signal at the DC bus of the inverter is collected and analysed to detect the fault. First, the simulation is developed for a voltage source inverter-fed induction machine drive which operates in healthy state and single switch open-circuit conditions. The faulty characteristic frequencies in the spectrum of DC-link current signal are derived on the basis of the power balance analysis through the inverter. Second, laboratory tests are implemented with a test bed of voltage source inverter-based induction machine drive in which a Hall sensor suited for the geometry complexity of the DC bus structure is utilized for measuring the magnetic field signal, which can be considered as an image of the simulated DC-link current. The fast Fourier transform of the magnetic field signal shows sharp increases in spectrum amplitude at the fundamental, second-order and third-order harmonics that they can be used as the indication to detect the fault. The approach using proposed signal offers several advantages such as single-sensor requirement, non-invasive and less costly device.

Wind energy conversion system control robustness based on current analysis of IGBT open-circuit fault

Wind energy conversion system control robustness based on current analysis of IGBT open-circuit fault

An Automatic Diagnosis of an Inverter IGBT Open-Circuit Fault Based on HHT-ANN

The main objective of this paper is to propose a method that contributes to the automatic diagnosis of the IGBT open-circuit fault of an inverter for detecting and localizing the fault using the stator current spectral analysis technique. The proposal focusses on the use of the combination of signal processing and artificial intelligence techniques for the detection and localization of the fault. The proposed diagnosis method begins first by using the Hilbert-Huang transform (HHT) to detect the harmonic characterizing the fault based on the complete empirical ensemble mode decomposition (CEEMD) of the three-stator currents (ias, ibs, ics ). The CEEMD provides the intrinsic mode function (IMF) which contains information of the IGBT open-circuit fault. For the exact choice of the IMF, a statistical study based on the calculation of the root mean square values (RMS) is carried out for each IMF. The IMF choice depends on the condition that the RMS values of the inverter upper IGBTs are always lower than the RMS values of the complementary ones. The results obtained can be seen to respond well to the RMS condition and the spectral envelope of the IMF1 makes it possible to detect the harmonic characterizing the inverter IGBT open-circuit fault. The proposed diagnosis method then moves to the use of the artificial neural network (ANN) to localize the faulty IGBT. The results obtained using the proposed method are validated experimentally and demonstrate well their effectiveness with a very high classification rate.

A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter

In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results.

A novel IGBT open-circuit protection method for three-phase PWM rectifier

In order to avoid the damage or the secondary fault caused by serious over-current, a new solution for protection of IGBT open-circuit faults in three-phase PWM rectifier is presented in this paper. Firstly, the open-circuit faults features of three-phase PWM rectifier have been analysed, and it was found that the most obvious features of open-circuit faults in three-phase PWM rectifier are the rise of some phase currents and the drop of DC voltage. Secondly, compared with the maximum and minimum absolute values of phase currents under normal state, one of them always can be 1.3 times larger than that of normal state when open-circuit faults occur in IGBTs except all IGBTs. Finally, by observing the DC voltage and the currents' behaviour, the maximum-minimum values-based method can detect the open-circuit faults of the system. The experiment results have verified that the method can effectively detect the open-circuit faults in IGBTs and protect the system.

A Data-Driven Method for IGBT Open-Circuit Fault Diagnosis Based on Hybrid Ensemble Learning and Sliding-Window Classification

In this article, a novel data-driven method is proposed for open-circuit fault diagnosis of insulated gate bipolar transistor used in three-phase pulsewidth modulation converter. Based on the sampled three-phase current signals, fast Fourier transform and ReliefF algorithm are used to select most correlated features. Then, based on two randomized learning technologies named extreme learning machine and random vector functional link network, a hybrid ensemble learning scheme is proposed for extracting mapping relationship between fault modes and the selected features. Furthermore, in order to achieve an accurate and fast diagnostic performance, a sliding-window classification framework is designed. Finally, parameters in the diagnostic model are optimized by a multiobjective optimization programming model to achieve optimal balance between diagnosis accuracy and speed. At offline testing stage, the overall average diagnostic accuracy can be as high as 99% with the diagnostic time of around one-cycle sampling time. Furthermore, real-time experiments verify its effectiveness and reliability under different operation conditions.

Neural Network Based Fault Diagnosis of Three Phase Inverter Fed Vector Control Induction Motor

The paper investigates the detection and location of IGBT open-circuit faults in two-level inverter fed induction motor controlled by indirect vector control strategy. The investigation proposes two new approaches entirely based on the Artificial Neural Network (ANN) for the extraction of the exact fault angle corresponding to the IGBT switch open-circuit fault. The first approach (Approach1) based on the Clark currents transform calculates the average value of the Clark currents to find the exact fault angle θ. The second approach (Approach2) based directly on the three-phase stator currents (without any transformation) calculates the average value of the three-phase currents to determine the exact fault angle between the phases (θab, θbc, θca). The paper conducts also a comparative study between the two approaches to assess the merits of each one of them. Experimental work is conducted to illustrate the effectiveness of the techniques and validate the results obtained.