Fault Diagnosis Of Inverter Research Articles

OC fault diagnosis of multilevel inverter using SVM technique and detection algorithm

The Open Circuit (OC) faults occurring in switches of Multilevel Converters (MLC) may lead to undesirable operation of the converter. Therefore, fault detection and its localization in minimum time are necessary. This paper focuses on the fast fault detection algorithm based on the two samples technique and the fault localization algorithm using the Entropy of Wavelet Packets (EWP) as a feature. The EWP feature is used to classify and localize the OC faults in Insulated Gate Bipolar Transistors (IGBTs) of three-phase, three-level inverter using Support Vector Machine (SVM) based fault classification algorithm. The proposed technique can detect the fault in single IGBT and multiple IGBTs in a lesser time range of microseconds to 0.33 ms. It gives better performance and accuracy (99.70%) than previously proposed SVM algorithms, as the EWP-based feature extraction process used in this paper is simple and accurate with a less computational burden.

Feature fusion based fault diagnosis of hoist inverter

Feature fusion based fault diagnosis of hoist inverter

The Diagnostic Method for Open-Circuit Faults in Inverters Based on Extended State Observer

To reduce the influence of unknown disturbance on open-circuit fault diagnosis of inverters in the motor drive system, an open-circuit fault diagnosis method, which is based on extended state observer, is proposed for inverters. A mixed logic dynamic model of the inverters is established by analyzing the current flow path when the system works normally and there are open-circuit faults. A voltage extended state observer is designed for the mixed logic dynamic model. The open-circuit faults are detected according to the phase voltage residual between the observed voltage and the actual voltage. The position of the faulty switches is determined by querying the voltage residual information table. Finally, the simulation results show that the method can effectively reduce the influence of the unknown interference on the inverter faults diagnosis, improve the fault diagnosis rate, and verify the effectiveness and feasibility of the method.

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

The increasing reliability and availability requirements of power electronic systems have drawn great concern in many industrial applications. Aiming at the difficulty in fault characteristics extraction and fault modes classification of the three-phase full-bridge inverter (TFI) that used as the drive module of brushless DC motor (BLDCM). A hybrid convolutional neural network (HCNN) model consists of one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) is proposed in this paper, which can tap more effective spatial feature for TFI fault diagnosis. The frequency spectrum from the three-phase current signal preprocess are applied as the input for 1D-CNN and 2D-CNN to conduct feature extraction, respectively. Then, the feature layers information are combined in the fully connected layer of HCNN. Finally, the performance status of TFI could be identified by the softmax classifier with Adam optimizer. Several groups of experiments have been studied when the BLDCM under different operating conditions. The results show that the fusion features can get a higher degree of discrimination so as to the presented network model also obtains better classification accuracy, which verify the feasibility and superiority to the other networks.

Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach

Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach

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.

Simple Threshold Method in Fault Diagnosis for Voltage Source Inverter in a Direct Torque Control Induction Motor Drive

Background: The need for a diagnosis today, becomes a necessity for variable speed AC drives in several industrial applications. An important research axis is oriented towards monitoring the state of the converter supplying the electric motor. Indeed, the voltage source inverter is likely to have switching faults. Therefore, an emergency stop of the motor drive must be done. Objective: After reviewing related patents and works, the objective of this paper is to identify the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. Methods: The proposed approach is a simple threshold fault classification method applied to fault diagnosis of a Direct Torque Control (DTC) induction motor drive using the stator Concordia mean current vector. With a fault occurrence, a localization domain consisting of seven patterns is constructed. Results: Simulated results on 1.5-kW induction motor drive show the effectiveness of the proposed approach with a good classification performance. Conclusion: The classification performance of our simple diagnosis system is acceptable for one fault occurrence compared to other methods. Faulty switch detection and identification is completed within a few periods of current. Using intelligent technique should improve classification performances for multiple faults occurrence.

Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives

This paper presents an extension theory-based assessment method to perform fault diagnosis for inverters in motor driving systems. First, a three-level neutral-point clamped (NPC) inverter is created using the PSIM software package to simulate faults for any power transistor in the NPC-type inverter. Fast Fourier transformation is used to transform the line current signals in the time domain into a spectrum in the frequency domain for analysis of the corresponding spectrum of features of the inverter for faults with different power transistors. Then, the relationships between fault types and specific spectra are established as characteristics for the extension assessment method, which is then used to create a smart fault diagnosis system for inverters. Fault-tolerant control (FTC) is used here when the rated output of a faulty inverter is decreased in order to maintain balanced output in three phases by changing the framework of the transistor connection. This is performed to reinforce the reliability of the inverter. Finally, by the simulation and experimental results, the feasibility of the proposed smart fault diagnosis system is confirmed. The proposed fault diagnosis method is advantageous due to its minimal use of data and lack of a learning process, which thereby reduces the fault diagnosis time and makes the method easily used in practice. The proposed fault-tolerant control strategy allows both online and smooth switching in the wiring structure of the inverter.

Open-circuit fault diagnosis of traction inverter based on improved convolutional neural network

In the traction system of high-speed EMUs, the inverter’s Insulated Gate Bipolar Transistor (IGBT) often occurs open-circuit (OC) faults. However, traditional fault diagnosis relies mainly on signal processing to extract fault features, which is susceptible to environmental interference, resulting in poor generalization ability of the model. Aiming at this problem, a fault diagnosis method based on an improved convolutional neural network is proposed. Firstly, the three-phase stator current signal is preprocessed by wavelet domain denoising. Secondly, fault features are independently learned through a convolutional network. Finally, a fully-connected layer is used for fault diagnosis. The experimental results show that this method could resolve the OC fault diagnosis problem of the inverter’s IGBT effectively, and also achieve higher accuracy under the interference of noise, and the diagnosis can be made at 0.01s after the fault occurs.

Rotor fault diagnosis of frequency inverter fed or line-connected induction motors using mutual information

Characteristics like robustness, adaptability to several load conditions, and low costs of operation and maintenance are some of the reasons three-phase induction motors are ubiquitous in industrial applications. Even so, these machines are subjected to electrical and mechanical faults which can result in malfunction. The detection of incipient faults is the focus of several recent studies because they provide information for timely decisions to avoid unplanned stops of industrial processes. A common problem of induction motors is the presence of broken rotor bars. Most of the methods of condition monitoring and fault diagnosis focus on the usage of only one type of power supply: line-connected or fed by frequency inverters. Given this situation, we present an alternative technique for the detection of broken rotor bars of three-phase induction motors regardless of the type of power supply. Our approach is based on similarity measures of the stator current signals of two motor phases in order to extract the relevant features of such signals, which are then classified using three intelligent systems: artificial neural network, support vector machines, and k-nearest neighbors. We performed 3310 experimental tests with motors operating in steady state, with sinusoidal and non-sinusoidal power supply, and variations of voltage unbalance levels, supply frequency, and load torque. Classification accuracy rates over 92% of success were obtained in these tests, which validate the proposed approach.

A Multiswitch Open-Circuit Fault Diagnosis of Microgrid Inverter Based on Slidable Triangularization Processing

In this article, a robust inverter fault diagnosis algorithm is proposed under microgrid environment considering unbalanced state and overcurrent component interference. First, the detected signals are transformed into different data triangles by the proposed slidable triangularization processing which can conveniently and effectively extract the multiscale trend features and data jitter components. Further, the signed features are obtained by the proposed jitter signing processing method, which can reduce the influence of amplitude, and facilitate the logical operation and mixed operation. Then, a quantitative trend fault degree is calculated by the signed information to reflect the fault degree and location information, which has the characteristics of quantitative change. It can reduce the fluctuation and quantity of fault data features. Finally, echo state network is used to implement the intelligent classification. Because the design of hidden layer and the network training speed is simple and fast, which makes the design, implementation, and debugging of fault diagnosis more convenient. Compared with the existing fault diagnosis algorithms, it is robust to unbalanced state and overcurrent component interference of microgrid. These features make it more suitable for inverter fault diagnosis of microgrid environment. The effectiveness of fault diagnosis algorithm is verified by the experiments and comparisons.

Single-Switch Open-Circuit Diagnosis Method Based on Average Voltage Vector for Three-Level T-Type Inverter

Fault-tolerant control strategy plays a significant role in improving the reliability of three-level T-type inverter where fault diagnosis method is the key and a research hotspot. Load power factor variation and modulation index regulation have great effect on conventional load current based diagnosis methods. Therefore, this article proposes a novel voltage vector based method for single-switch open-circuit fault diagnosis in three-level T-type inverter. Average output voltage vector calculated by voltages between dc-link neutral-point and bridge output terminals is taken as the eigenvector in the procedure and failed switch can be located by three diagnosis variables including angle of eigenvector, normalized modulus of eigenvector, and neutral-point potential. These variables will be some certain values under circuit fault, which are defined as eigenvalues. The vector trajectory prediction technology is utilized for threshold setting, by which the diagnosis is suitable for different power factors and different modulation indices. The strategy of redundant vector replacement is applied to realize fault-tolerant operation and verifying the proposed scheme. Simulations and experiments are carried out to illustrate the superiorities of the proposal.

Open gate fault diagnosis and tolerant for voltage source inverter fed speed sensorless induction motor drive

ABSTRACT This paper presents an effective fault diagnostic and tolerant method for an open-gate fault of a three-phase inverter. The induction motor (IM) drive system is controlled by a speed sensorless indirect rotor field-oriented control (IRFOC). The proposed diagnostic method requires low computation to detect open-gate faults. This method uses a simple direct current technique based on the measured motor currents to detect faulty switch. The fault-tolerant scheme is based on changing the inverter topology from six to four switches to ensure proper operation. In the faulty case, the fault-tolerant technique disconnects the motor phase terminal from the faulty arm of the inverter and connects it to the midpoint of the DC-link via an external switch. The performance of the IM drive is investigated and analysed under normal, faulty and post-fault operations through simulation and experiment. The results demonstrate the effectiveness of the fault diagnostic and tolerant method for retaining almost the normal performance of the IM drive system.

Open-circuit fault diagnosis of three-level inverter based on Park transformatio

Open-circuit fault diagnosis of three-level inverter based on Park transformatio

Open-circuit fault diagnosis of traction inverter based on compressed sensing theory

This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information (G-LS-SVM) to solve the insulated-gate bipolar transistor(IGBT) open-circuit failure problem of the traction inverter in a catenary power supply system. First, a simulation model based on traction inverter topology is built, and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification. Second, compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal. The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics. Finally, the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities. A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary. The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%. Moreover, the G-LS-SVM model is robust and not affected by Gaussian white noise.

Three-Level T-Type Inverter Fault Diagnosis and Tolerant Control Using Single-Phase Line Voltage

This study combines extension theory and chaos theory to create a three-level T-type inverter fault diagnosis system that uses single-phase line voltage signals. First, the three-level T-type inverter single-phase line voltage output waveform was measured when faults occurred separately in 12 power transistors. Subsequently, the Lorenz master-slave dynamic error transformation was used to obtain the chaos eye coordinates, which functioned as fault characteristics. Fault diagnosis involved extension theory-based fault categorization. Specifically, fault characteristic values were adopted as the input signal to determine the correlation between chaos eyes coordinates and transistor fault, thereby locating faulty transistors. In summary, this study established a low-cost and fast-operating inverter fault diagnosis system. The system integrates detection results with inverter fault-tolerant control to enable the constant operation of inverters in power generation without derating, thereby greatly enhancing system reliability. Finally, the reliability of the smart fault diagnosis system and fault-tolerant control was verified through measurement results.

Current observer-based online open-switch fault diagnosis for voltage-source inverter

In order to guarantee the system reliability and enhance post-fault maintenance efficiency of inverters, this paper presents an current observer-based online open-switch fault diagnosis method for the voltage-source inverters (VSI). Analyzing the state space model of VSI when fault occurs, a state observer of the system is built to estimate phase currents. Once the open-switch fault occurs, the current residual vector between the estimated currents on observer and the measured currents, and fault diagnosis is realized by exploiting the featured amplitude and phase of the current residual vector. In order to realize on-line diagnosis with high reliability and speed, the average value of current residuals in a fundamental period is obtained and Clark transformation is performed so as to acquire vector angle. In addition, the sum of absolute values of measured currents is normalized to serve as the sign of fault occurrence. The simulation and experimental results show the effectiveness of proposed method.

A Current-Based Approach for Short-Circuit Fault Diagnosis in Closed-Loop Current Source Inverter

In this article, a method based on output currents is proposed to diagnose short-circuit faults of single or double switches in current source converter. The method only needs the feedback currents and their corresponding reference signals that can be obtained from the main control system. This diagnosis algorithm needs to calculate the average values of the feedback currents and the average absolute values of the current reference signals in one power frequency cycle. Diagnosis variables can be obtained from the ratio of the two values. Then, three detection thresholds are set to judge and locate the short-circuit power switches. The method can be implemented in the main control program directly, without additional voltage sensors and other auxiliary detection devices. Meanwhile, the method is simple and easy to implement in practice. The effectiveness of proposed algorithm is validated with experiments.

Application of hybrid dimensionality reduction for fault diagnosis of three-phase inverter in PMSM drive system

The feature dimension reduction is a useful pre-processing step for fault diagnosis, where the irrelevant and redundant information in the data can be reduced. In this case, not only the computation complexity can be reduced, but also the better classification performance can be obtained. Hence, this paper presents a multiple fault diagnosis method for the three-phase inverter in PMSM drive system, where the hybrid dimensionality reduction method is applied to reduce the dimension of the fault features. First, time-domain fault features are extracted from the line-to-line voltage signals. Second, a feature reduction is performed by combining principal component analysis and linear discriminant analysis, where the linear discriminant analysis is improved by introducing the singular value decomposition and redefining the between-class scatter which solve the issue of small sample and the problem that the similar classes are not easily separated. Finally, the faults are diagnosed by support vector machine. Both the simulation and the experimental results show that the proposed method can improve the discrimination performance of the fault types after the dimension reduction, making it in favor of fault classification.

Multi-Switches Fault Diagnosis Based on Small Low-Frequency Data for Voltage-Source Inverters of PMSM Drives

For the inverter fault diagnosis of permanent magnet synchronous machine drives, it is difficult to simply and conveniently use the small low-frequency data without needing additional hardware and affecting the accuracy of the diagnosis results. This problem often leads to unnecessary computation and wastes a lot of corresponding system memory, increases the hardware requirements and realization difficulty, and affects practicability. In order to improve this problem, to make the design, debugging, and implementation of fault diagnosis more convenient and at low cost, and further to improve the practicability of the algorithm, a novel multi-switches fault diagnosis algorithm is proposed. First, a second low frequency processing method is used to obtain the small low-frequency data simply from the feedback signals of the controller. These small low-frequency data contain the main feature information of different switch states. Second, these small low-frequency data are effectively normalized by the single extremum normalization method, which is based on the symmetry features of different state data. These processed data can keep the asymmetry feature of the fault state of an inverter very well. Third, the main fault components and features are extracted from the distortion part and envelope change of the processed small low-frequency data. Fourth, echo state network is used by combining it with the extracted features to implement the intelligent classification. Different from the traditional neural network, the design of structure of hidden layer network is simplified, and the network training speed is very fast. Therefore, the debugging of this network is very convenient. Compared with the existing fault diagnosis algorithms, the proposed algorithm is convenient and at low cost to realize the multi-switches fault diagnosis. The effectiveness of fault diagnosis algorithm is verified by the experiment.