Fault Diagnosis Of Power Converters Research Articles

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

Power converters can convert the electrical energy output by power source into specific forms required by target device. They are widely used in various fields such as electrification, transportation, and power systems, and are the core components of the systems. Power converters are composed of power semiconductor devices (such as IGBTs), which are prone to failure due to abnormal conditions and aging degradation, leading to power converter faults. Therefore, over the past twenty years, extensive fault diagnosis methods for power converters have been studied and proposed. Among them, symmetry-based methods have the characteristics of rapid diagnosis speed and strong robustness, but so far there’s no systemic summary of such methods. Therefore, this paper reviews the relevant research on fault diagnosis of power converters based on symmetry in recent years, analyzes the mathematical principles of these methods, and compares their diagnostic performance. In addition, two factors that may have an influence on the symmetry are pointed out. This paper provides references and suggestions for the future development and research of power converter fault diagnosis.

A Multi-Information Fusion Algorithm to Fault Diagnosis of Power Converter in Wind Power Generation Systems

Power electronics-based converters are the major and most vulnerable components in wind power generation systems. Converter faults will affect power quality,damage expensive equipment such asgenerators, or even pose a massive threat to the entire power grid. Fault diagnosis is considered as a powerful means to improve system reliability and reduce maintenance costs. Existing data-driven fault diagnosis methods related to improving feature extraction approaches to obtain high diagnostic accuracy are mostly based on single-scale feature of signals; this neglects the potentially valuable information of other scales. This paper proposes an algorithm of Dempster-Shafer and Deng entropy fusion multi-scale approximate entropy (DSDEMAE) for wind power converter fault diagnosis. Firstly, it calculates the multi-scale approximate entropy of fault signals, mining more potentially valuable information. Secondly, Dempster-Shafer is used to fuse features of various scales and effectively handles the conflicts and uncertainties between different features. Finally,Deng entropyis adopted to measure the uncertainty to adjust weight distribution, reducing the influence of highly conflicting features and mutually benefitting features of different scales. Extensive experimental results on simulated and experimental data demonstrate the effectiveness of the proposed algorithm. Compared with advanced methods, this method can diagnose the faults with higher accuracy and stronger robustness.

Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM

In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possibly increase the accuracy of fault diagnosis. This strategy involves three significant steps: first, a size reduction procedure is applied to the training dataset, which uses hierarchical K-means clustering and Euclidean distance schemes; second, both significantly reduced training datasets are utilized by the KPCA technique to extract and select the most sensitive and significant features; and finally, in order to distinguish between the diverse WEC system operating modes, the selected features are used to train a bidirectional long-short-term memory classifier (BiLSTM). In this study, various fault scenarios (short-circuit (SC) faults and open-circuit (OC) faults) were injected, and each scenario comprised different cases (simple, multiple, and mixed faults) on different sides and locations (generator-side converter and grid-side converter) to ensure a comprehensive and global evaluation. The obtained results show that the proposed strategy for FDD via both applied dataset size reduction methods not only improves the accuracy but also provides an efficient reduction in computation time and storage space.

Interactive multi-model fault diagnosis method of switched reluctance motor based on low delay anti-interference.

Given fault false alarm and fault control failure caused by the decrease of fault identification accuracy and fault delay of Switched Reluctance Motor (SRM) power converter in complex working conditions, a method based on the Interactive Multi-Model (IMM) algorithm was proposed in this paper. Besides, the corresponding equivalent circuit models were established according to the different working states of the SRM power converter. The Kalman filter was employed to estimate the state of the model, and the fault detection and location were realized depending on the residual signal. Additionally, a transition probability correction function of the IMM was constructed using the difference of the n-th order to suppress the influence of external disturbance on the fault diagnosis accuracy. Concurrently, a model jump threshold was introduced to reduce delay when the matched model was switched, so as to realize the rapid separation of faults and effective fault control. The simulation and experiment results demonstrate that the IMM algorithm based on low delay anti-interference can effectively reduce the influence of complex working conditions, improve the anti-interference ability of SRM power converter fault diagnosis, and identify fault information accurately and quickly.

Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks

Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches.

Analysis of Interpretability and Generalizability for Power Converter Fault Diagnosis Based on Temporal Convolutional Networks

Despite the success of data-driven converter fault diagnosis methods, interpretability and generalizability limit the further promotion of data-driven methods in industrial applications. Therefore, to improve the accuracy in face of out-of-distribution problems and increase confidence of power converter fault diagnosis, it is essential to understand the change and decision mechanism inside the deep model. First, we construct a general temporal convolutional network to visualize the diagnostic process, which has been proven effective in power converter fault diagnosis. Then, the effect of hyperparameters on generalizability is analyzed under typical power converter disturbances. Finally, the concern area of the model for the current in the fault decision is interpreted intuitively by gradient-weighted class activation mapping and the feature maps generated by the different channels are analyzed from multiple perspectives. The visualization results help to understand the complex structure of neural networks and can support the design of model to improve generalizability.

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.

Wind turbine power converter fault diagnosis using DC-link voltage time–frequency analysis

With the developments in power electronic devices, there is increasing use of the insulated gate bipolar transistor devices in power converters. Power converters are more and more gaining attention in the wind energy conversion system. In this article, a grid-connected wind energy conversion system is considered. Wind energy conversion system optimal operation requires a diagnostic method for back-to-back power converter to be addressed in detail in this article. Therefore, an open-circuit switching fault diagnosis method for a back-to-back power converter is developed to improve the system reliability and optimize the produced power and also achieve its real operational cost. In this work, we investigate only the DC-Link voltage signal and we use the time–frequency analysis to achieve the diagnosis purpose. The scheduled diagnosis approach is divided into two tasks: the first one is the fault detection task and the second one is the fault localization task. The main novelty of the proposed diagnosis approach is to localize the appropriate faulty power converter, that is, rotor side power converter or grid side power converter. Also, this approach is able to localize faults when there is a simultaneous fault on both power converters. Simulations are carried out to verify the robustness of the proposed diagnosis approach. The achieved simulation results would help in diagnosing of Back-to-back power converter that would expectedly cancel the traditional diagnosis method.

Fault Diagnosis of Wind Turbine Power Converter Considering Wavelet Transform, Feature Analysis, Judgment and BP Neural Network

The larger capacity of power converters increases with the size of wind turbines over the years, which implies more failures occur in the components of power converter. Meanwhile, the strategies of fault diagnosis of power converter in wind turbine are still under discussion and research. The main purpose of this paper is proposing a strategy containing wavelet transform, feature analysis, judgment and back propagation neural network (BPNN) classification (WT-FA-JD-BP) to identify the single and double power components open-circuit faults accurately, happen in grid-side converter (GSC) of permanent magnet synchronous generator (PMSG) wind turbine systems. First, as the original signals, the three-phase bridge legs voltage of the GSC are collected under different faults. Second, wavelet transform is used to decompose and reconstruct the signals. Third, a new method includes feature analysis and judgment is conducted to amplify the divergence of data obtained by wavelet transform. Finally, the data are used as the inputs of BPNN for decision-making and classification. The simulation and experimental results show that the proposed strategy can classify the single and double open-circuit faults, and the accuracy is higher than that without data feature analysis and judgment.

Fault prediction of power electronics modules and systems under complex working conditions

Power electronics modules such as inverters and rectifiers are crucial in industry and they are indispensable in various power conversion systems. There have been many studies on the fault diagnosis of power converters or power modules in the system but recently more attention has been paid on predicting failures. Most conventional techniques often rely on accurate physical models or high frequently sampled electric signals in simulation or experiment environments. In practice, however, the life and the degradation of power electrics devices are highly influenced by loads and operation regimes. The analysis needs considering various identical or similar devices in a networked power grid as well. Thus, it is not trivial to achieve the predictive analysis in such complex working conditions. This paper presents a systematic approach investigating the fault prediction of power converters in power conversion systems. Two data-driven methods with novel techniques, which take into account working condition variances and the data imbalance, have been developed and applied to an industry use case where only high level system heartbeat signals are available. These methods are validated to effectively predict the power converter failures.

An Active Life Extension Strategy for Thermally Aged Power Switches Based on the Pulse-Width Adjustment Method in Interleaved Converters

The research on noninvasive incipient fault diagnosis of power converters is very critical to avoid strenuous periodic checkups and costly interruptions. In many of the research studies, on-state resistance of power MOSFETs is identified as the fault precursor, and suggested to be monitored online for prognosis. In this paper, a condition monitoring algorithm is proposed for power MOSFETs of interleaved converters and integrated with the conventional current loops. The proposed technique utilizes on-state resistance information of the switches to adjust the current reference of each interleaving leg. In addition, a pulse-width adjustment method is proposed to avoid one current sensor and current-control loop. With the proposed scheme, the thermal stress on the aged device can be reduced, and the overall lifetime of the converter can be extended. The proposed approach is verified on two-leg interleaved converters both in simulation and experimentally.

Remaining Useful Lifetime Estimation for Thermally Stressed Power MOSFETs Based on <sc>on</sc>-State Resistance Variation

The research on noninvasive incipient fault diagnosis of power converters is very critical to avoid strenuous periodic check-ups and costly interruptions. Thermal cycling is one of the main techniques to accelerate the package-related failure progress. In this paper, first, a custom designed accelerated aging platform that can expose multiple discrete power MOSFETs to thermal stress simultaneously is introduced. Based on the collected experimental data, the variation of the on -state resistance is identified as the failure precursor, and an exponential degradation model that fits successfully with the experimental data are developed. The remaining useful lifetime (RUL) of degraded power MOSFETs is estimated through classical least-squares algorithm run on experimental data filtered by Kalman Filter which deals with the measurement noise and model uncertainties. The essential advantage of the proposed method is that it does not require junction temperature information. The RUL estimation with limited field data is demonstrated on a number of experimental results.

Fault Diagnosis of Power Converter for Switched Reluctance Motor based on Discrete Degree Analysis of Wavelet Packet Energy

Power converter plays a very important role in switched reluctance motor (SRM) systems, and it is also the easiest one to experience failures. Power converter faults will cause the motor to run in non equilibrium states, and a long time fault operation will lead to motor and other modules damaged, and make the system completely lose working stability. This paper uses an asymmetric bridge converter as the research object with three-phase SRM, employs the wavelet packet decomposition for the phase currents. It analyzes and studies the short circuit fault condition of IGBT, uses an energy discrete degree of the wavelet packet nodes as the fault characteristic, and conducts the corresponding experimental and simulation analysis to verify the effectiveness and practicality of the proposed method.

A New Method for Power Converter Fault Diagnosis in SRM Drives

This paper presents a new fault diagnostic technique applied to switched reluctance motor drives, based on the analysis of the power converter supply current. A fault is detected when the measured amplitude of the dc bus current differs from its expected amplitude, assuming normal operating conditions. The information about phase currents amplitudes and the control commands of all power switches permit to easily estimate the amplitude of the power converter supply current, since an asymmetric bridge converter is used. Simulation and experimental results are presented. Open- and short-circuit fault occurrences in the converter power switches are considered and analyzed. The proposed technique can early detect these fault occurrences and can also identify the affected motor phase. In almost all situations, the faulty element is also identified. An early fault diagnosis, with an accurate fault identification, is of a paramount importance since it permits the early adoption of fault-tolerant procedures that minimize the fault impact on the machine operation.