Short Circuit Fault Diagnosis Research Articles

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

Multi-granularity short circuit fault diagnosis for tokamak power converters under small sample conditions and noise interference

The power supply system of a tokamak device is a crucial subsystem responsible for providing the magnetic field for plasma confinement. The timely application of fault diagnosis techniques to identify abnormal states of the power supply is of crucial importance for the safe and efficient operation of tokamak devices. Intelligent diagnostic methods based on Convolutional Neural Networks (CNN) are widely employed nowadays. However, within the operational environment of the device, there exist multiple physical field couplings, and inevitable noise will impact the data. Moreover, the measurement equipment exhibits instability, resulting in limited availability of data that meets the required criteria. To address these issues, this paper investigates a fault diagnosis approach utilizing an Auxiliary Classifier Generative Adversarial Network (ACGAN) for data augmentation, and proposes a Multi-Granularity Denoising CNN (MGDCNN) that accounts for noise interference, enabling diagnosis of faults at different granularities. In our approach, we first construct a multi-granularity structure of the data based on prior knowledge. Then, a hierarchical structure corresponding to the multi-granularity data and robust to noise is built by introducing an embedded Discrete Wavelet Transform (DWT) into the one-dimensional (1-D) CNN. Finally, the natural stratification of the network is combined with individual granularity branches for fault diagnosis. Experimental results on simulated datasets and public benchmark datasets demonstrate that the proposed model achieves a diagnostic accuracy of 98.81 % for short-circuit faults in power supply converter at the finest granularity. Even with noisy data, the model maintains a diagnostic accuracy of approximately 97 %, indicating superior noise resistance performance in the inference process of the MGDCNN network.

A battery internal short circuit fault diagnosis method based on incremental capacity curves

The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%.

Fuzzy-Kalman-Filter-Based Short-Circuit Fault Diagnosis Design for Lithium-Ion Battery

Fuzzy-Kalman-Filter-Based Short-Circuit Fault Diagnosis Design for Lithium-Ion Battery

Fault Diagnosis Method of Inter-Turn Short Circuit of Permanent Magnet Synchronous Motor Based on Third Harmonic

In order to realize the effective diagnosis of turn to turn short circuit fault of permanent magnet synchronous motor, a method of turn to turn short circuit fault diagnosis of permanent magnet synchronous motor based on third harmonic is proposed in this paper. Establish the system frequency oscillation analysis model of permanent magnet synchronous motor under the condition of turn to turn short circuit, collect the turn to turn harmonic of permanent magnet synchronous motor, then realize the third harmonic detection of permanent magnet synchronous motor under turn to turn short circuit fault, and detect the inertia of permanent magnet synchronous motor system. Based on the transient energy analysis and fault feature extraction, the turn to turn short circuit fault of permanent magnet synchronous motor is diagnosed. The test results show that this method has high reliability and good detection performance, and can improve the output stability and condition monitoring ability of permanent magnet synchronous motor.

A Novel Hybrid Optical Imaging Sensor for Early Stage Short-Circuit Fault Diagnosis in Printed Circuit Boards

A Novel Hybrid Optical Imaging Sensor for Early Stage Short-Circuit Fault Diagnosis in Printed Circuit Boards

Automatic Diagnosis Method for Short Circuit Faults in Power Measurement Instruments Based on CNN

The accuracy of short-circuit fault diagnosis methods for power measuring instruments is still low, which can directly lead to abnormal power data statistics. To address this issue, a convolutional neural network is used to construct an automatic diagnosis model for short circuit faults in power measuring instruments. The model utilizes wavelet packet energy spectrum to extract crucial features from the signal. The model identifies and determines the fault type based on the obtained operation characteristic data and the set short-circuit fault diagnosis criteria. The Sparrow optimization algorithm is used to optimize the model's parameters and enhance its performance. Experimental analysis revealed that after the signal features were extracted by using the wavelet energy spectrum distribution, the error value fluctuated in the range of 0.00 ~ 0.04 with regard to the measured value and was mainly around 0.02. The extracted features achieved high accuracy levels. The designed model exhibited an average diagnostic accuracy of 97.03%, surpassing the other three models by 9.24%, 7.10%, and 4.25%. The presented model can improve the precision and productivity of fault detection, support the safety and reliability of power system operations, and facilitate the collection and analysis of power usage data

Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing

In recent years, internal short circuits have frequently been the cause of safety accidents, and traditional diagnosis methods for such problem have limitations that prevent their application under complex working conditions. Furthermore, due to the scarcity of real internal short circuit data, it is difficult to obtain a large amount of real vehicle data, which makes internal short circuit fault diagnosis even more challenging. In this paper, fault batteries with unknown parameters are diagnosed using a residual network based on multi-label processed battery data and transfer learning is utilized to optimize the diagnosis effect for fault localization and fault degree identification, thus increasing the information richness of the training model samples and improving the training effect. Accuracy, recall, and false positive rate are used to evaluate the diagnosis results of the residual network. The results demonstrate that the accuracy rate increased by 11.1% after transfer learning, indicating a significant reduction in misdiagnoses. The proposed fault diagnosis method using residual network and transfer learning has practical significance for enhancing battery fault detection accuracy and efficiency. Especially in areas such as electric vehicles and energy storage systems, it can provide effective technical support for battery health monitoring and early warning.

Diagnosis of inter-turn short circuit fault in IPMSMs based on the combined use of greedytracking and random forest

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm. The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.

Diagnosis and location of inter-turn short circuit fault in pumped storage unit

In order to monitor the inter-turn insulation health of the rotor windings of pumped storage unit (PSU) in real time, a fault diagnosis method based on magnetic field detection is proposed in this study. Firstly, the characteristic harmonics of the air-gap flux density (AGFD) corresponding to the different states are obtained by deriving the expressions for the AGFD in the normal and inter-turn short circuit (ITSC) states of the rotor winding. Secondly, the detection principle of Hall sensor and its application are studied, and an intelligent detection theory combining frequency-resolved spectrum (FRS) and radar chart is proposed. Thirdly, constructing a electromagnetic model based on the actual operating PSU size, then the time/frequency characteristics of the Hall induced electromotive force (HIEF) are analysed, from which the intelligent diagnostic spectrums are constructed for fault detection. Finally, the Hall probe detection sensitivity is calibrated to 1 mV/mT. By installing Hall probe on the slot wedge surface of the generator stator, HIEF signals for different ITSC levels are acquired and integrated with pole positioning signals for condition monitoring, which can effectively identify and locate ITSC fault, while the detection sensor has significant advantages in terms of size and installation simplicity.

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM

Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.

Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval

Accurate health diagnostics of lithium-ion batteries are indispensable for efficient utilization. A decrease in battery capacity not only diminishes the energy efficiency but also causes several detrimental effects, such as an internal short circuit (ISC) fault; these fault can lead to thermal runaway. However, the simultaneous impact of aging and ISC faults complicates the ability to distinguish between two factors within a singular discharging or charging process. This study focuses on the co-diagnosis of battery capacity and ISC faults, emphasizing that the amount of leakage current attributable to an ISC fault remains consistent at intervals where the average voltage is identical during discharging and charging procedures. To perform this analysis, different equivalent circuit models and parameter tables are employed for the discharging and charging processes separately. Subsequently, the ISC fault and capacity are estimated using the estimated state of charge (SOC) of intervals. The practicality of the proposed method is evaluated through experiments under various dynamic profiles, ISC fault severities, aging degrees, and temperature conditions. The results substantiate the proposed method’s efficacy in co-diagnosing the capacity and ISC faults. Therefore, this method represents an advancement in the ongoing efforts to ensure the safe and effective deployment of lithium-ion batteries.

Aging-robust and Current Sensor-free Soft Short Circuit Fault Diagnosis for Lithium-ion Batteries

Aging-robust and Current Sensor-free Soft Short Circuit Fault Diagnosis for Lithium-ion Batteries

ResNet-18 based Inter-turn Short Circuit Fault Diagnosis of PMSMs with Consideration of Speed and Current Loop Bandwidths

ResNet-18 based Inter-turn Short Circuit Fault Diagnosis of PMSMs with Consideration of Speed and Current Loop Bandwidths

Soft Short-Circuit Fault Diagnosis for Vehicular Battery Packs With Interpretable Full-Dimensional Statistical Analytics

Soft Short-Circuit Fault Diagnosis for Vehicular Battery Packs With Interpretable Full-Dimensional Statistical Analytics

Quantitative diagnosis of micro-short circuit for lithium-ion batteries considering aging based on incremental capacity curve

Early diagnosis of internal short-circuit faults for preventing thermal runaway in lithium-ion batteries stands as a pivotal task within battery management systems. With the continuous use of electric vehicles, the coexistence of aging and micro-short circuit (MSC) within battery packs is possible. These two faults manifest identical characteristics during discharge, rendering them susceptible to erroneous diagnoses. In this study, a novel fault diagnosis method based on the incremental capacity (IC) curves is proposed, devised to discern aging cells from those afflicted by MSC. This method screens out aging cells by feature parameters of IC curve. Based on the unique correspondence between IC peak and state of charge in this study, the cell that reaches the peak first is designated as the reference cell. By calculating the leakage current based on the temporal divergence of peak arrivals of each cell from this reference, this method estimates the MSC resistance to quantify the degree of the short-circuit fault. A series of experiments involving the presence of aging batteries, simultaneous occurrence of multiple MSC faults, or the evolution of MSC severity indicate the method is feasible and effective for diagnosing aging and MSC faults in battery packs simultaneously.

Diagnostic Method for Short Circuit Faults at the Generator End of Ship Power Systems Based on MWDN and Deep-Gated RNN-FCN

Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit faults at the generator terminal in the ship’s power system to ensure the safe and stable operation of modern ships. In this study, a generator terminal short-circuit fault diagnosis method is proposed based on a hybrid model that combines the Multi-Level Wavelet Decomposition Network, Deep-Gated Recurrent Neural Network, and Fully Convolutional Network. Firstly, the Multi-Level Wavelet Decomposition Network is used to decompose and denoise the collected electrical signals, thus dividing them into sub-signals and extracting their time-domain and frequency-domain features. Secondly, synthetic oversampling based on Gaussian random variables is employed to address the problem of imbalance between normal data and fault data, resulting in a balanced dataset. Finally, the dataset is fed into the hybrid model of the Deep-Gated Recurrent Neural Network and Fully Convolutional Network for feature extraction and classification of faults, ultimately outputting the fault diagnosis results. To validate the performance of the proposed method, simulations and comparative analysis with other algorithms are conducted on the fault diagnosis method. The proposed algorithm’s accuracy reaches 96.82%, precision reaches 97.35%, and the area under curve reaches 0.85, indicating accurate feature extraction and classification for identifying short-circuit faults at the generator terminals.

Online Short-Circuit Fault Diagnosis in Three-Core Power Distribution Cable Based on Magnetic Pattern

Online Short-Circuit Fault Diagnosis in Three-Core Power Distribution Cable Based on Magnetic Pattern

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning