Open-circuit Fault Research Articles

Deep learning techniques for transmission line fault classification – A comparative study

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

Robust Open-Circuit Fault Diagnosis for PMSM Drives Using Wavelet Convolutional Neural Network With Small Samples of Normalized Current Vector Trajectory Graph

Open-circuit fault is one of the most common faults in permanent magnet synchronous machine (PMSM) drives. The open-circuit fault can cause the obvious change of stator currents of PMSM. Hence, the previous artificial intelligence based-fault diagnosis method mainly relies on the samples extracted from stator currents. However, the large sets of the samples are required due to the variation of the PMSM operating point, increasing the complexity of fault diagnosis. What's more, stator currents are easily affected by the noise, decreasing the accuracy of fault diagnosis. To solve the issues, this paper proposes a robust open-circuit fault diagnosis method using wavelet convolutional neural network with small samples of normalized current vector trajectory graph. The proposed method uses current normalization to establish small sample sets and combines convolutional neural network with discrete wavelet transform to enhance robustness to noise. The proposed fault diagnosis method is validated by simulation and experiment. Both the results show that the proposed method can effectively diagnose 22 kinds of open-circuit fault types (including healthy mode), being with great antinoise ability and robustness to different working conditions.

A Fault-Tolerant LLC Converter With High Reliability and Low Cost for Two-Stage Converters

The popularity of electric vehicles is getting higher, and a large number of DC-DC converters are deployed. LLC converters have witnessed a rapid deployment due to their remarkable merits, often employed as the isolation stage in two-stage converters. However, the contradiction between the increasing demand for reliability and the limited space is becoming prominent. Therefore, ensuring fault tolerance capacity with fewer devices is highly desired. A fault-tolerant LLC converter, which can keep working after multiple unexpected faults, is designed to take over half-bridge LLC converters. The pre-fault converter behaves like a full-bridge LLC converter and would be reconstructed to retain uninterrupted power output after faults. The reconstructed converter after the first fault behaves like two parallel half-bridge LLC converters and would be further reconstructed into a single half-bridge converter after the second fault. The faults can both be handled, including short-circuit faults and open-circuit faults. The transition processes of reconstruction are extremely short. The spike current during the transition processes is well limited, and the output voltage has slight fluctuation. The proposed converter could drastically reduce the need for redundancy and has obvious cost advantages. Finally, a prototype is developed to verify the superiority of the proposed converter.

Compatible Phase Current Reconstruction Scheme for Fault-Tolerant Five-Leg Dual-Inverter Fed Open-Winding Permanent Magnet Synchronous Motor

In this paper, a novel phase current reconstruction strategy for a Five-Leg Dual-Inverter (FL-DI) fed Open-Winding Permanent Magnet Synchronous Motor (OW-PMSM) drive is put forward. Benefiting from the doubling of the power legs, the OW-PMSM drive is endowed with outstanding fault-tolerant ability. When an open-circuit fault occurs, the OW-PMSM automatically turns to the post-fault Two-Phase (2-ph) operation. By reconfiguring the wiring method, the fault-tolerant FL-DI topology is obtained, and the post-fault Three-Phase (3-ph) operation is realized. However, the current flow path changes and measurement vectors become unavailable in post-fault operation processes, making the existing current reconstruction schemes unusable. To prevent the system collapse caused by the current reconstruction failure, a novel current reconstruction strategy is proposed, which can be applied in the case of the open-circuit fault in any leg. In addition, the proposed current reconstruction strategy is compatible with the specific Zero-Sequence Current (ZSC) suppression for the FI-DI fed OW-PMSM drive, which ensures better system performance in the post-fault operation. The reconstructed current is of high accuracy, where the relative reconstruction error is 7.8% at low load and 5.3% at heavy load. Finally, experimental results are presented to verify the feasibility and effectiveness of the proposed strategy.

A Fast Diagnosis Strategy for Inverter Open-Circuit Faults Based on the Current Path of Brushless DC Motors

The proposed method in this paper aims to diagnose open-circuit faults quickly and accurately in brushless DC motor inverters to ensure the motors' safe operation. The technique utilizes a topology for current path detection and a method based on current path change to identify and locate faults in the inverter. The proposed method improves the speed of fault diagnosis and is not affected by sudden changes in load. Additionally, it allows for prompt handling of faults and ensures reliable motor operation. The effectiveness of this method is validated through experimental results.

Experimental investigation of a real-time singularity-based fault diagnosis method for five-phase PMSG-based tidal current applications

This paper presents a novel real-time singularity-based fault diagnosis method for tidal current applications, specifically utilizing a five-phase permanent magnet synchronous generator with trapezoidal back electromotive forces. The proposed method incorporates an innovative orthogonal signal generator through a second-order filter, enabling the extraction of detectable singularity signatures from phase current signals. The principle of the method is elucidated through step-by-step design procedures, outlining the indicator enhancement approach and adaptive thresholds employed for enhanced robustness and adaptability. Fault detection is performed based on the improved fault indicators and an adaptive threshold law, followed by immediate fault localization that is achieved via twice average operations of the phase currents. To demonstrate the effectiveness and efficiency of the proposed method, a comparative study is carried out with a classical mean current vector-based fault diagnosis method. A small-scale experimental platform emulating a tidal current application is established for a comprehensive evaluation of both methods. The experimental results highlight the superior fault diagnosis performance of the proposed method, particularly in detecting single and multiple open circuit faults in phases or switches, while exhibiting enhanced robustness against variations in torque and speed. The simplicity of implementation and rapid detection mechanism are principal merits for the proposed method.

A Comprehensive Method for Online Switch Fault Diagnosis and Capacitor Condition Monitoring of Three-Level T-Type Inverters

Capacitors and semiconductor power devices are the most fragile components in power electronic systems. Hence, designing a comprehensive method for online switch fault diagnosis (FD) and capacitor condition monitoring (CM) is conducive to guaranteeing the reliability of the three-level T-type (3L-T) inverter in an all-around way. First, based on modeling the 3L-T inverter on the dc link and ac side, the inherent harmonic components of the dc link under normal and open-circuit faults conditions are analyzed. Furthermore, the capacitor current reconstruction method is designed. Subsequently, a comprehensive FD and CM framework is proposed, which consists of signal injection, extra hardware, 3L-T model, FD, and CM submodule. In FD, the detection variable, which is the function of output current and its changing rate, is designed, and the fault location is carried out based on the phase current residual and the sum of the neutral-point voltage residual. The capacitance estimation method is introduced based on the input current reconstruction technology and recursive least squares algorithm using the inherent or injected ac component on the dc link. Finally, experimental results demonstrate that the proposed comprehensive method merits high estimation accuracy, fast dynamic response, and better FD performance under various conditions.

Open-Circuit Fault Diagnosis for Permanent Magnet Synchronous Motor Drives Based on Voltage Residual Analysis

With the widespread application of permanent magnet synchronous motor (PMSM) drives, the demand for safety and reliability has increased. Inverter open-circuit fault is a frequent and severe failure that requires reliable detection and remedial measures. This paper proposes an open-circuit fault diagnosis technique based on voltage residual analysis that does not require additional voltage sensors. The proposed technique analyzes the characteristics of the reference voltage and the actual output voltage under both healthy and fault conditions. To achieve fast and reliable fault detection, it uses the DC component and second harmonic component in the residual voltage between estimated voltage and controller reference voltage. Once an open-circuit fault is detected, the polarity of the DC component is capable of identifying the fault location. The effectiveness and robustness of the proposed technique are verified through simulations and testing on a triple redundant PMSM under various load and speed conditions.

Decoupled Speed and Flux Control of a Three-Phase Permanent Magnet Synchronous Motor under an Open-Circuit Fault Using a PR Current Controller

Presented in this article is a method for decoupling the speed and flux control of a three-phase permanent magnet synchronous motor (PMSM) during an open-circuit fault (OCF) using a current controller based on proportional resonant (PR) control techniques. The suggested control approach is relatively simple to implement and maintains good motor operation during the open circuit. PMSM performance under pre-fault, OCF, and post-fault conditions were investigated using Matlab/Simulink and an experimental setup based on a dSPACE DS1104. The conducted investigations provide strong evidence supporting the efficacy and resilience of the suggested control approach during OCF conditions.

Open-Winding Permanent Magnet Synchronous Generator for Renewable Energy—A Review

The open-winding permanent magnet synchronous machines (OW-PMSMs) have recently been gaining more attention because of their fault-tolerant capability and power quality comparable to a 3-level converter-driven system. This paper reviews the common configurations of OW-PMSM when used as a generator, highlighting its shortcomings and benefits. The OW-PMSM with a common DC bus was found to be a promising direct-drive generator solution for wind energy conversion (WEC) systems considering fault tolerance, DC bus utilization, and power quality when appropriate control algorithms are in place. The presence of the zero-sequence current is the key disadvantage of the common DC bus configuration. The review highlights the algorithms that have been proposed to suppress the zero-sequence current of the OW-PMSM under healthy and various fault conditions, especially the open-circuit fault of semiconductor switch. Shutting down remotely located wind turbines because of faults, until they can be repaired, may not make economic sense. The OW-PMSM can offer the opportunity, to run a WEC system even under fault conditions albeit with low output power. This paper will assess the literature gaps in the existing control techniques that prevent the extension via a comprehensive review.

Real-Time Open-Circuit Fault Diagnosis Method for T-Type Rectifiers Based on Median Current Analysis

T-type rectifiers are widely used in DC fast chargers, super uninterrupted power supplies, renewable energy, and other low-voltage and medium-voltage rectifier applications. High equipment reliability and high-quality power are often required in these applications but are damaged by the transistor open-circuit fault. This paper proposes a novel real-time open-circuit fault diagnosis method for the T-type rectifier based on median current analysis. The method only uses the three-phase currents to obtain the median current and realize the diagnosis. In the rectifier, partial open-circuit faults result in weak fault signals, making the diagnosis challenging. To solve this, the median current obtained by the currents performs fault frequency modulation to enhance the fault signals at first. Then, the ratio of the fundamental frequency to the dc component is used to realize fault detection. The inner/outer fault is identified at the same time. At last, the fault transistor is located by simple change point detection. The change points determine the fault phase and transistor fault. The effectiveness and robustness of the proposed method are verified by the experiment results.

Electrical Signature Analysis for Open-Circuit Faults Detection of Inverter With Various Disturbances in Distribution Grid

This paper proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

Implementation and reliability analysis of a new non‐isolated quadratic buck–boost converter using improved Markov modelling

AbstractHere, a new buck–boost converter with reliability analysis is proposed. The converter has utilized a new voltage multiplier cell at the input side to boost the output voltage. It has a continuous input current with a wide range of duty cycle operations. The converter achieves unity gain at 26.8% duty cycle and can also operate in continuous and discontinuous conduction modes in buck and boost modes. The detailed reliability analysis of the proposed converter using improved Markov modelling is also presented by considering both open circuit and short circuit faults across the semiconductor devices. The variation of reliability and the mean time to failure (MTTF) with different converter parameters like the duty cycle and the input voltage is also discussed here. The hardware results of the developed prototype converter are also presented here at varying duty ratios. The power loss analysis and comparison of the converter with other newly proposed buck–boost topologies show the advantages of the proposed converter. For checking the stability of the converter, the state space model of the converter is also discussed in detail of the proposed converter.

Reliability Improvement by Fault-Tolerant Operation of NPC Inverter for Motor Driving

The high-reliability operation of three-level inverters is crucial to prevent equipment damage, process downtime, and economic losses. This paper investigates a three-level neutral-point-clamped inverter under all possible combinations of open-circuit and short-circuit faults and proposes a new postfault operation method without adding extra hardware. This method provides comprehensive solution for operations after single and multiple-device failures, increasing the inverter reliability by 24%. This paper classified post-fault modulations and uncovered previously unknown fault scenarios that can be addressed using the proposed control method. A new postfault modulation based on space vector modulation with virtual vectors is proposed. The feasibility of the proposed control method is verified by simulation and an experiment for one fault scenario in a three-level neutral-point-clamped inverter with a 3.73 kW motor load. This paper contributes to improving the reliability of three-level inverters.

Induction Machine Emulation for Variable Frequency Drive Converter Faults

Voltage in current out (VICO) models are commonly used for induction machine (IM) emulation. This model does not provide the information of back-emf and hence cannot be used for open circuit fault emulation. A current in voltage out model (CIVO) is an alternate IM model where it provides back-emf information. However, the CIVO model cannot be used directly with the conventional emulator configuration. Hence, in this paper, a new emulator configuration has been proposed to accommodate the CIVO model. The model is used in a new emulator configuration by incorporating physical emulator parameters. The paper further discusses the upgrade of the model considering saturation nonlinearities. The emulator employing such a model mimics the real induction machine back-emf with high accuracy. The proposed emulator is tested for different gate driver faults such as single switch and multiple switch open faults, starting and speed changing conditions. The experimental results are validated with a 5 hp machine. Different fault signatures are also identified with the help of the emulator to help with fault diagnosis and localization.

Matlab sensitivity analysis toolbox: an application on faults identification in induction motors

PurposeThis paper aims to introduce the usage of sensitivity analysis (SA) for the problem of faults identification in three-phase induction motors (IMs). These motors are susceptible to different kinds of faults that should be detected in a proper time to keep the systems working in a safety environment.Design/methodology/approachOne of the effective approaches for faults identifications, which is presented in the literature, is a model-based strategy. This strategy mainly depends on using a software model to make an identification decision. Therefore, this work intends to examine the model sensitivity towards variables’ variation. The SA toolbox of Matlab R2017b package is used for this purpose since the Matlab software is a well-known environment, and it is easy for a nonstatistical person to deal with it. As a study case, open-circuit and stator inter-turn faults in the stator windings of a three-phase IM have been chosen.FindingsThe results show that the model-based strategy is considerably speed up by up to 30% when neglecting the trivial model’s parameters with the same accurate identification decision as compared with the results of this strategy without using the SA.Originality/valueThe novelty of this work is summarized in devoting the usage of SA in the field of faults identification to enhance the speed of final decision.

A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters

With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate flawlessly to avoid energy and financial losses. As the failure of semiconductor switches is the leading cause of abnormal operation of PV inverters and typically cannot be detected by internal protection circuits, this paper aims to develop a method for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected PV inverters. In this study, a ReliefF-mRMR-based multi-domain feature selection method is designed to ensure the completeness of the fault characteristics. An NGO-HKELM-based classification method is proposed to guarantee the desired balance between generalization and exploration capability. The proposed method overcomes the common problems of poor training efficiency and imbalances between generalization and exploration capabilities. The performance of the proposed method is verified with the detection of switch OC faults in a three-phase H-bridge inverter and neutral-point-clamped inverter, with diagnostic accuracy of 100% and 99.46% respectively.

Evaluation of hypervelocity impact of micrometeoroids and orbital debris on next-generation space solar cells

This paper discusses the hypervelocity impacts of micrometeoroids and orbital debris (MMODs) on inverted metamorphic triple-junction (IMM3J) and perovskite solar cells, which are much thinner than conventional triple-junction (3J) solar cells. We experimentally found that IMM3J solar cells can suffer from short-circuit faults due to the hypervelocity impacts of MMODs, unlike conventional 3J cells, and determined the projectile diameters and velocities that could cause them using a model proposed by Burt. No short-circuit mode was identified in perovskite solar cells, but they had open-circuit faults several days after the hypervelocity impact experiment, which are possibly attributed to the decomposition of the perovskite crystal by moisture in the air due to the broken seal.

Currents Analysis of a Brushless Motor with Inverter Faults—Part II: Diagnostic Method for Open-Circuit Fault Isolation

In this paper, a brushless motor with a three-phase inverter is investigated under healthy and multiple open-circuit faults. The occurrence of faults in an inverter will lead to atypical characteristics in the current measurements. This is why many usual entropies and multiscale entropies have been proposed to evaluate the complexity of the output currents by quantifying such dynamic changes. Among this multitude of entropies, only some are able to differentiate between healthy and faulty open-circuit conditions. In addition, another selection is made between these entropies in order to improve diagnostic speed. After the fault detection based on the mean values, the open-circuit faults are localized based on the fault diagnostic method. The simulation results ensure the ability of these entropies to detect and locate open-circuit faults. Moreover, they are able to achieve fault diagnostics for a single switch, double switches, three switches, and even four switches. The diagnostic time to detect and to isolate faults is between 10.85 ms and 13.67 ms. Then, in order to prove the ability of the fault diagnostic method, a load variation is performed under the rated speed conditions of the brushless motor. The validity of the method is analyzed under different speed values for a constant torque. Finally, the fault diagnostic method is independent from power levels.

An Open-Circuit Faults Diagnosis Method for MMC Based on Extreme Gradient Boosting

The diagnosis of open circuit faults is required for the reliability of modular multilevel converters (MMC) and the complex operating environment of MMC may cause missing data and external noise in signal detections, where some of the present open circuit fault detection methods can be disabled. This paper proposed a machine learning (ML) diagnosis strategy for open circuit faults in MMC based on extreme gradient boosting (XG-Boost). In this method, after data processing, the data segments composed of real-time capacitance voltage and current data are input into the trained XG-Boost multi-classification model for fault diagnosis without manually setting the empirical threshold, as is required in traditional change-of-rate-of-voltage-based diagnosis methods. This method has excellent robustness against external noise and missing data while better accuracy, higher speed and lower real-time calculation cost than existing ML-based methods are achieved. The effectiveness of the proposed method is verified by experiment results. It can diagnose the fault within 20ms with accuracy as high as 99.6%, even under the interference of Gaussian (GS) noise and the capacitor voltage data missing.