Open-circuit Fault Research Articles

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.

Reliability evaluation of a novel fault tolerant multilevel inverter with reduced components

Safety–critical applications rely heavily on multilevel inverters. This article introduces a fault tolerant (FT) multilevel inverter sustaining an uninterrupted operation with an open switch fault occurred in a single and multiple power switches. Power semiconductor devices used in dc-ac converters increased in numbers to obtain high-quality voltage waveform which makes them vulnerable to failure. As a result, reliability is one of the biggest challenges in the utilization of multilevel inverters (MLIs) in many industrial applications. At the same time, reduced component multilevel inverters can achieve the highest resolution in output voltage waveform by compromising significant features (such as fault tolerance ability, charge balance control, etc.) but it is required to be preserved. For effective fault tolerant operation of MLIs, redundant switching states have given the highest priority in terms of switch level failures. A study is being undertaken to develop a novel fault-tolerant MLI, in the event of an open circuit fault on single and multiple switches. Therefore, the proposed FT-MLI structure can fulfill the operational requirement for a time being and claimed to be reduced components. A valid fault tolerant switching technique along with appropriate fault clearance is employed to obtain the desirable output voltage waveforms. In order to determine the feasibility of FT-MLI, performance evaluations are carried out based on results obtained under normal and abnormal operating conditions through the use of a laboratory prototype.

Fault-Tolerant and Self-Reliant Characteristic in Series Resonant Converter for Semiconductor Open/Short-Circuit Faults

With the increased popularity of series resonant converter (SRC) in grids and microgrids, a robust converter with fault-tolerant (FT) feature becomes supremely important. A single-switch primary-side failure results in half of the pre-fault voltage and lesser power at the output. While a secondary-side short-circuit (SC) fault results in negligible voltage and power, leading to discontinuous converter operation. For more than one switch fault on any side, the SRC behavior in the post-fault condition is different. This article analyses the self-reliant characteristic in SRC for single/two-switch open-circuit (OC) and SC fault. A post-fault correction is proposed, which maintains the continuity of operation and the rated output voltage at the load for different fault conditions. An FT capacitor is used along with the control parameter variation in the post-fault correction for a single-switch and different combinations of two-switch faults. The proposed method is also applicable for single-diode SC or OC fault. The analysis of the self-reliant feature and the post-fault correction is discussed and tested on a 1 kW, 250 V SRC prototype for different fault conditions.

A Concurrent Diagnosis Method of IGBT Open-Circuit Faults in Modular Multilevel Converters

The insulated-gate bipolar transistor (IGBT) is one of the most vulnerable components in the modular multilevel converter (MMC). One or more IGBT open-circuit faults may deteriorate the output performance of the MMC and even destroy the MMC systems. In this article, a concurrent and direct fault diagnosis method has been proposed. The capacitor voltages are estimated with an amending Kalman filter (KF). By comparing the measured and estimated values, the corresponding faulty submodule (SM) can be detected directly and quickly. The diagnosis process of SMs is independent of each other so that all the SMs can be monitored in real time and the diagnosis time will not increase with multiple open-circuit faults occurring. The proposed method can also identify which IGBT has occurred open-circuit fault in the faulty SM and has no special requirement for the modulation method. In addition, the amending KF has excellent robustness to the transient process and can overcome the divergence problem, which is applicable in the complex nonlinear MMC system. The effectiveness of the proposed fault diagnosis method is confirmed by simulation in MATLAB/Simulink and experimentation in a single-phase MMC prototype with eight SMs per arm.

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.

Lithium-Ion Battery Cell Open Circuit Fault Diagnostics: Methods, Analysis, and Comparison

Battery fault diagnosis has great significance for guaranteeing the safety and reliability of lithium-ion battery (LIB) systems. Out of many possible failure modes of the series–parallel connected LIB pack, cell open circuit (COC) fault is a significant part of the causes that lead to the strong inconsistency in the pack and the reduction of pack life. Therefore, it is extremely important to diagnose COC faults in real time. Motivated by this fact, we propose Kirchhoff's law based method, short-time Fourier transform based method, the Pearson correlation coefficient based method, dual extended Kalman filter (DEKF) based method, and long short-term memory recurrent neural network based method for diagnosing COC fault. These diagnostic approaches do not rely on other sensor data except pack current and terminal voltages of modules composed of cells in parallel. Furthermore, several experiments on a 4S-3P battery pack are taken under time-varying ambient temperature conditions to evaluate and compare the computation cost, diagnostic delay, and accuracy of these approaches. Test results show that only the DEKF-based approach owns the weakest robustness, and Kirchhoff's law based method with the merit of the lowest diagnostic delay and computation cost is the most appropriate approach for online COC fault diagnosis.

Magnetic stability of low corrective force PM in series hybrid magnet memory machine under inverter open-circuit fault

Variable flux memory machine (VFMM) can dynamically adjust its air-gap magnetic field by changing the magnetization states of the employed low corrective force (LCF) permanent magnets (PMs). Due to the demagnetization risk of LCF PMs under armature current, it is necessary to analyze their magnetic stability in various operation states. The magnetic stability of LCF PMs in series hybrid magnet VFMM (SHM-VFMM) under inverter open-circuit (IOC) fault is studied in this article. Firstly, the topology of the investigated SHM-VFMM is introduced, and the field-circuit coupled model of the machine under IOC fault is established. Then, the magnetic stabilities of LCF PMs during the normal and fault operations are analyzed and compared by using the finite element method. The effects of LCF PM demagnetization and fault current on torque are also studied. Afterward, the influence mechanism of the fault armature magnetomotive force (MMF) on LCF PM demagnetization is revealed by using the synthetic MMF graphic method. Finally, the main factor affecting the LCF PM demagnetization under IOC fault is highlighted and an effective method for recovering the magnetic stability of LCF PM is proposed.

A Simple Open-Circuit Fault Diagnosis Method for Grid-Tied T-Type Three-Level Inverters With Various Power Factors Based on Instantaneous Current Distortion

Different open-circuit (OC) faults for grid-tied T-type three-level inverters with various power factors may lead to similar current distortion characteristics, making it challenging to locate the faulty switch. This article proposes an OC fault diagnosis method applied to grid-tied T-type three-level inverters, which hierarchically diagnoses the group-level and device-level faults. Based on the current distortion similarity analysis with various power factors operation, the switches in one phase are divided into two groups. Subsequently, the group-level fault detection is implemented by locating the half-cycle where the zero-section phenomenon occurs. Moreover, the device-level fault location is realized by injecting the specific switching signals. Simulations and experiments are performed to verify the analysis of the current distortion. The result considering various conditions shows the proposed approach’s correctness and robustness. The proposed method is effective in the power factor ranging from 0 lag to 0 lead. The OC fault can be located within 6 ms.

Open-Circuit Fault-Tolerant Control for a Three-Phase Current-Fed Dual Active Bridge DC–DC Converter

Fault-tolerant methods have been recognized for improving the reliability of multiphase bidirectional dc–dc converters, including current-fed and voltage-fed dual active bridge converters. Fault-tolerant research for voltage-fed dual active bridge converters has caught much attention recently. In this article, a new fault-tolerant method for current-fed dual active bridge (CF-DAB) converters with blocking capacitors is proposed. The operation of the CF-DAB converter when an open-circuit fault occurs is comprehensively presented and analyzed. A frozen-phase fault-tolerant method with additional blocking capacitors on both sides of the transformer is proposed. The aim of the use of blocking capacitors is to block the dc current component of the high-frequency transformer, which could avoid the core saturation of the transformer. Also, the dc blocking capacitors help isolate the faulty phase when a frozen-phase fault-tolerant method is applied. The proposed method is shown to be a practical, simple, low loss, and effective solution for not only open-circuit fault isolation but also for shedding capability of the multiphase converter. The proposed concept could be applied for general multiphase current-fed converters. The experiment of a 6-kW prototype is implemented to confirm the proposed concept.

An Open-Circuit Fault Diagnosis Method for Four-Wire T-Type Three-Level Rectifier

The four-wire T-type three-level rectifier (four-wire 3LT2R) has a wide range of applications because of its excellent performance. However, the open-circuit fault of transistors can lead to power loss, current harmonics, and even system crashes. A novel open-circuit fault diagnosis method for four-wire 3LT2R based on the three-phase currents and modulation voltages is proposed in this article, considering power factor regulation. At first, the relationship between the power factor and the working interval of the transistor is deeply analyzed. Then, the diagnostic method is designed based on the analysis. The “zero current” in each phase current is used to detect the faults with the different features related to the size of working intervals. After the fault is detected, the fault transistor is located according to the polarities of the currents and the modulated voltages. No additional sensors are needed. Experiments are carried out, and the results show its high effectiveness, robustness, and applicability for different power factors.

Fault Tolerant Multi-Tx/Multi-Rx Inductive Power Transfer System With a Resonator Coil

This article presents a novel multi-transmitter (Tx)/multi-receiver (Rx) inductive power transfer (IPT) system. Compared with the conventional single-Tx/single-Rx systems, multi-Tx/multi-Rx topologies increase reliability and fault tolerance. However, unequal power distribution is challenging in these systems due to coupling differences, which prevents them from operating at rated power or requires overdesigned modules. This study proposes the addition of a middle-stage resonator (MSR) that balances power distribution by separating direct couplings between Tx and Rx coils. Thus, it increases reliability and fault tolerance. Direct couplings between Tx and Rx coils are avoided via the proposed coil structure, which also provides rotational symmetry. Moreover, an analytical method is proposed to avoid bifurcation, which reduces switching losses. Then, the fault tolerance analysis on multi-Tx/multi-Rx systems and optimum selection of module numbers are investigated. Finally, a 1-kW 2Tx/1MSR/4Rx prototype is established to continue operation under single-Tx, single-Rx, and double-Rx open-circuit faults.

Decoupled Fault-Tolerant Model Predictive Current Control for Dual Three-Phase PMSMs With Harmonic Compensation

This article proposes a decoupled fault-tolerant model predictive current control for a dual three-phase permanent magnet synchronous motor with harmonic compensation. The harmonics caused by nonsinusoidal back electromotive force can be well controlled under the open-circuit fault scenario by designing a harmonic closed-loop scheme. First, a decoupled predictive model and fault-tolerant voltage vectors are deduced based on a reduced-dimension matrix, which can eliminate the coupling issues between the harmonic and fundamental subspaces. Afterward, a uniform harmonic-free virtual vector is constructed by rearranging the irregular voltage vectors. More importantly, a virtual null vector is designed in the scheme, where the virtual null vector is effective in the harmonic subspace and has no components in the fundamental subspace. So, the harmonics can be further controlled without affecting torque and flux generation. Extensive experimental results verify the effectiveness of the proposed method.

A Real-Time Open-Circuit Fault Diagnosis Method Based on Hybrid Model Flux Observer for Voltage Source Inverter Fed Sensorless Vector Controlled Drives

This article presents a open-circuit fault diagnosis method for a sensorless vector control system based on the voltage–current hybrid model flux observer. The error voltage, which is obtained after a PI module, is used as a diagnostic factor. This PI module is designed to correct the deviation between the voltage model flux observer and the current model flux observer. The error voltage is not affected by load and speed variation. First, the activation function is used to separate fault information and normal information, and the feature function is defined to describe the signal feature in healthy and faulty conditions. Second, a second-order moving mean filtering method is proposed to reduce the storage of the error voltage data and increase the timeliness of diagnosis. Finally, the fault is diagnosed and located through logical calculations. Compared with conventional methods, this method features the timeliness of fault diagnosis in a closed-loop system and robustness with respect to speed and load changes. It reduces the storage and operation burden such that it can be easily implanted into the control algorithm as a subroutine. Simulations and experiments show the accuracy and superiority of the proposed fault diagnosis method for voltage source inverter fed sensorless vector controlled drives.

T-type inverter fault diagnosis based on GASF and improved AlexNet

Aiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis method is proposed, which is based on Gramian Angular Summation Field and improved AlexNet network. The fault results can be diagnosed by collecting only the single-phase line voltage. Firstly, the collected one-dimensional timing signal is mapped into two-dimensional images through the Gram summation angle field algorithm. Then feature extraction is performed by the improved AlexNet to take advantage of its ability to extract image features. Finally, the fault diagnosis result is output by the Softmax layer. Simulation experiments show that the method proposed in this paper can automatically extract features that are helpful for fault identification from raw data. The fault diagnosis rate of this model is as high as 99.72%, and it can diagnose not only a single fault, but also multiple faults in different phases. Compared with other methods, the proposed method shows a better fault feature extraction effect and higher fault diagnosis accuracy.

A Fault-Tolerant Bidirectional Converter for Battery Energy Storage Systems in DC Microgrids

Battery energy storage systems (BESSs) can control the power balance in DC microgrids through power injection or absorption. A BESS uses a bidirectional DC–DC converter to control the power flow to/from the grid. On the other hand, any fault occurrence in the power switches of the bidirectional converter may disturb the power balance and stability of the DC microgrid and, thus, the safe operation of the battery bank. This paper presents a fault-tolerant topology along with a fault diagnosis algorithm for a bidirectional DC–DC converter in a BESS. The proposed scheme can detect open circuit faults (OCFs) and reconfigure the topology to guarantee the safe and continuous operation of the system while it is connected to the DC microgrid. The proposed method can be extended to multi-phase structures of interleaved bidirectional DC–DC converters using only two power switches and n TRIACs to support the OCF occurrence on 2 × n switches of n legs. The proposed fault diagnosis algorithm detects OCFs only by observing the current of the inductors and does not require any sensor. Hence, the cost, weight, volume and complexity of the system is considerably reduced. Experimental results show that the reconfiguration of the converter, along with its fast fault detection, leads to fewer switches overloading and less DC voltage deviation.

Integration of reconfigurable fault-tolerant three-level inverter in photovoltaic power system

ABSTRACT A lot of previous studies have shown a great interest in connecting photovoltaic (PV) arrays to electric power grids. A Maximum Power Point Tracker (MPPT) controlled 100 kW PV array to boost its power that feeds the grid with the help of a three-level inverter. This paper investigated the effects of open-circuit faults of three-level inverter IGBT switches on the performance of the PV array system. This paper proposes a new diagnostic method called the Double Threshold-Trigonometric Coordinates (DT-TC) that relies on the double threshold technique and the trigonometric coordinates, respectively, for early detection and the precise location of open-circuit faults. To the best knowledge of the authors, two simultaneous open-circuit faults were not considered in the previous studies. The obtained simulation results prove the great success of the proposed diagnosis method even under noticeable changes in irradiation, temperature, and the number of PV array connected in series and in parallel. In this study, the reconfigurable fault-tolerant inverter was included to ensure the perfect continuity of the PV array system and its recovery in an extremely very short time up to 7 ms.

Inverter fault diagnosis based on Fourier transform and evolutionary neural network

The fault diagnosis of the inverter is fundamental to energy intelligence. Due to the complex characteristics of the inverter (e.g., high-dimensional decision and poor stability), it is challenging to solve the problem using traditional fault diagnosis methods. Recently, artificial intelligence (AI)-based approaches have emerged as the most promising methods. However, they often require to set hyperparameters manually, which hinders further AI-based applications in fault diagnosis of inverters. To fill the gap, we propose an inverter fault diagnosis method using fast Fourier Transform (FFT) and evolutionary neural network. This method combines the amplitude of low-frequency harmonic component of the three-phase inverter output current which is obtained by FFT and the average value in a period of three-phase inverter output current into the fault eigenvector. This method uses an evolutionary neural network trained by combining genetic algorithm (GA), ant colony optimization (ACO) algorithm and Back-propagation (BP) algorithm to realize fault diagnosis. This method can effectively resist noise interference and reduce the number of independent variables in the part of feature extraction, so that it can simplify the network model. In addition, this method can avoid the network training from trapping in local optima in the part of fault classification, with high accuracy and fast response speed. The experimental results show that the proposed algorithm and method of fault feature extraction can effectively detect and locate the insulated-gate bipolar transistor (IGBT) with open circuit (OC) fault in three-phase inverter, and can be applied to online monitoring.

Track and hunt metaheuristic based deep neural network based fault diagnosis model for the voltage source inverter under varying load conditions

Induction motors act as the pillars for industries and are preferable in various applications due to the characteristics of stability and sturdiness. The stability level should be efficiently maintained in order to monitor and analyze the faults that occur in three-phase Voltage Source Inverter. In this research, fault prediction occurs in the inverter under various load conditions is performed using the proposed track and hunt meta-heuristic-based deep neural network. The open circuit fault is generated in the three-phase voltage source inverter transistor under varying load conditions and is determined using the proposed track and hunt optimization. The proposed track and hunt meta-heuristic is developed by hybridizing the characteristics of hunting and tracking. After the data acquisition, the features are extracted from the collected faulty data in the form of three-phase current, voltage, torque, and speed. The Deep Neural Network classifier is trained using the obtained features and the faulty switches are identified through the average value of the three-phase current. The three-phase induction motor is connected as a load for the voltage source inverter with reference to the frequency variation. The prediction performance of track and hunt- deep learning classifier reveals that the percentage improvement of 10–15% is acquired for the developed method.

Frequency Response Analysis for Three-Phase Star and Delta Induction Motors: Pattern Recognition and Fault Analysis Using Statistical Indicators

This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification.

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.