IGBT Open-circuit Fault Diagnosis Research Articles

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

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 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.

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.

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 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.

IGBT Open-Circuit Fault Diagnosis in a Quasi-Z-Source Inverter

In this paper, a fast and practical method is proposed for open-circuit (OC) fault diagnosis (FD) in a three-phase quasi-Z-source inverter (q-ZSI). Compared with the existing fast OC FD techniques in three-phase voltage source inverters, this method is more cost-effective since no ultrafast processor or high-speed measurement is required. Additionally, the method is independent of the load condition. The proposed method is only applicable to Z-source family inverters and is based on observing the effect of shoot-through intervals on the system variables during switching periods. The proposed algorithm includes two consecutive stages: OC detection and fault location identification. When both stages of the OC FD algorithm are done, a redundant leg is activated and utilized instead of the failed leg. The accuracy of the proposed method is confirmed by the experimental results from a low-voltage q-ZSI prototype.

IGBT Open-Circuit Fault Diagnosis for 3-Phase 4-Wire 3-Level Active Power Filters based on Voltage Error Correlation

A novel open-circuit fault diagnosis method for 3-phase 4-wire 3-level active power filters based on voltage error correlation is proposed in this paper. This method is based on observing the output pole voltage error of the active power filter through two kinds of algorithms. One algorithm is a voltage error analytical algorithm, which derives four output voltage error analytic expressions through the pulse state, current value and dc bus voltage, respectively, assuming that all of the IGBTs of a certain phase come to an OC fault. The other algorithm is a current circuit equation algorithm, which calculates the real-time output voltage error through basic circuit theory. A correlation is introduced to measure the similarity of the output voltage errors between the two algorithms, and OC faults are located by the maximum of the correlations. A FPGA has been chosen to implement the proposed method due to its fast prototyping. Simulation and experimental results are presented to show the performance of the proposed OC fault diagnosis method.