Open-circuit Fault Research Articles

Novel data-driven open-circuit fault diagnosis method for modular multilevel converter submodules based on optimized deep learning

Novel data-driven open-circuit fault diagnosis method for modular multilevel converter submodules based on optimized deep learning

Open-Circuit Fault Diagnosis Strategy for Novel DFPMM Drive Based on Fuzzy Logic

Open-Circuit Fault Diagnosis Strategy for Novel DFPMM Drive Based on Fuzzy Logic

A Transferrable and Noise-Tolerant Data-Driven Method for Open-Circuit Fault Diagnosis of Multiple Inverters in a Microgrid

A Transferrable and Noise-Tolerant Data-Driven Method for Open-Circuit Fault Diagnosis of Multiple Inverters in a Microgrid

An open-circuit fault diagnosis method for PMSM drive system based on multi-branch 1D-CNN

An open-circuit fault diagnosis method for PMSM drive system based on multi-branch 1D-CNN

A Cascaded H-Bridge Multilevel Inverter with DC Cells Input Fault Tolerance Capability Based on PSC-PWM Control

Multilevel inverters have proven their efficiency in generating better AC voltage outputs. This functional quality is mainly due to the use of multi-source DC inputs. Despite the fact that this type of topology is generally reliable, switching faults caused by the complete loss of a switching component or DC input cell may still occur. Such incident may inflict heavy impacts to the conversion chain resulting in a permanent damage to the switching cells or the connected load. This article presents a dynamic switching control strategy capable of tolerating DC cells open-circuit input faults in basic symmetric Cascaded H-bridge multilevel inverter architectures. The proposed topology uses a control scheme to overcome the switching cells’ fault by adapting the configured level of conversion and bypassing the flawed unit with no additional switching components. Furthermore, the proposed control strategy is capable of reversing back the inverter configuration to its original functional state after the disappearance of the faut. This research article answers the need for a theorical study of a fault tolerant Cascaded H-Bridge multilevel inverter where the short circuit’s fault types are bypassed by Pulse width modulation alteration only.

A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors

As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy.

A diagnostic strategy for IGBT open-circuit faults in modular multilevel converters based on improved diagnostic indices

Capacitor voltage deviations between two submodules (SMs) are always used as the diagnostic index for the insulated gate bipolar transistor (IGBT) open-circuit fault diagnosis in modular multilevel converters (MMCs). However, it is difficult to determine when MMCs operate in different conditions. To solve this issue, this article proposes a diagnostic strategy for IGBT open-circuit faults in MMCs based on improved diagnostic indices. First, two diagnostic indices, i.e., the concept of the count of periods during which the switching function is fixed (CSF) and capacitor voltage sorting sequence number (CSN) are proposed. On this basis, fault detection is implemented by judging the changing trend of the CSN in the SM whose CSF is the highest. The remaining malfunctioning SMs are located in turns by a similar process to fault detection. The proposed diagnostic indices show consistent faulty characteristics under different operating conditions, thus avoiding readjusting the threshold according to different operating conditions. In this sense, the proposed strategy can be easily used and promoted in different operating scenarios. Experimental results in a hardware-in-the-loop (HIL) platform verify the effectiveness of the proposed strategy.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

Real-Time Diagnosis of Open Circuit Faults in Three-Phase Voltage Source Inverters

Real-Time Diagnosis of Open Circuit Faults in Three-Phase Voltage Source Inverters

Research on Fault-Tolerant Control Strategy of Open-End Winding Drive With a Common DC Bus Under a Switch Open-Circuit Fault

Research on Fault-Tolerant Control Strategy of Open-End Winding Drive With a Common DC Bus Under a Switch Open-Circuit Fault

Fault-Tolerant Control of Open-Circuit Faults in Standard PMSM Drives Considering Torque Ripple and Copper Loss

Fault-Tolerant Control of Open-Circuit Faults in Standard PMSM Drives Considering Torque Ripple and Copper Loss

Application of Digital Galvanometer Scanner System for CO2 Fractional Laser Safety Improvement

An analog PID controller-based galvanometer scanner is widely used by fractional laser medical equipment (FLME) to scan lasers across tissue surfaces, achieving the desired therapeutic effect. This type of driver, primarily composed of passive components and operational amplifiers, can only accept commands from the central controller of the FLME, with a simple hardware circuit-based fault diagnosis; thus, the safety of the FLME is compromised. To address these issues, the failure mechanisms of galvanometers and their impact on the safety of FLME are thoroughly analyzed first. Then, an adaptive limit protection method, a coil open circuit fault diagnosis, a communication timeout protection based on two handshakes, and a galvanometer control timeout protection are proposed, respectively, based on a digital driver platform, to supplement the deficiencies in the original fault diagnosis and protection system. This ensures the safety of the FLME. Finally, the effectiveness of the proposed strategies is validated through experiments.

Health Status Detection for Motor Drive Systems Based on Generalized-Layer-Added Principal Component Analysis

Health status detection for motor drive systems includes detecting the working status of the motor and diagnosing open-circuit (OC) faults in the inverter. This paper proposes a generalized-layer-added principle component analysis (GPCA) to determine the load-up/load-shedding status of a motor and diagnose faults in its inverter. Most current methods for detecting OC faults are constrained by changes in the current amplitude and frequency, potentially leading to misjudgments during load-up/load-shedding transient states. The proposed method addresses this issue. Initially, this paper employs a homogenization method to process current data, eliminating the impact of transient processes during motor load-up/load-shedding states on inverter fault diagnosis. Subsequently, the fast Fourier transform (FFT) is used to extract the frequency domain characteristics of the data. If the PCA method is trained with a singular matrix, this can lead to an unreliable result. This paper introduces a generalization layer based on the PCA method, leading to the GPCA method, which enables training with singular matrices. The GPCA method is then developed to compute data features. By presetting thresholds and utilizing the prediction error value and contribution rate index of the GPCA method, the relevant state of the motor drive system can be determined. Finally, through simulations and experiments, it has been demonstrated that the method, using data from the stable working state, can effectively detect the working status of a motor and diagnose OC faults in its inverter, with a diagnostic time of 0.05 current cycles.

Advanced detection and localization of open circuit faults in two-level three-phase IGBT-based inverters using machine learning approaches and discrete wavelet transform

Advanced detection and localization of open circuit faults in two-level three-phase IGBT-based inverters using machine learning approaches and discrete wavelet transform

Switch fault identification scheme based on machine learning algorithms for PV-Fed three-phase neutral point clamped inverter

Any faults in power electronic switches must be detected and located promptly to certify the unchanging operation and reliability of multilevel inverter (MLI) systems. Machine learning (ML) techniques are increasingly being utilized to diagnose faults in MLIs due to their advantages, such as high precision, less calculation time, and reduced complexity. A faulty switch identification and phase identification method based on different ML classifiers is presented in this paper, explicitly targeting open-circuit faults in switches in a PV-fed 3-phase three-level Neutral Point Clamped (NPC) inverter. The NPC MLI output voltage and current signals are input signals to classify faults. The information or essential feature from the raw voltage and current data is extracted using a recursive discrete Fourier (RDF) followed by a discrete wavelet transform (DWT). Finally, the standard deviations of the approximate and detailed coefficients are used as input for ML algorithms. To do the comparative analysis, a variety of ML techniques are then applied to these features, including logistic regression (LR), random forest (RF), quadratic discriminate analysis (QDA), K-nearest neighbour (K-NN), and support vector machines (SVM). The experiment results indicate that the RF algorithms achieve the highest classification accuracy of 99.59 % for various switch fault conditions.

Voltage Error Estimation-Based Fault Diagnosis Method for Three- phase PWM-inverter in Grid-connected Photovoltaic Power System Without any Over-rating

Inverter is an essential component of a grid connected PV system. In fact, an open-switch fault in this power converter could result an important system malfunction and consequently leads to system disconnection. In this context, this paper focuses on the fault-tolerant control of PWM-inverter with redundancy leg for grid-connected PV system. In addition, a fast method of fault detection and compensation is used to maintain the continuous operation of the converter when there is a half-leg open-circuit fault (IGBT+ anti-parallel diode) on one of PWM-inverter legs. Therefore, it can detect this type of fault and compensate it in less than 50 μs by using a time criterion instead of voltage criterion. Also, the fuzzy logic technique is employed to control the active and reactive power injected into the network without exceeding the whole system power capacity limits. Note that, in this study, the active and reactive power references of the chosen operating point (the reactive-to-active power ratio: tgφ = Q/P = 0.19) are based on experimental tests. Simulation results show the feasibility and the effectiveness of the proposed fault diagnosis method in terms of fault detection and service continuity in presence of a half-leg open-circuit fault in PWM-inverter without any over-rating of the PV system components or instability.

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

This research proposes a strategy to diagnose open-phase faults (OPF) and open-switching faults (OSF) in dual three-phase permanent magnet synchronous motor (DTP-PMSM) inverters. The method is based on the dual d–q predictive current model and involves establishing a mathematical model and utilizing the finite control set model predictive current extraction technique to predict the motor current. It then analyzes the characteristics of the switching-tube current under both normal and fault conditions. Finally, a fault predictive current model is introduced and the residual is calculated based on the predicted fault current value and the actual measured current value to diagnose the inverter fault. The proposed method effectively overcomes misjudgment issues encountered in traditional open-circuit fault diagnosis of inverters. It enhances the system’s response speed during dynamic processes and strengthens the robustness of diagnosis algorithm parameters. The experimental results demonstrate that the proposed method can rapidly, effectively, and accurately diagnose open-circuit faults presented in this paper fastest within one-fifth of a current cycle. It achieves a diagnostic accuracy rate of 97% in the dual three-phase permanent magnet synchronous motor drive system.

Diagnosis of Open-Circuit Faults in DC-Field Excited Variable Flux Reluctance Machine Utilizing Armature and Field Currents

More electric aircraft has high reliability requirements, and the DC-field excited modular variable flux reluctance machine (MVFRM) is its key component. The MVFRM has both armature and field windings, in which AC (alternating currents) and DC (direct currents) are supplied. Hence, traditional fault diagnosis is not applicable for MVFRM. In this paper, the diagnostic methodology for open-circuit faults in both windings is proposed by utilizing armature currents and field currents. Based on the internal relationship between the armature currents with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axis currents and the field currents, the characteristics and diagnosis mechanisms of armature winding and field winding open-circuit faults are analyzed. The per-unit threshold setting method is adopted to improve the rapidity of the algorithm without reducing robustness. The rapidity and effectiveness of the fault diagnosis algorithm are verified by different types of open-circuit fault diagnosis experiments. Furthermore, the robustness of the fault diagnosis algorithm is verified by the variable speed and different loads of open-circuit fault diagnosis experiments.

A Simple Online Fault Location and Tolerant Control Strategy for Power Electronic Transformers With a Single IGBT Open-Circuit Fault

A Simple Online Fault Location and Tolerant Control Strategy for Power Electronic Transformers With a Single IGBT Open-Circuit Fault

Real-Time Diagnosis Based on Signal Convolution-Pooling Processing and Shared Filter Learning for Transistor Open-Circuit Faults in a T-Type Inverter

Real-Time Diagnosis Based on Signal Convolution-Pooling Processing and Shared Filter Learning for Transistor Open-Circuit Faults in a T-Type Inverter