Insulated Gate Bipolar Transistor Faults Research Articles

Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.

Fault diagnosis of wind turbine power converter using intrinsic mode functions with relative energy entropy

PurposeThe purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.Design/methodology/approachThe most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.FindingsThe presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.Originality/valueThis study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission.

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges’ currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results.

Switch Open-Fault Detection for a Three-Phase Hybrid Active Neutral-Point-Clamped Rectifier

This paper proposes a fault-detection method for open-switch failures in hybrid active neutral-point-clamped (HANPC) rectifiers. The basic HANPC topology comprises two SiC-based metal-oxide-semiconductor field-effect transistors (MOSFETs) and four Si insulated-gate bipolar transistors (IGBTs). A three-phase rectifier system using the HANPC topology can produce higher efficiency and lower current harmonics. An open-switch fault in a HANPC rectifier can be a MOSFET or IGBT fault. In this work, faulty cases of six different switches are analyzed based on the current distortion in the stationary reference frame. Open faults in MOSFET switches cause immediate and remarkable current distortions, whereas, open faults in IGBT switches are difficult to detect using conventional methods. To detect an IGBT fault, the proposed detection method utilizes some of the reactive power in a certain period to make an important difference, using the direct-quadrant (dq)-axis current information derived from the three-phase current. Thus, the proposed detection method is based on three-phase current measurements and does not use additional hardware. By analyzing the individual characteristics of each switch failure, the failed switch can be located exactly. The effectiveness and feasibility of the proposed fault-detection method are verified through PSIM simulations and experimental results.

An Online Data-Driven Method for Simultaneous Diagnosis of IGBT and Current Sensor Fault of Three-Phase PWM Inverter in Induction Motor Drives

This article presents an online data-driven diagnosis method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in the three-phase pulsewidth modulation inverter. The fast Fourier transform (FFT) algorithm is used to extract the fault frequency spectrum features of the three-phase currents. Then, a feature selection method named ReliefF is introduced to select the most critical features by removing redundant and irrelevant features. In addition, as novel fast learning technology, a random vector functional link network is applied to learn the faulty knowledge from the historical dataset. Based on the well-learned model, the fault type and location of the converter can be accurately identified as long as the three-phase current signals are measured. Offline test results verify that the proposed method can identify the IGBT and sensor faults with an accuracy of 98.83% and outperforms the state-of-the-art learning algorithms. Moreover, the real-time hardware-in-the-loop test results show that the proposed method can successfully identify the IGBT faults and current sensor faults within 22 ms. It is robust to the dc-link voltage fluctuations, model parameters, and speed or load variations. The extensibility of the proposed method is also validated based on the test results in terms of other fault modes and drive systems.

A Single Method for Multiple IGBT, Current, and Speed Sensor Faults Diagnosis in Regenerative PMSM Drives

This paper proposes a single fault diagnostic method for regenerative permanent magnet synchronous machine drives with the ability to handle multiple insulated gate bipolar transistor faults, single current sensor fault, and rotor-position disconnection. The considered faults are discriminated, and the faulty device is correctly identified in each case. The robustness against false alarms is improved by using adaptive thresholds and normalized diagnostic variables. The proposed algorithm requires the information given by current sensors and the rotor position sensor that are already available from the drive system, without requiring a high computational burden. Simulation and experimental results are presented to validate the effectiveness of the considered algorithm for both motoring and regenerative operations.

Performance investigation of an advanced diagnostic method for SSTPI-fed IM drives under single and multiple open IGBT faults

PurposeThis paper aims to analyze and investigate the performance of an improved fault detection and identification (FDI) method based on multiple criteria, applied to six-switch three-phase inverter (SSTPI)-fed induction motor (IM) drives under both single and multiple open insulated-gate bipolar transistors(IGBT) faults.Design/methodology/approachThis paper proposes an advanced diagnostic method for both single and multiple open IGBT faults dedicated to SSTPI-fed IM drives considering five distinct faulty operating conditions as follows: a single IGBT open-circuit fault, a single-phase open-circuit fault, a non-crossed double fault in two different legs, a crossed double fault in two different legs and a three-IGBT open-circuit fault. This is achieved because of the introduction of a new diagnosis variable provided using the information of the slope of the current vector in (α-β) frame. The proposed FDI method is based on the synthesis and the analysis, under both healthy and faulty operations, of the behaviors of the introduced diagnosis variable, the three motor phase currents and their normalized average values. Doing so, the developed FDI method allows a best compromise of fast detection and precision localization of IGBT open-circuit fault of the inverter.FindingsSimulation works, carried out considering the implementation of the direct rotor flux oriented control in an IM fed by the conventional SSTPI, have proved the high performance of the advanced FDI method in terms of fast fault detection associated with a high robustness against false alarms, against speed and load torque fast variations and against the oscillations of the DC-bus voltage in the case of both healthy and faulty operations.Research limitations/implicationsThis work should be extended considering the validation of the obtained simulation results through experiments.Originality/valueDifferent from other FDI methods, which suffer from a low diagnostic effectiveness for low load levels and false alarms during transient operation, this method offers the potentialities to overcome these drawbacks because of the introduction of the new diagnosis variable. This latter, combined with the information provided from the three motor phase currents and their normalized average values allow a more efficient detection and identification of IGBT open-circuit fault.

A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter

Three-phase pulse width modulation converters using insulated gate bipolar transistors (IGBTs) have been widely used in industrial application. However, faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads. For ensuring system reliability, it is necessary to accurately detect IGBT faults accurately as soon as their occurrences. This paper proposes a diagnosis method based on data-driven theory. A novel randomized learning technology, namely extreme learning machine (ELM) is adopted into historical data learning. Ensemble classifier structure is used to improve diagnostic accuracy. Finally, time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time. By this mean, an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time. Compared to other traditional methods, ELM has a better classification performance. Simulation tests validate the proposed ELM ensemble diagnostic performance.

On-line fault diagnosis model for locomotive traction inverter based on wavelet transform and support vector machine

A traction inverter is the power source for rail transit vehicles. An insulated-gate bipolar transistor (IGBT) is the primary component of a traction inverter. IGBT faults can cause serious problems in locomotive power supply systems. The disadvantage of traditional fault detection methods for IGBT modules is a lack of real-time processing and high efficiency. An on-line fault diagnosis method based on a wavelet transform and multi-classification support vector machine (multi-SVM) is proposed for IGBT faults. Wavelet decomposition is used to process fault current signals, and energy vectors are constructed. Multi-SVM is used to establish a fault recognition model. The validity of this method is verified by simulations with MATLAB/Simulink.

Noninvasive Localization of IGBT Faults by High-Sensitivity Magnetic Probe With RF Stimulation

This paper presents a new and simple method to noninvasively localize failure sites in an insulated-gate bipolar transistor (IGBT) module by using a magnetic probe. The method detects the magnetic field generated by leakage currents occurring at the broken gates of an IGBT module. In this method, not only an RF stimulus signal but also a dc bias voltage is applied to the IGBT module under test, because the resistance of the failure site’s change depends upon the dc bias condition. A high-sensitivity magnetic probe is used to detect the magnetic field to locate the corresponding fault positions. The magnetic probe is fully integrated onto a chip by a 180-nm CMOS process and includes a magnetic pickup coil, a low-noise amplifier, a downconversion mixer, and a low-pass filter. Magnetic scanning experiments are implemented for two kinds of IGBT module samples to demonstrate the detection of the broken gate positions. In addition, a visual confirmation of the broken gate positions is performed. Several postprocessing steps in a clean room are applied to these samples in order to visually confirm the scanning results of the proposed method. Experimental results successfully demonstrate that our method can be used for noninvasive localization of small intrinsic faults, on two kinds of SiC and Si substrates, of IGBT modules.

Comparative Study of Fault Diagnostic Methods in Voltage Source Inverter Fed Three Phase Induction Motor Drive

Three phase Pulse Width Modulation inverter plays vital role in industrial applications. The performance of inverter demeans as several types of faults take place in it. The widely used switching devices in power electronics are Insulated Gate Bipolar Transistors (IGBTs) and Metal Oxide Field Effect Transistors (MOSFET). The IGBTs faults are broadly classified as base or collector open circuit fault, misfiring fault and short circuit fault. To develop consistency and performance of inverter, knowledge of fault mode is extremely important. This paper presents the comparative study of IGBTs fault diagnosis. Experimental set up is implemented for data acquisition under various faulty and healthy conditions. Recent methods are executed using MATLAB-Simulink and compared using key parameters like average accuracy, fault detection time, implementation efforts, threshold dependency, and detection parameter, resistivity against noise and load dependency.

IGBT fault current limiting circuit

There is a growing market demand for insulated gate bipolar transistors (IGBTs) with high efficiency but long short-circuit withstand time. The inherent device tradeoff, however, does not allow device designers to achieve both goals simultaneously. The proposed circuits, by limiting the fault current magnitude, extends the short-circuit withstand time of high efficiency (high-gain) IGBTs. Limiting of the fault current magnitude also results in reduced turn-off voltage transients; a desirable byproduct, especially for higher current modules. Moreover, the adverse Miller effect is counterbalanced to a great degree. If the fault current is of a short transient type, the circuit restores normal operation, a unique and desirable feature for noise-prone systems. The circuit does not require an external DC supply to operate. This feature, combined with the simplicity of the circuit, makes it feasible to insert the circuit in IGBT modules or connect it as an interface between the gate driver and module.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>