Electrical Faults Research Articles

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Electric Motor Bearing Fault Noise Detection via Mel-Spectrum-Based Contrastive Self-Supervised Transformer Model

Electric Motor Bearing Fault Noise Detection via Mel-Spectrum-Based Contrastive Self-Supervised Transformer Model

Multi-Task Learning-Based Method for Load Identification, Electrical Fault Detection and Signal Denoising

Multi-Task Learning-Based Method for Load Identification, Electrical Fault Detection and Signal Denoising

Recognition of partial discharge in GIS based on image feature fusion

<p>Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.</p>

Short Circuit Fault Detection in Permanent Magnet Synchronous Motor Based-on Group Model of Data Handling Deep Neural Network

Short circuit fault (SCF) in stator coils is one of the most common types of electrical faults. The expansion of this fault leads to the permanent demagnetization of the magnet, and causes irreparable damage to the machine in a short period. With the development of artificial intelligence technologies and various machine learning and deep learning techniques, an increase in fault detection accuracy has been achieved. In this paper, permanent magnet synchronous motor (PMSM) is investigated under normal mode and fault conditions, namely SCF in winding loops, phase to phase SCF and open circuit fault of one of the phases. Group Model of Data Handling deep neural network (GMDH-DNN) is used to produce a SCF detection model. Results of simulating the proposed method and the data extracted from the PMSM reveals that the accuracy rate of SCF detection in the winding loops of the PMSM in the proposed method is equal to 99.2%, which constitutes an improvement of 1.7% compared to other existing methods such as conditional generative adversarial network (CGAN). Moreover, simulating other existing methods - namely support vector machine (SVM), k nearest neighbors (KNN), C4.5, multi-layer perceptron (MLP), recursive deep neural network (RDNN) and long short-term memory networks (LSTM) – and comparing them with the proposed method, unveil that the accuracy of the proposed method for SCF detection in winding loops overweigh those of aforesaid existing methods.

Design of a Testing Tool Based on Fault Injection for Functional Safety

ISO 26262 provides testing requirements for functional safety development and testing to mitigate unacceptable risks arising from system functional failures. Fault injection plays a vital role in assessing system robustness and validating the efficacy of safety mechanisms. This paper explores the fault types and implementation process of fault injection in the context of functional safety confirmation and validation. A fault injection board specifically designed for electrical faults is developed and integrated with Controller Area Network (CAN) messages to verify the effectiveness and correctness of safety mechanisms. The proposed approach offers the advantages of flexible switching and ease of control, making it a valuable tool in ensuring the functional safety of automotive systems.

Early-Stage Electrical Fault Identification for Traction Transformers Using Vibration Signals Based on Dual-Attention Convolutional Network

Early-Stage Electrical Fault Identification for Traction Transformers Using Vibration Signals Based on Dual-Attention Convolutional Network

Algorithm development for analyzing transient stability in Matlab

The electrical energy supply quality of the supply company is of great importance for both the supply company as the end users. This is because it is convenient for the supply company to bill all the electrical energy generated at that moment, and the end users can thus benefit from the supplied energy to perform the day-to-day chores. However, the electrical energy supply quality can be discontinued due to electrical faults caused by natural phenomena (thunderstorm, snow etc.) or by human operation errors. Therefore, due to the possible electrical fault occurrences in the different location of power system, transient stability analysis is required to determine the power system capacity to remain stable in presence of electrical disturbances.

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

Multi-Objective PSO for Control-Loop Tuning of DFIG Wind Turbines with Chopper Protection and Reactive-Current Injection

The control systems for the variable-speed wind turbine based on the Doubly Fed Induction Generator (DFIG) pose some tuning challenges. The performance and stability of DFIG wind turbines during faults in power grids are directly related to their controller settings. This work investigates how incorporating protection via a braking-Chopper controller connected to the DC link (DC Chopper) and a reactive-current injection during the PI-tuning process affects the performance of DFIG wind turbines during electrical faults. For the tuning process, the Multi-Objective-Particle-Swarm-Optimization (MOPSO) algorithm was used. Thus, two different approaches adopting this methodology were investigated, considering sequential and simultaneous tuning. The results showed that sequential tuning presented a better performance in relation to the reactive-current injection and lower amplitude deviations of the electrical quantities during and after the fault. On the other hand, simultaneous tuning reached damping of the mechanical oscillations faster and presented better performance of the protection system.

High Gain Zeta Converter with ANFIS Based MPPT for Standalone Application with Galvanic Isolation

The installation of Photovoltaic (PV) systems is steadily increasing globally, as they stand as the primary means of accessing the electrical grid. A considerable amount of effort is dedicated to ensuring the optimal functioning of these PV systems. As a result, the goal of this proposed work is to achieve stable DC-link voltage of the ZETA Converter using the ANFIS MPPT strategy. In this proposed approach, a ZETA converter is used, which results in improved voltage gain and reduced ripple content of the output voltage and current. An isolation transformer is integrated into the system to provide galvanic isolation. This transformer enhances safety by isolating the output from the high-frequency converter from the PV system and other components. Galvanic isolation prevents the transmission of electrical faults and protects connected loads. The output from the isolation transformer is supplied to a DC load through a rectifier. The diode bridge rectifier converts the alternating current (AC) output from the transformer into direct current (DC), making it suitable for powering DC loads. To stabilise the converter’s output voltage and mitigate the intermittent nature of PV, an intelligent MPPT is used, which also aids in maintaining DC-link voltage without distortions and ripples from the converter. The entire paper is validated through a MATLAB simulation 2021a.

A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus

The MVB is a kind of widely used vehicle-level bus, which is crucial for the normal operation of trains. However, the MVB bus contains device node terminals and the topology is complex, which makes fault location difficult. The voltage waveform of the MVB physical layer can reflect the electrical characteristics and fault characteristics of a network in real time and has the value of assisting the diagnosis of MVB network faults. Based on the characteristics of the MVB physical-layer voltage waveform, this paper studies the optimal sampling rate and feature-extraction algorithm of the MVB voltage waveform and the combination of the data of the data-link layer to assign accurate timestamps to waveform data, and finally presents a design for an MVB dual-mode data-acquisition platform with multiple sampling rates/test conditions. Experimental results show that the proposed algorithm can accurately extract waveform feature information under the optimal sampling rate of 62.5 MHz, which makes the collection of MVB voltage waveform data more reliable and practical.

An Interference Optimization – Induced Electrical Turbine Fault Prediction and Analysis Method

Predicting electrical turbine faults is decisive for consistent operation and power generation output. Based on the operative cycles of the electrical turbine, the faults are predicted to prevent power generation interruptions. This paper introduces an Interference Optimization-based Fault Prediction Method (IO-FPM) for serving smooth operation purposes. In this method, the inferred optimization using classifier tree learning is induced for segregating the operating cycles of the turbine. The maximum and minimum threshold conditions for turbine operation using resistance and magnitude of the blades are accounted for each operation cycle. The classifier performs segregation based on low and high thresholds for predicting failure cycles. Such cycles are altered using pre-maintenance intervals and mechanical fault diagnosis at an early stage. This prevents turbine failure regardless of external influencing factors.

FREEDOM: Validated Method for Rapid Assessment of Incipient Faults of Aerospace Systems

Model-based fault detection and isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in flight and evaluations of numerical models of the equipment. This results in an intensive computational procedure that can be addressed only grounding the aircraft. We introduce an original methodology to sensitively accelerate FDI by reducing the computational demand to identify the health status of the aircraft. Our scheme FREEDOM (Fast Reliability Estimate and Incipient Fault Detection of Multiphysics Aerospace Systems) proposes an original combination of a novel two-step compression strategy to compute offline a synthesized representation of the dynamical response of the system and uses an inverse Bayesian optimization approach to infer online the level of damage determined by multiple fault modes affecting the equipment. We demonstrate and validate FREEDOM against numerical and physical experiments for the case of an electromechanical actuator employed for secondary flight controls. Particular attention is dedicated to simultaneous incipient mechanical and electrical faults considering different experimental settings. The outcomes validate our FDI strategy, which permits to achieve the accurate identification of complex damages outperforming the computational time of state-of-the-art algorithms by two orders of magnitude.

A Chinese power text classification algorithm based on deep active learning

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep active learning (CCTP-DAL). Our core idea is to apply a hierarchical confidence strategy to a deep active learning model, to balance the trade-offs between the amount of training data and the accuracy of text classification. Our CCTP-DAL (1) trains the Bert model using a small amount of labeled data to calculate the confidence level of each short text, (2) selects high-confidence text data with optimal model generalization capability based on the hierarchical confidence level, and (3) fuses deep learning models and active learning strategies to ensure high text classification accuracy with less labeled training data. We benchmark our model on a real crawler data on the web with extensive experiments. The experimental results demonstrate that our proposed model can achieve higher text classification accuracy with less labeled training data compared with other deep learning models.

Real-Time Monitoring of Air Discharge in a Switchgear by an Intelligent NO2 Sensor Module.

An air-insulated power equipment adopts air as the insulating medium and is widely implemented in power systems. When discharge faults occur, the air produces decomposition products represented by NO2. The efficient NO2 sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO2 detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO2 detection, In2O3 sensors have exhibited tremendous advantages. In our work, four composites based on In2O3 are integrated into sensor arrays, which could detect 250 ppb of NO2 and exhibit excellent selectivity when simultaneously exposed to CO. To further reduce the impact of humidity on gas-sensing performance, a convolutional neural network and a long short-term memory model equipped with an attention mechanism are proposed to evaluate NO2 concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO2 concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO2 generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO2 estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.

Electromagnetic interference and the effect of low-voltage protective measures at electric vehicle charging stations

The nominal power of electric vehicle charging stations or charging parks is constantly increasing. Most of the users are ordinary persons and handle such equipment with a rated power of several 100 kW. Until now, equipment with such power ratings was only common in electrical operating facilities such as industrial plants where the users are at least instructed and protective measures are specified. If ordinary persons handle equipment with such power ratings in the field, the question arises as to whether the conventional safety goals are met in the event of an electrical fault. The consideration is: If the power increases so much, it can be assumed that the short-circuit power and thus the fault current increase and so does the risk of a dangerous electric shock. In this contribution, calculations of line-to-earth short-circuits on the low-voltage AC side of the three-phase system and their effects in typical configurations of charging stations are carried out. Considering the electromagnetic interference, the calculations provide the fault current and its distribution to determine the electrical potentials during the fault. From this, the (partial) fault voltages and active fault voltages are calculated. Based on the active fault voltage, the expected body impedance and consequently, the body current can be determined. With the body current and the break time of the protection device the risk of electric shock using international standards as guidelines is evaluated. As a result, recommendations for the planning, installation and safe operation of charging stations are given. It turns out that considering certain aspects like the conductor cross-sections or the electromagnetic interference, the risk of electric shock can be reduced to a conventional level. Periodic testing of the electrical system is necessary for safe and reliable operation. For example, follow-on faults due to unintended, improper use by ordinary persons can be prevented. Also, regular inspection of the electrical system is necessary for safe and reliable operation to prevent hazards due to aging or wear. However, it seems challenging to define an installation guideline that applies to all configurations as the boundary conditions vary depending on the type of system, installations in the area of interference and environmental influences.

Innovations in Building Diagnostics and Condition Monitoring: A Comprehensive Review of Infrared Thermography Applications

Infrared thermography is a non-destructive technique that uses infrared radiation to visualize surface temperature variations. It is a versatile tool that can be used to detect a variety of problems in buildings, including insulation deficiencies, moisture intrusion, structural compromise, and electrical faults. The review paper discusses the fundamental principles of infrared thermography, the different types of infrared approaches, and the condition monitoring of buildings using infrared imaging techniques. It also discusses research showing how infrared thermography has been applied to recognize and solve different building-related problems. The article highlights the potential for infrared thermography to advance while also acknowledging its current limits. Infrared thermography is predicted to become an even more effective technique for building diagnostics with the development of more sensitive cameras and the incorporation of artificial intelligence.

A critical review of PV systems’ faults with the relevant detection methods

PhotoVoltaic (PV) systems are often subjected to operational faults which negatively affect their performance. Corresponding to different types and natures, such faults prevent the PV systems from achieving their nominal power output and attaining the required level of energy production. Regarding the operational optimization of PV systems, this paper aims primarily at surveying and categorizing different types of PV faults, classified as electrical, internal, and external, where each is thoroughly investigated: internal faults occur at the PV cellular level, and can either be short circuit, open circuit, bridging, or bypass diode faults. External faults on the other side are mainly classified as temporary (i.e., clouds shading, snowstorms, etc.) or permanent (e.g., glass breakage, frame defects, etc.) mismatch faults. Lastly, electrical faults involve common circuitry problems, such as short circuits (e.g., line to ground, line to line, etc.), power processing units’ faults (e.g., inverter faults), and arc faults. As for the detection methods, six major fault detection methods are investigated for the AC side of the PV system with twenty-nine total AC based fault detection methods. On the other hand, eleven major fault detection methods are surveyed for the DC side of PV systems with seventy-three total DC based fault detection methods. The investigated methods are critically analyzed, and compared relevantly to each other, within the mutual sub-sets. The resulting tabulated comparative data assessments for PV faults (i.e., cause-effect relationships, impact on the PV system performance), as well as for faults detection methods (i.e., priority for application, etc.) compose a rich background for related PV systems’ performance security fields, where a nexus future work is also suggested.

Fault diagnosis of electrical equipment based on virtual simulation technology

Abstract In order to efficiently and accurately diagnose train electrical faults, we propose a fault diagnosis method for electrical equipment based on virtual simulation technology. First, Creo software was used to build a subway train model. Then, 3DMAX software was used to make animation and demonstrate the working principle and action process of the train electrical system. Finally, using Unity 3D software, a human–computer interaction mechanism was established, achieving presence and realism. This system realizes the functions of knowledge learning, student assessment, principal display, and troubleshooting of the electrical system of subway trains and is compared with the method of manual diagnosis. Experimental results show that in the designed fault diagnosis system, the detection time for various types of faults is shorter than 30 s, whereas the diagnosis time of the manual diagnosis method is 30–52 s. It shows that the electrical equipment fault diagnosis system based on virtual simulation has the advantages such as short fault diagnosis time and high efficiency. In addition, the highest diagnostic accuracy of the manual diagnosis method is 75.48%, which is far lower than the accuracy of the diagnostic system. Conclusion: It is proved that the designed fault diagnosis system has the advantages such as short detection time and high accuracy and can meet the safety requirements of industrial production.