Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
1,645
result(s) for
"Electric fault location."
Sort by:
Power Systems Modelling and Fault Analysis - Theory and Practice
This book provides a comprehensive practical treatment of the modelling of electrical power systems, and the theory and practice of fault analysis of power systems covering detailed and advanced theories as well as modern industry practices.
The continuity and quality of electricity delivered safely and economically by today’s and future’s electrical power networks are important for both developed and developing economies. The correct modelling of power system equipment and correct fault analysis of electrical networks are pre-requisite to ensuring safety and they play a critical role in the identification of economic network investments. Environmental and economic factors require engineers to maximise the use of existing assets which in turn require accurate modelling and analysis techniques. The technology described in this book will always be required for the safe and economic design and operation of electrical power systems.
eBook
A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors
This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
Journal Article
Early Detection of Faults in Induction Motors—A Review
There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.
Journal Article
Optical Detection and Cluster Analysis of Metal-Particle-Triggered Alternating Current Optical Partial Discharge in SFsub.6
Accurately detecting defect-induced photon emissions enables early defect detection and characterization. To address this, a defect evolution state recognition model based on phase-resolved photon counting and dimensionality reduction calculations is proposed under alternating current (AC) excitation. Initially, photon information from protruding metal defects simulated using needle–plane electrodes during partial discharge (PD) evolution is analyzed in SF[sub.6] . Subsequently, phase-resolved photon counting (PRPC) techniques and statistical analysis are employed to extract feature parameters for quantitative characterization of defect-induced photon responses. Finally, a t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction analysis is utilized to establish criteria for categorizing defect evolution states. The findings reveal that metal-particle-triggered optical PRPC maintains the obvious polarity effect, and the entire evolution of the discharge can be divided into three processes. These research findings are expected to advance the accurate assessment of operational risks in gas-insulated systems.
Journal Article
Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective
Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions.
Journal Article
Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system
The reliable operation of electrical power transmission systems is crucial for ensuring consumer’s stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method’s detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.
Journal Article
Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review
The most often used motor in commercial drives is the induction motor. While the induction motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses can result in defects. Therefore, many researchers who are involved in condition monitoring have been interested in the development of reliable and efficient fault diagnostic technologies. This paper’s goal is to provide an overview of available fault detection methods for the broken rotor bar problem, one of several defects associated to induction motors. Despite the fact that it is less common than bearing or insulator failure, this fault may cause electrical machines to fail catastrophically. It can be quite harmful, especially in large motors, and it can develop as a result of manufacturing faults, repeated starting of the machine, mechanical stress, and thermal stress. Hence, a review on rotor defect diagnosis was conducted. In order to confirm rotor bar fracture, this research provides probable defect signatures that can be extracted from the current signal. Each defect signature is reported according to (a) loading level, (b) the number of BRBs, (c) validation, and (d) methodologies.
Journal Article
Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things
Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.
Journal Article
High impedance faults detection in power distribution networks using rogowski coils, kalman filtering, least-squares and non-recursive DFT computation engines
This paper presents a novel computational engine based on non-recursive discrete Fourier transform (DFT) technology to detect high-resistance faults (HRFs) in power distribution networks. The non-recursive DFT approach utilizes the interconnection between a sliding window for current signals and a foundation function window during transient error. This non-recursive DFT technology is characterized by a fixed error in amplitude calculation and a rotated output angle. The proposed technique is compared against several established methods for high-resistance fault detection in distribution systems, including current reconstruction (CR) using Rogowski coils, Kalman filtering, and least-squares computational engines. The performance of each technique is evaluated by assessing the estimated percentage error in the calculation of fundamental and harmonic amplitudes. To study the proposed technique, the aforementioned methods were carefully modeled and simulated using MATLAB software for the IEEE 33-bus test feeder, simulated arcing faults, and Rogowski coils under various test conditions. The comparison is conducted under the influence of different arc models in the distribution system to assess the performance of the proposed technology. The comparative results demonstrate the effectiveness of the proposed non-recursive DFT-based technique in detecting high-resistance faults in power distribution networks, outperforming the other established methods considered in this study.
Journal Article